We evaluated the best generative AI chatbots on the market to see how they compare on cost, feature set, ease of use, quality of output, and support to help you determine the best bot for your business. Here are our picks for the top 21 generative AI chatbots for 2024.

Top Generative AI Chatbot Software

The following chart shows at a glance how the top generative AI chatbot software we evaluated compares on features, query limit, language model, and price—as well as whether the vendor provides a Chrome extension to improve ease of use—to help you determine the best option for your needs.

Freshchat

Best for Automating Self-Service

Overall Rating: 4.6

• Cost: 5
• Feature Set: 5
• Ease of Use: 5
• Quality of Output: 5
• Support: 2.5

Freshchat enables businesses to automate customer interactions through chatbots and also offers live chat capabilities for real-time customer support. It allows companies to manage and streamline customer conversations across various channels and an array of integrated apps.

Freshchat provides features like customizable chat widgets, agent collaboration, customer context, and analytics to track chat performance and customer satisfaction. What distinguishes Freshchat is that it enables sales and marketing—and even support teams—to not only reach customers but to scale those interactions so that the expertise of each live company staffer can be used to converse with many customers.

It does this using its unified agent workspace—which holds a full menu of past conversations—as well as responses from sales, marketing, and support, which an agent can quickly and easily share with an interested customer.

What I found most interesting was that the app has a “Freddy Insights” tool that provides key trends and insights that can be fed into a conversation at opportune moments to prompt a faster decision.

Pros and Cons

Pricing

• Free: Up to 10 agents
• Growth: $23 per agent, per month, or $228 per agent billed annually
• Pro: $59 per agent, per month, or $588 per agent billed annually
• Enterprise: $95 per agent, per month, or $948 billed annually

Features

• Chatbot analytics
• Canned responses
• Customer satisfaction (CSAT) survey and report
• Analytics and reporting
• Multi-channel communication

Crisp Chatbot

Best for Lead Nurturing

Overall Rating: 4.6

• Cost: 5
• Feature Set: 3.5
• Ease of Use: 5
• Quality of Output: 5
• Support: 5

Crisp Chatbot uses artificial intelligence to understand user queries and provide relevant responses. It can handle basic inquiries, provide product information, schedule appointments, and collect customer feedback.

In a growing trend across the AI chatbot sector, the Crisp Chatbot can be customized to match a business’s branding and tone. This is increasingly important in crowded markets where a number of companies are seeking to create a distinct brand to cut through the clutter.

In my conversations with Crispchat, I found the bot extremely helpful at answering my questions. When I asked whether it was good for small businesses, it answered in the affirmative and gave me two salient reasons why—24/7 support and ease-of-use—while also explaining the positive impact these would have on my company’s customer experience.

Pros and Cons

Pricing

• Free: 14-day trial
• Basic: Free forever for two seats
• Pro: $25 per month, per workspace for up to four seats
• Unlimited: $95 per month, per workspace for up to 20 seats
• Enterprise: Contact for custom quote

Features

• Integrations with social apps like Facebook Messenger, WhatsApp, Line, and Instagram
• Conversation routing capability
• Capable of sending multilingual messages
• MagicReply feature provides suggested AI-powered chatbot answers on multiple channels, in several languages

ChatGPT

Best Chatbot for Versatility and Advanced Generative AI Chat Features

Overall Rating: 4.5

• Cost: 4.2
• Feature Set: 4.4
• Ease of Use: 5
• Quality of Output: 3.8
• Support: 5

Developed by OpenAI as part of the GPT (generative pre-trained transformer) series of models, ChatGPT is more than just another natural language processing (NLP) tool designed to engage in human-quality conversations with users. The fact that it was developed by OpenAI means this generative AI app benefits from the pioneering work done by this leading AI company. ChatGPT was the first generative AI app to come to market, launching in November of 2022.

The OpenAI platform can perform NLP tasks such as answering questions, providing recommendations, summarizing text, and translating languages. Aside from content generation, developers can also use ChatGPT to assist with coding tasks, including code generation, debugging help, and programming-related question responses.

I have used ChatGPT for various tasks, from summarizing long articles for research purposes to brainstorming business plans and customer pain points. The output is almost always satisfactory, in-depth, and surprisingly nuanced.

OpenAI has received significant funding from Microsoft and will likely be a leader in the years ahead, both in terms of advanced functionality (depth and versatility of toolset) and its ability to offer technology that’s ahead of the curve.

Pros and Cons

Pricing

• Free: Access to limited features
• ChatGPT Plus: $20 per month
• Enterprise: Contact OpenAI for a custom quote

Features

• Analytics dashboard for enterprise users
• Can understand and generate text in multiple languages
• Offers contextual understanding of extended conversation

Kommunicate

Best for e-Commerce Businesses

Overall Rating: 4.5

• Cost: 3.3
• Feature Set: 5
• Ease of Use: 5
• Quality of Output: 5
• Support: 4

Kommunicate is a generative AI-powered chatbot designed to help businesses optimize customer support and improve the customer experience. One of its chief goals is assisting and completing sales for e-commerce vendors, though it also handles support and the full range of customer queries.

The app provides automated conversational capabilities through chatbots, live chat, and omnichannel customer support. Kommunicate can be integrated into websites, mobile apps, and social media platforms, allowing businesses to engage with customers in real time and provide instant assistance regarding any issue that involves a sale or service.

To assist with this, it offers a FAQ bot to lessen the load of simple, repetitive customer queries. The app’s feature set is far more robust due to a long list of integrations, including OpenAI, IBM Watson, Zapier, and Shopify. It enables easy, seamless hand-off from chatbot to a human operator for those interactions that call for it.

Pros and Cons

Pricing

• Free: 30-day trial
• Lite: $100 month-to-month, or $1,000 billed annually, for up to two teammates; $20 per month for each additional teammate
• Advanced: $200 month-to-month, or $2,000 billed annually, for up to five teammates; $30 per month for each additional teammate
• Enterprise: Custom pricing

Features

• Omnichannel—web, mobile, and social media
• CSAT rating
• Integrations with third-party services
• Can manage customer conversations through bots, live chat, Facebook, WhatsApp, and Line

Claude

Best Chatbot for Long Conversational Memory

Overall Rating: 4.4

• Cost: 5
• Feature Set: 3.5
• Ease of Use: 5
• Quality of Output: 5
• Support: 4

Claude is Anthropic’s free AI chatbot. It runs Claude 3, a powerful LLM known for its large context window of 200,000 tokens per prompt, or around 150,000 words. This gives it one of the best conversational memories around.

To put that in perspective, you can be at the tail end of a conversation the size of Jane Austen’s Pride and Prejudice and Claude will still remember everything that was said, taking your previous questions, file uploads, and responses into account when it responds to your newest prompts.

Its ability to analyze and summarize long documents is excellent, and its answers tend to be more straightforward than those of Chat-GPT. When trying Claude, I was constantly surprised at how concise its answers were to my questions. It felt like I was messaging a true human expert rather than someone going out and finding the answers and regurgitating them back to me. All of this makes Claude a valuable research assistant and creative collaborator

Pros and Cons

Pricing

• Free: Unlimited access to Claude.ai chat
• Claude Pro: $20 per month

Features

• 200,000 token context window (around 500 pages)
• Document summarization and analysis
• Straightforward, clear responses
• Integrates with 600 apps through Zapier

ChatSpot

Best for HubSpot Customers

Overall Rating: 4.4

• Cost: 5
• Feature Set:5
• Ease of Use: 5
• Quality of Output: 5
• Support: 1

ChatSpot combines the capabilities of ChatGPT and HubSpot CRM into one solution. With this tool, you can draft blog posts and tweets and also create AI-generated images, or you can feed it a prompt to enable you to get specific data from your HubSpot CRM.

ChatSpot allows you to perform many functions, including adding contacts and creating tasks and notes. You can also ask it to summarize your CRM data or generate a bar chart of results to understand your company’s performance.

If you’re a HubSpot customer, this chatbot app can be a useful choice, given that Hubspot offers so many ways to connect with third party tools—literally hundreds of business apps. And HubSpot, as its users are well aware, is a platform that offers great functionality for sales reps. This chatbot will likely remain a top candidate for sales and marketing professionals who need improved functionality for customer sales and service.

Pros and Cons

Pricing

• ChatSpot is free

Features

• Uses OpenAI’s GPT-3 and GPT-4
• Integration with ChatGPT, HubSpot, DALL-E, and Google Docs
• Prompt templates and library

Intercom

Best for Handling Support Queries

Overall Rating: 4.3

• Cost: 2.3
• Feature Set: 5
• Ease of Use: 4.5
• Quality of Output: 5
• Support: 5

Intercom AI’s chatbot, Fin, powered by large language models from OpenAI, aims to improve customer experience, automate support processes, and enhance user engagement. The fact that OpenAI (with all of its deep funding and vast expertise) provides Intercom’s underlying engine is clearly a plus.

Intercom can engage in realistic conversations with customers, helping to resolve common issues, answer questions, and initiate actions. It’s an app aimed clearly at the lucrative call center sector. In trying Intercom while acting as a customer seeking assistance, I found that its answers to my questions were helpful and quick. It also felt like there was a human on the other end of the chatbox.

So, what distinguishes it? It has gained wide adoption in the industry and is used by companies ranging from Amazon to Microsoft to Meta. It also offers a “plug and play” chatbot architecture to make setup relatively easy. And Intercom’s “Composer AI” feature enables a call center rep to rephrase a message with one click, turning a single phrase into longer, more detailed response. It can easily summarize entire conversations with one click.

Pros and Cons

Pricing

• Free: 14-day trial
• Fin AI Chatbot: Usage measured in resolutions; $0.99 per resolution.
• Essential: ​​$39 per seat, per month
• Advanced: $99 per seat, per month
• Expert: $139 per seat, per month

Features

• Supports up to 43 languages
• Integrates with Facebook and Instagram
• Answers customer questions based on support content
• Makes article suggestions

Google Gemini

Best Chatbot for Brainstorming Ideas

Overall Rating: 4.3

• Cost: 5
• Feature Set: 5
• Ease of Use: 4.5
• Quality of Output: 5
• Support: 1

Formerly known as Bard, Google Gemini is an AI-powered LLM chatbot built on the PaLM2 (Pathways Language Model, version 2) AI model. You can export your Google Gemini conversation to Google Docs or Draft in Gmail, and the platform allows you to create a shareable public link you can send to a third party, making it useful in collaborative workflows for professional work environments.

It’s a major plus for this app that it’s developed and supported by Google. Admittedly, this app had some difficulties when it was first rolled out. Apparently scrambling to keep up with the phenomenal success of OpenAI’s ChatGPT, Google didn’t iron out all the bugs first. However, Gemini is being actively developed and will benefit greatly from Google’s deep resources and legions of top AI developers.

An important benefit of using Google Gemini is that its supporting knowledge base is as large as any chatbot’s—it’s created and updated by Google. So if your team is looking to brainstorm ideas or check an existing plan against a huge database, the Gemini app can be very useful due to its deep and constantly updated reservoir of data.

Pros and Cons

Pricing

• Free to use

Features

• Can export conversations
• “Google it” function directs you to search engine to provide more information
• Can generate code in 20 programming languages

For an in-depth comparison of two leading chatbots, read our guide: ChatGPT vs. Google Bard: Generative AI Comparison

Jasper

Best Chatbot for Marketing and Sales Teams

Overall Rating: 4.2

• Cost: 2.7
• Feature Set: 5
• Ease of Use: 5
• Quality of Output: 5
• Support: 3

The Jasper generative AI chatbot can be trained on your brand voice to interact with your customers in a personalized manner. Jasper partners with OpenAI and uses GPT-3.5 and GPT-4 language models and their proprietary AI engine. The company also sources from other models such as Neo X, T5, and Bloom.

Jasper’s strongest upside is its brand voice functionality, which allows teams and organizations to create highly specific, on-brand content. This capability is invaluable for marketing and sales teams that need to ensure that all chatbot communications are created with an accurate brand identity.

Out of the box, Jasper offers more than 50 templates—you won’t need to create a chatbot persona from scratch. The wide array of models that Jasper accesses and its focus on customizing for brand identity means this is a choice that marketing teams should at least audition before they make any final selections for an AI chatbot.

Pros and Cons

Pricing

• Creator: $49 per month billed monthly, or $468 billed annually
• Pro: $69 per month billed monthly, or $708 billed annually
• Business: Custom quotes

Features

• Browser extension available
• Supports up to 30 languages.
• Integrates with Zapier, Webflow, Make, Google Sheets, and other tools
• Collaboration capability

For a full portrait of today’s generative AI leaders, see our guide: Generative AI Companies: Top 12 Leaders

Tidio

Best for Small and Medium-Sized Businesses

Overall Rating: 4.2

• Cost: 4.1
• Feature Set: 3.5
• Ease of Use: 5
• Quality of Output: 5
• Support: 4

SMBs looking for an easy-to-use AI chatbot to scale their support capacity may find Tidio to be a suitable solution. Tidio Lyro lets businesses automate customer support processes, reduce response times, and handle tasks such as answering frequently asked questions. You can also use Tidio Lyro to answer customer inquiries, provide automated responses, and assist with basic analytics, allowing you to manage customer support efficiently.

Tidio fits the SMB market because it offers solid functionality at a reasonable price. SMBs are under pressure to offer basic customer service at a low cost; to address this, Tidio allows the creation of a wide array of prewritten responses for simple questions that customers ask again and again. Tidio also offers add-ons at no extra cost, including sales templates to save time with setup.

Additionally, the quality of Tidio’s output was ranked highly in our research, so even as the AI chatbot focuses on affordability, it offers a quality toolset.

Pros and Cons

Pricing

• Free: Up to 50 live chat conversations
• Lyro AI: Starts at $39 per month for up to 200 conversations
• Tidio+: Starts at $499 per month

Features

• Live chat capability
• Integration with Messenger, Instagram, WhatsApp, and email services
• Multi-language support
• Canned responses

Perplexity AI

Best Chatbot for Finding Information on the Internet

Overall Rating: 4.2

• Cost: 4.2
• Feature Set: 4.4
• Ease of Use: 5
• Quality of Output: 5
• Support: 2

Perplexity AI is a generative AI chatbot, search, and answer engine that allows users to express queries in natural language​​ and provides answers based on information gathered from various sources on the web. When you ask a question of Perplexity AI, it does more than provide the answer to your query—it also suggests related follow-up questions. In response, you can either select from the suggested related questions or type your own in the text field.

Perplexity AI’s Copilot feature can guide users through the search process with interactive multiple searches and summarized results. This capability is helpful when exploring complex topics. However, it’s limited to five searches every four hours for free plan users and up to 300 searches for paid users.

Perplexity AI’s strength in searching the internet means this tool is ideal for advanced researchers, from academic to small business to large enterprise, including companies that want to explore how they’re viewed on the web. Because AI enables it to understand your search query at a multi-dimensional level, the app can guide you in directions you might not have thought of, making it “serious researcher’s best friend.”

Pros and Cons

Pricing

• Free: Limited features
• Pro: $20 per month, or $200 per year

Features

• Supports Claude 3, Llama 3, and GPT-4
• Supports 30 languages, including English, Bengali, and Danish
• Text and PDF file upload capability

LivePerson

Best for Conversation Analytics

Overall Rating: 4.1

• Cost: 0.6
• Feature Set: 5
• Ease of Use: 5
• Quality of Output: 5
• Support: 5

The LivePerson AI chatbot can simulate human conversation and interact with users in a natural, conversational manner. Its goal is to discover customer intent—the core of most successful sales interactions—using analytics. To this end, LivePerson offers what it calls a “meaningful automated conversation score,” a metric that attempts to quantify whether a given bot-human interaction was successful in terms of company branding and service.

Additionally, the platform enables you to convert webpages, PDFs, and FAQs into interactive AI chatbot experiences that use natural human language to showcase your brand’s expertise. The bot’s entire strategy is based on making as much content as possible available in a conversational format.

LivePerson can be deployed on various digital channels, such as websites and messaging apps, to automate customer interactions, provide instant responses to inquiries, assist with transactions, and offer personalized recommendations. Significantly, LivePerson is also geared to be embedded in social media platforms, so it certainly aims to reach a large consumer base.

Pros and Cons

Pricing

• Conversational Cloud: Pay-as–you-go, per resolution
• Generative AI: Pay-as-you-go, per resolution

Features

• Conversation builder and orchestrator
• Administrative function includes the ability to create and manage users and skills
• Supports 19 messaging channels, including Facebook, Instagram, X  and WhatsApp.
• Reporting and analytics

Chatsonic

Best Chatbot for Individuals in the Creative Industries

Overall Rating: 4

• Cost: 4.6
• Feature Set: 4.4
• Ease of Use: 5
• Quality of Output: 2.5
• Support: 2

Trained and powered by Google Search to converse with users based on current events, Chatsonic positions itself as a ChatGPT alternative. The AI chatbot is a product of Writesonic, an AI platform geared for content creation. Chatsonic lets you toggle on the “Include latest Google data” button while using the chatbot to add real-time trending information.

The benefit of this “latest data” approach is that it helps individuals in creative fields like advertising and marketing stay up to date on current trends. In contrast, some of the more advanced chatbots use large language models that are updated infrequently, so those looking for this week’s information won’t find what they need.

This current events approach makes the Chatsonic app very useful for a company that wants to consistently monitor any comments or concerns about its products based on current news coverage. Some companies will use this app in combination with other AI chatbot apps with the Chatsonic chatbot reserved specifically to perform a broad and deep brand response monitoring function.

Pros and Cons

Pricing

• Free: Up to 10,000 words per month
• Unlimited: $20 per month billed monthly, or $192 billed annually for one user
• Business: $19 per month billed monthly, or $152 billed annually for one user; up to 200,000 words for GPT 3.5 or 33,333 words for GPT 4.0; add words and users for additional cost
• Enterprise: Custom quote

Features

• Google Docs-like Sonic Editor
• Integration with third-party apps
• More than 100 AI templates

Poe

Best Chatbot for Testing Multiple AI Chatbots

Overall Rating: 3.9

• Cost: 5
• Feature Set: 2.8
• Ease of Use: 4.5
• Quality of Output: 5
• Support: 3.9

Poe is a chatbot tool that allows you to try out different AI models—including GPT-4, Gemini, Playground, and others listed in this article—in a single interface. This is helpful for people who want to pit them against each other to decide which tool to purchase. It’s also great for those who plan to use multiple LLM models and unlock their various strengths for a low price of $16.67 per month when paid annually.

For example, if you plan to use Claude 3 for conversational chat and GPT 4 for content generation—their respective specialties—you can get both by subscribing to Poe rather than paying for each separately, which would cost $40 per month. Developers can also use Poe to build their own chatbots using one of the popular models as the foundation, streamlining the process.

Pros and Cons

Pricing

• Free: Limited access
• Poe Subscription: Starts at $16.67 per month; includes unlimited daily messages and access to exclusive bots like GPT 4 and Claude-3-Opus

Features

• Test and compare multiple popular AI chatbots
• Access multiple AI chatbots like GPT-4 and Claude-3-Opus for low price
• Free version available
• Streamlines building your own AI chatbot using a top LLM as your model

Drift

Best for Businesses that Rely on B2B Sales and Marketing

Overall Rating: 3.8

• Cost: 1.8
• Feature Set: 5
• Ease of Use: 4.5
• Quality of Output: 5
• Support: 2

Drift’s AI is trained on more than 100 million B2B sales and marketing conversations, enabling it to understand and respond to B2B customer inquiries in the conversational manner that’s expected in this market sector—including multi-language support.

The Drift AI chatbot is designed to handle different types of conversations, including lead nurturing, customer support, and sales assistance. It can engage with website visitors and provide relevant information or route inquiries to the appropriate human representative.

Drift can be custom-trained for your B2B business in fine detail, allowing it to learn your brand’s voice and respond in a manner similar to your in-house reps. The B2B market is a specific use case for AI chatbots, and Drift’s focus on this market means that a B2B company can set up a highly functional chatbot that will evolve with the B2B market over time with less work.

Pros and Cons

Pricing

• Premium: $30,000 billed annually
• Advanced: Available upon request
• Enterprise: Custom quote

Features

• User administration with role-based access control
• Live chat, custom chatbots, and AI-powered chatbots
• Meeting scheduler with Google, Office 365, and Teams integration
• Conversation and conversion reporting capability

Ada

Best for Customer Service Automation

Overall Rating: 3.8

• Cost: 0.6
• Feature Set: 5
• Ease of Use: 5
• Quality of Output: 5
• Support: 2.5

Ada is an AI-powered customer service automation platform that uses natural language processing and machine learning algorithms to automate customer service tasks. It is designed to help resolve customer issues, allowing businesses to streamline customer service operations and enhance the customer experience.

There are two ways to use Ada. You can either connect it to your knowledgebase and use generative AI to answer questions grounded in your existing content, or build a hard-coded chatbot using Ada’s natural language understanding and its drag-and-drop platform for a pre-scripted easy and fast setup.

In either case, Ada enables you to monitor and measure your bot KPI metrics across digital and voice channels—for example, automated resolution rate, average handle time, containment rate, CSAT, and handoff rate. It also offers predictive suggestions for answers, allowing the app to stay ahead of customer interactions. Ada’s user interface is intuitive and easy to use, which creates a faster onboarding process for customer service reps.

Pros and Cons

Pricing

• Generative: Contact Ada’s sales team for quote
• Scripted: Contact Ada’s sales team for quote

Features

• Integrations with third-party apps like Shopify, Marketo, and Clearbit
• Automated resolution measurement
• Advanced branding including additional customizations

YouChat

Best Chatbot for Students

Overall Rating: 3.6

• Cost: 5
• Feature Set: 2.9
• Ease of Use: 5
• Quality of Output: 3.8
• Support: 1

Designed by You.com, YouChat is an AI-powered generative chatbot that can summarize text, write code, suggest ideas, compose emails, and answer general questions based on information available on the web.

It also cites its information source, making it easy to fact-check the chatbot’s answers to your queries. YouChat combines various elements in search results, including images, videos, news, maps, social, code, and search engine results on the subject.

In essence, YouChat is a lighter weight tool with an affordable price plan that performs a wide array of tasks—particularly those needed by students. YouChat offers an easy user interface that will appeal to a busy user base that wants to jump right in without undergoing a lot of technical training.

The upside of this kind of easy-to-use app is that, as generative AI advances, today’s fairly lightweight tools will likely offer an enormous level of functionality. So any student or SMB user who starts with it now will probably reap greater benefits in the months and years ahead.

Pros and Cons

Pricing

• Free: Up to 10 AI writing generations
• YouPro: $14.99 per month
• YouPro for Education: $6.99 per month

Features

• Code generator
• Multimodal search capability
• Support for social media profiles

HuggingChat

Best Chatbot for Developers

Overall Rating: 3.3

• Cost: 5
• Feature Set: 2.9
• Ease of Use: 4.8
• Quality of Output: 1.3
• Support: 1

Developed by Hugging Face, HuggingChat is a chatbot based on the Open Assistant Conversational AI Model. It uses NLP and ML algorithms to interact with users and can generate answers to questions, write essays, write code, translate text, and construct emails. The platform has been trained on a large dataset of diverse conversations and can learn from new interactions.

Hugging Face has a large and enthusiastic following among developers—it’s something of a favorite in the development community. Its platform is set up as an ideal environment to mix and match chatbot elements, including datasets ranging from Berkeley’s Nectar to Wikipedia/Wikimedia, and the AI models available range from Anthropic to Playground AI.

Many of these resources may not mean much to the SMB owner or enterprise manager, but they mean a great deal to developers with the expertise to use a deep resource base to customize an AI chatbot. Given that HuggingChat offers such a rich developer-centric platform, users can expect it to grow rapidly as AI chatbots are still gaining more adoption.

Pros and Cons

Pricing

• Free: Limited access
• Pro: $9 per month; gives early access to new features
• Enterprise Hub: $20 per user, per month

Features

• Web search functionality complements response with web content
• Multilingual support
• Can debug and write code and create Excel formulas

Replika

Best for Personal Use

Overall Rating: 3.2

• Cost: 5
• Feature Set: 0.6
• Ease of Use: 5
• Quality of Output: 5
• Support: 1.5

Replika is an artificial intelligence chatbot designed to have meaningful and empathetic-seeming conversations with users. It’s focused more on entertaining and engaging personal interaction rather than straightforward business purposes.

To support its goal, Replika uses natural language processing and machine learning algorithms to understand and respond to text-based conversations. Replika aims to be a virtual friend or companion that learns from and adapts to your personality and preferences.

To better engage, the platform learns your texting style and mimics it. Of course, this means that the longer you interface with the app, the more accurately Replika can mimic your style.

Its motto is “My AI Friend,” and the vendor claims that it can offer dialogue geared for emotional support. To that end, it can engage in a wide variety of topics or even help you learn new things.

Pros and Cons

Pricing

• Free: Limited access
• Month-to-Month: $19.99
• Annual: $69.96.
• Lifetime: $299.99

Features

• Role-playing activities
• Communication through text, pictures, voice calls, and video calls
• Can engage in conversations about a wide range of topics, from personal interests to general knowledge
• Guides users through specific conversation topics such as mindfulness, stress management, or self-care

Bing Chat Enterprise

Best Chatbot for Organizations in the Microsoft Ecosystem

Overall Rating: 3.1

• Cost: 4
• Feature Set: 3.5
• Ease of Use: 1.8
• Quality of Output: 2.5
• Support: 4

Microsoft generative AI tool Bing Chat Enterprise uses GPT-4, which includes a top large language model, to generate natural language responses to user queries. Bing Chat Enterprise has three conversation styles: creative, balanced, and precise. These styles help you set the tone for the expected response to your query.

Bing Chat Enterprise is available in 160 regions. Users can also access it via the Windows Copilot Sidebar, making this app easily accessible. Microsoft is incorporating AI across its product portfolio, so this chat app will likely show up in a number of applications. If your company uses Microsoft, this chat app is a good choice.

The greatest strong point for the Bing Chat tool is that it’s produced by Microsoft, arguably the leader in AI today. The company’s deep resources and dominant technical expertise in AI software should support this chat app very well in the years ahead.

Microsoft is also skilled at serving both the consumer and the business market, so this chat app can be configured for a variety of levels of performance. It has the depth of features needed to serve the SMB market and large enterprise.

Pros and Cons

Pricing

• Free: Available to customers licensed for Microsoft 365 E3, E5, Business Standard, Business Premium, or A3 or A5 for faculty
• Standalone: $5 per user, per month

Features

• Versatile mix of conversation styles for different enterprise AI use cases
• Citation capability lets you fact-check output
• Lets users upload images or can generate images

OpenAI Playground

Best Chatbot for Customizability

Overall rating: 3.0

• Cost: 4.15
• Feature Set: 5
• Ease of Use: 0.55
• Quality of Output: 2.5
• Support: 2

OpenAI Playground was designed by the same generative AI company that created ChatGPT (see above). As such, it is well funded and is continuously improved by some of the best developers in the AI industry. Expect it to stay ahead of the curve in terms of feature set.

The platform is a web-based environment allowing users to experiment with different OpenAI models, including GPT-4, GPT-3.5 Turbo, and others. OpenAI Playground is suitable for advanced users looking for a customizable generative AI chatbot model that they can fine-tune to suit their business needs. This advanced platform enables a vast level of choices and approaches in an AI chatbot.

OpenAI Playground’s focus on customizability means that it is ideal for companies that need a very specific focus to their chatbot. For instance, a sophisticated branding effort or an approach that requires a very proprietary large language model, like finance or healthcare. Given that this app needs true developer expertise to be fully customizable, it is not the best choice for small businesses or companies on a tight budget.

Pros and Cons

Pricing

Priced per 1,000 tokens, about 750 words; varies by model choice. New users get $5 in free credit to use for their first three months.

• GPT-4 Models: Input costs $0.03, output costs $0.06 for 8,000 context; input costs $0.06, output costs $0.12 for 32,000 context
• GPT-3.5 Turbo: Input costs $0.0015, output costs $0.002 for 4,000 context; input costs $0.003, output costs $0.004 for 16,000 context

Features

• Text and completion summarization
• Multiple options for model selection
• Language translation
• Sentiment analysis

Key Features of Generative AI Chatbots

Generative AI chatbots require a number of advanced features to accomplish their many tasks, ranging from context understanding to personalization.

Natural Language Processing

Natural language processing is a critical feature of a generative AI chatbot. NLP enables the AI chatbot to understand and interpret casual conversational input from users, allowing you to have more human-like conversations. With NLP capabilities, generative AI chatbots can recognize context, intent, and entities within the conversation.

Context Understanding

Context understanding is a chatbot’s ability to comprehend and retain context during conversations—this enables a more seamless and human-like conversation flow. A high-quality artificial intelligence chatbot can maintain context and remember previous interactions, providing more personalized and relevant responses based on the conversation history. This enables chatbots to provide more coherent and relevant replies.

Personalization

When shopping for generative AI chatbot software, customization and personalization capabilities are important factors to consider as they enable the tool to tailor responses based on user preferences and history. ChatGPT, for instance, allows businesses to train and fine-tune chatbots to align with their brand, industry-specific terminology, and user preferences.

Multilingual Support

An AI chatbot’s ability to communicate in multiple languages makes it appealing to global audiences. This functionality also allows the chatbot to translate text from one language to another.

How to Choose the Best Generative AI Chatbot For Your Business

The best generative AI chatbot for your company serves your business’s needs and balances quality service with moderately expensive or lower cost pricing based on what works with your budget. Additionally, you’ll need to ensure it has all the necessary AI features you need for your operations, and that these features will be supported going forward.

Organizations in the Microsoft ecosystem may find Bing Chat Enterprise beneficial, as it works better on the Edge browser. ChatGPT does not cite its data sources, but it is one of the most versatile and creative AI chatbots. Google Bard cites data sources and provides up-to-date information, but its response time is sometimes slow. Chatsonic can generate AI images as part of the answer to your query.

What appear to be positives to you may be negatives to another user, and vice versa. The best tool for your business is unique to you—conduct your own research to fully understand the chatbot market, identify your overall AI goals, and shop for a chatbot tool that offers features and capabilities that meet your requirements.

How We Evaluated the Best Generative AI Chatbots

We evaluated today’s leading AI chatbots with a rubric that balanced factors like cost, feature set, quality of output, and support.

Feature Set | 30 percent

Features carry the most weight in our evaluation process. We evaluated various capabilities offered by each generative AI software, including multi-language support, the ability to accept spoken word input, the programmability of the solution, the kind of users it is built for, and customization options.

Ease of Use | 25 percent

We assessed each generative AI software’s user interface and overall user experience. This included evaluating the ease of installation, setup process, and navigation within the platform. A well-designed and intuitive interface with clear documentation, support materials, and the AI chatbot response time contributed to a higher score in this category.

Cost | 20 percent

We reviewed each AI chatbot pricing model and available plans, plus the availability of a free trial to test out the platform. Our research found that some platforms are completely free, while some offer both free and paid plans—a tool like Google Bard gives you access to all its features for free, ChatGPT has a free plan with access to GPT 3.5 capabilities, while GPT 4 requires a monthly subscription. On the other hand, Jasper is a paid chatbot offering a seven-day free trial.

Support | 15 percent

Our analysis also considered the level of support provided by the AI software provider. We assessed the availability and responsiveness of customer support, including customer service hours, email support, live chat support and knowledge base.

Quality of Output | 10 percent

To determine the output quality generated by the AI chatbot software, we analyzed the accuracy of responses, coherence in conversation flow, and ability to understand and respond appropriately to user inputs. We selected our top solutions based on their ability to produce high-quality and contextually relevant responses consistently.

AI Chatbots: Frequently Asked Questions (FAQs)

Below, we provide answers to the most commonly asked questions about AI chatbots.

What Features Should Businesses Look for in AI Chatbots?

Key features to look for in AI chatbots include NLP capabilities, contextual understanding, multi-language support, pre-trained knowledge and conversation flow management. It is also important to look for a tool with a high accuracy rating, even if the questions asked are complex or open-ended.

Why are AI Chatbots Important for Businesses?

AI chatbots are important to businesses because they enhance customer experience and provide various operational benefits, such as improved customer experience, personalized experiences, cost reduction, and increased productivity. Most important: they provide customer service at a far lower cost.

How Can AI Chatbots Enhance Customer Support and Engagement?

AI chatbots can boost customer support by providing 24/7 support, answering common questions, and personalizing interaction based on customer preferences. (For instance, multilingual AI chatbots can communicate in multiple languages, enabling businesses to assist customers from different regions).

What are Traditional Chatbot Builders?

Traditional chatbot builders are tools that help you build conventional rule-based chatbots that stick to a prewritten script. Compared to AI chatbots, they lack natural language processing abilities and feel far less human-like.

Bottom Line: Today’s Top AI Chatbots Take Highly Varied Approaches

Determining the “best” generative AI chatbot software can be subjective, as it largely depends on a business’s specific needs and objectives. Chatbot software is enormously varied and continuously evolving,  and new chatbot entrants may offer innovative features and improvements over existing solutions. The best chatbot for your business will vary based on factors such as industry, use case, budget, desired features, and your own experience with AI. There is no “one size fits all” chatbot solution.

For a full portrait of today’s top AI companies, read our guide: 150+ Top AI Companies

The post 21 Best Generative AI Chatbots in 2024 appeared first on eWEEK.

We tested the top AI writing software to see how it compares on features, pricing, and relative strengths and weaknesses, and to see how well it meets a variety of common use cases. Here are our picks for the best AI writing tools in 2024:

• Copy.ai: Best for Beating Writer’s Block
• Rytr: Best for Copywriters
• Quillbot: Best for Paraphrasing
• Frase.io: Best for SEO Teams and Content Managers
• Anyword: Best for Copywriting Performance Analysis
• Grammarly: Best for Grammatical and Punctuation Error Detection
• Hemingway Editor: Best for Content Readability Measurement
• Writesonic: Best for Blog Content Writing
• AI Writer: Best for High-Output Bloggers
• ContentScale.ai: Best for Creating Long-Form Articles

Comparing the Top AI Writing Tools

The following chart shows at-a-glance how each of the AI writing tools compare on key features, pricing, and availability of a free version.

Copy.ai

Best for Beating Writer’s Block

Copy.ai is an artificial intelligence writing tool designed to help marketers, business owners, and copywriters create various forms of content, including website copy, sales landing pages, email, and social media and blog posts. A boon to content marketers, Copy.ai can automatically conduct SEO research and produce content briefs for writers, streamlining the production process and giving writers guidance.

Another distinctive feature is the thought leadership tool, which automatically turns raw transcripts from interviews with experts into a variety of content assets, including blog posts, social media posts, and newsletters. This dramatically reduces the time it takes to manage content repurposing. The AI writing tool also lets you easily generate copy that aligns with your organization’s persona.

Copy.ai’s in-platform AI chatbot acts as a writing assistant to help beat writer’s block by aiding with brainstorming. For example, I asked it to give me 10 Instagram post ideas for fashion week—it delivered 10 useable ideas I could use as the basis for my social posts. These features enable your team to create and distribute more high-quality content at a lower cost than before.

Pros and Cons

Pricing

• Free: Up to 2,000 words per month
• Starter: $49 per month or $36 per month billed annually for one seat, unlimited words
• Advanced: $249 per month or $186 per month billed annually for up to five seats
• Enterprise: Call for quote

Key Features

• More than 90 copywriting tools included
• Allows you to save and reuse key information via its Infobase feature
• Streamlines SEO content research and brief creation
• Automates content repurposing
• Chat feature enables sales and marketing teams to interact more naturally with AI
• Easy content brainstorming
• Automated sales email writing
• Supports 25 languages

For an in-depth guide to the best AI detection tools, read our guide: AI Detector Tools

Rytr

Best for Copywriters

Rytr is an AI-powered writing tool capable of producing copywriting content on various topics. It’s one of the best AI writing tools for commercial copywriting jobs, where copywriters can use it to automate the creation of post and caption ideas, paragraph content, SEO meta titles, emails, call to actions, replies, and other less complex copywriting assets.

The platform also supports more than 40 other use cases, including generating blog ideas and creating job descriptions. In addition, paid users can create their own use cases by training Rytr for their specific needs.

I tested Rytr out for copywriting and asked it to write me a call-to-action for “A knee pad that protects middle-aged gardeners from hurting themselves.” In response to my prompt, it provided the following two variants:

• “Safeguard your knees, garden with ease.”
• “Safeguard your knees, garden in comfort.”

When I changed the tone from “convincing” to “humorous,” it generated two more options that were not useable out-of-the-box but were still useful for ideation:

• “Kneel with confidence, gardenlords!”
• “Unleash your inner knee-jerk green thumb!”

Overall, Rytr its a useful AI writing tool for copywriters who want to streamline their writing process and come up with more ideas for a variety of content types.

Pros and Cons

Pricing

• Free: Up to 10,000 characters per month
• Saver: $9 per month, or $90 per year; up to 100,000 characters per month
• Unlimited: $29 per month, or $290 per year

Key Features

• Content outline and brief generator
• Chrome extension for improving your writing
• Business idea generator
• Supports copywriting frameworks like AIDA (Attention–Interest–Desire–Action) and PAS (Problem–Agitation–Solution)
• Serves as keyword extractor and generator
• Creates SEO meta descriptions
• Writes calls-to-action

To see top AI software in several categories, see our guide: Best Artificial Intelligence Software

QuillBot

Best for Paraphrasing

QuillBot is an AI-powered writing assistant that, unlike most AI writing tools, focuses on helping you paraphrase and summarize texts. This makes it great for content marketers who often have to write repetitive copy with slight variations across their different content assets or even within the same blog post. For example, instead of writing “automate your administrative accounting tasks” five times, they could use QuillBot to spin that into five different variations.

It also functions as a citation generation tool, making it somewhat useful for academics—but it may not be the best tool for writing essays and research papers, as its output doesn’t consistently pass AI detection tools.

One of QuillBot’s standout features is the ability to choose from nine modes of paraphrased output. You can select from natural, academic, simple, creative, shortened, expanded, and more. As an example, when I prompted it with “The shift to agriculture took thousands of years” in academic mode, the output read, “The transition to an agrarian society spanned several millennia.”

Overall, I found the tool helpful for the outwardly simple but cognitively-taxing task of coming up with new ways to express your ideas in writing.

Pros and Cons

Pricing

• Free: Limited capability
• Premium: $19.95 per month, $13.33 per month billed semi-annually, and $8.33 per month billed annually
• Team: Varies depending on the number of users

Key Features

• Offers Chrome and Word extensions for grammar checking
• Can create source citations in various styles, including APA, MLA, and Chicago
• Nine paraphrasing modes
• Language translator capability

See the very best of today’s generative AI tools: Top Generative AI Apps and Tools

Frase.io

Best for SEO Teams and Content Managers

Frase.io is an AI writing tool designed to help you generate content, provide suggestions for better writing, and optimize articles for SEO. SEO teams and content managers use its templates and outline builder to automatically produce article structures that align with the intent of the searcher, and as a result, increase the chances that the article will rank highly in search engine results pages (SERPs).

Frase’s keyword optimization feature will identify important keywords while you write, make suggestions about how frequently to use them, and track how often they are used in the copy. In addition to keyword tips, it also tells you the ideal number of headings, words, links, and images your articles should have to outrank the competition.

Many of the SEO managers I’ve worked with have used Frase, and I’ve found it especially helpful for optimizing articles for SEO. Watching the SEO “topic score” go up as I make edits is motivating and lets me know I’m on the right track.

Pros and Cons

Pricing

• Trial: $1 for five days
• Solo: $15 per user, per month, or $144 billed annually
• Basic: $45 per user, per month, or $456 billed annually
• Team: $115 per month, or $97 billed annually, for three users; $25 per month for each additional seat

Frase also offers a Pro Add-On that allows unlimited AI content for $35 per month, but does not offer a free plan.

Key Features

• Automated content briefs and outlines
• Content SEO topic scoring capability
• AI written SEO-optimized copy
• Suggestions on keyword optimization
• Google Search Console (GSC) integration

For a detailed look at a leading AI tool, see our guide: ChatGPT: Understanding the ChatGPT ChatBot

Anyword

Best for Copywriting Performance Analysis

Anyword is an AI writing tool that uses machine learning algorithms to generate content and analyze the performance of your copy across various channels. What distinguishes the tool is its Copy Intelligence functionality, which analyzes all of your previously published content to determine which messaging works best on your website, ads, socials, and email channels while clueing you into opportunities to improve your copy.

Its Target Audience feature lets copywriters and marketers define their ideal readers down to their key problems and desires. The AI writing tool will then take this into account when creating and analyzing content.

I found Anyword’s templates extremely helpful for prompting the AI writer to create content that fit my needs. In addition, its self-guided wizards walked me through the information I needed to provide the tool for it to write a blog post or ad campaign for my needs.

Pros and Cons

Pricing

• Starter: $49 per user, per month, or $468 billed annually
• Data Driven: $99 per month, or $948 billed annually for three users
• Business: $499 per month, or $5,988 billed annually for three users
• Enterprise: Custom pricing

Key Features

• Supports up to 30 languages
• Google Chrome extension available
• More than 100 performance-driven templates
• Integrates with Grammarly
• Analyzes copy performance
• Identifies ways to enhance your content
• Adheres to your brand guidelines and target audience data

Grammarly

Best for Grammatical and Punctuation Error Detection

Grammarly is a popular AI writing app that helps you improve your writing by checking for grammatical and spelling mistakes and offering suggestions for enhancing clarity, tone, conciseness, and style. It can be used in a wide range of contexts, including writing emails, reports, essays, or social media posts.

Grammarly can be used as a browser extension, a desktop application, or a mobile app. It’s free to use with limited features, or is available with additional functionalities as a premium subscription.

I’ve found Grammarly’s free version to be extremely useful as an AI editing tool. When I use it to run a final grammar and spelling check on a finished article, it inevitably catches mistakes and offers solutions to fix them. I also use it when writing emails to get the tone right. For example, when emailing a new client, I like to make sure the tone is confident and upbeat—Grammarly’s tone detector helps me achieve that goal.

Pros and Cons

Pricing

• Free: Free version lacks some high-end features
• Premium: $30 per month, or $144 billed annually
• Business: $25 per month, or $300 billed annually

Key Features

• Real-time grammar correction
• Generative AI assistance
• Plagiarism checker
• Advanced clarity suggestions
• Writing tone detector
• Checks grammar, spelling, and punctuation

Hemingway Editor

Best for Content Readability Measurement

Named after a writer known for his concise and simple prose, Hemingway Editor is a writing tool that helps you enhance the clarity, grammar, and readability of your written work. It analyzes text and provides various readability suggestions while highlighting lengthy, complex sentences, excessive adverbs, passive voice, and hard-to-read phrases.

It also assigns a readability score based on the grade level required to understand the text. If the score is too high, content writers can tweak the highlighted sentences to lower the grade level. It is available both as a web-based application and as a desktop app.

I’ve used Hemingway in various capacities and I find that it does its main job of measuring content readability extremely well. Its passive voice detector is my favorite part of the tool.

That said, one of the risks of using an AI editing tool like Hemingway is that, if you follow its suggestions without thinking, you may edit all the rhythm, flow, and personality out of your writing, leaving you with text that’s plain, lifeless, and stilted and suited for a grade level well below your target. Overall, though, I’ve found it a useful tool for making sure blog content is clear and easy to understand while streamlining the editing process.

Pros and Cons

Pricing

The platform is free to use.

Key Features

• Improves writing clarity and conciseness
• Provides a readability score
• Offers alternative word suggestions
• Detects passive voice and excessive adverbs
• Highlights hard-to-read sentences

To see a comparison between two leading AI tools, read our guide: ChatGPT vs. Google Bard

Writesonic

Best for Blog Content Writing

Writesonic uses artificial intelligence technology, specifically natural language processing (NLP), to provide content generation services. It’s one of the best AI writing tools for creating full blog posts.

Writesonic’s AI can generate text based on prompts and user input. In the case of creating a blog post, the wizard will ask you to input information such as article length, keywords, number of headings, topic, and references. Once you’ve approved of the outline, it’ll generate a blog post following these instructions as well as SEO optimization best practices.

While it can be a helpful resource for automated content creation, the quality of generated content may vary, and human editing is often required to ensure accuracy and coherence.

Pros and Cons

Pricing

• Free: 25 generations per month
• Standard: $948 billed annually
• Professional: $2,388 billed annually
• Advanced: $4,788 billed annually

Key Features

• Supports GPT 3.5 and GPT 4
• AI article and blog writer
• Article rewriter
• Sentence expander
• Text summarizer
• Story generator
• Landing page generator

AI Writer

Best for High-Output Bloggers

AI Writer is designed to generate full-length articles in minutes, making it great for high-output bloggers, affiliate marketers, and other people who need articles fast. The platform lets you tailor the AI’s writing to your specific needs by selecting from a long list of recommended keywords for your topic or by manually inputting your chosen keywords. It also suggests sub-topics for your article and helps you structure your content with headings. The tool even cites its sources, displaying journalistic responsibility.

Significantly, it helps you choose topics about which to write. While exploring the platform, I found its topic generator helpful for coming up with article topics using a seed keyword—after prompting with “cold calling,” for example, I received a long list of article keywords and their associated traffic. I then selected Research and Write, a one-click article feature for Cold Calling Leads, and within minutes, I had a basis for a new 600-word article on the topic.

Compared to other AI writing tools, AI Writer’s blog content creation was incredibly quick. However, a note on quality—although AI Writer’s articles are typically well-written, SEO-friendly, and factually correct, they could benefit from a writer’s touch. When I took another pass to add personality, anecdotes, actionable insights, and personal experiences to the copy, it became more appealing to readers and search engines.

Pros and Cons

Pricing

• Basic: $29 per user, per month
• Standard: $49 per month for three users
• Power: $375 per month for 10 users

Key Features

• AI text generator
• SEO editor
• One-click article writer
• Topic generator based on seed keywords (with traffic estimate)
• Can publish to WordPress
• Email composer
• Text rewording capability

ContentatScale.ai

Best for Creating Long-Form Articles

Those looking for an AI writing tool to automatically create long-form articles (over 2,000 words) may find ContentatScale features suitable. After plugging in your topic and further context, it will automatically generate body text, headers, subheaders, a title, bulleted lists, a call to action, and other content elements necessary for effective long-form SEO articles. Not only will it write the blog post—it will also help you come up with ideas, outlines, and relevant keywords in a fraction of the time it would take you to do it manually.

The platform claims to pass AI detection tests, indicating that its generated content mimics human writing and is not easily distinguishable from human-written content. ContentatScale also offers an AI detector solution that ranks as one of best reviewed AI detector tools. Overall, it can be helpful for streamlining the long-form content writing process.

Pros and Cons

Pricing

• Trial: Seven days; $39.99
• Solo: $250 monthly for eight posts
• Starter: $500 per month for 20 posts
• Scaling: $1,000 per month for 50 posts, or $1,500 per month for 100 posts

Key Features

• Keyword, YouTube, podcast, file, blog outputs
• Unique Voice AI Training options
• AI detection capability
• Natural language processing analysis

How to Choose the Best AI Writing Software for Your Business

Choosing the best AI writing tool for your business depends on your AI writing needs and your budget. Our “best-for” use cases are a good starting point to help you decide. In addition, here’s a little more information to help you narrow down your choice:

• Copy.ai and Rytr are best for copywriters and social media managers looking to create short-form content and marketing copy.
• AI Writer, Anyword, and Writersonic are solid options for those looking to create blog posts at scale.
• Frase can help you optimize your content for SEO.
• Grammarly and Hemingway are great options for checking grammar, spelling, and style in your AI-generated content.
• QuillBot and Writesonic may be the best for paraphrasing.

Other factors to consider when choosing the best AI writing tools for your business include features, content quality, user interface, and customization options. Keep in mind that it doesn’t have to be an either-or choice—these tools can be used together for better quality content. For example, you can use QuillBot to paraphrase Anyword’s AI-generated text, Grammarly to correct spelling and punctuation errors, and the Hemmingway App to improve readability.

How We Evaluated the Best AI Writing Tools

We weighed the best tools across five categories. Each category has subcategories that helped us evaluate and compare the AI writing tools.

Feature Set | 30 percent

We assessed the writing capabilities of each tool, including its grammar and spelling correction, sentence rephrasing, and content generation capabilities. We looked for tools that provided accurate and high-quality writing suggestions.

Quality of Output | 25 percent

We evaluated the accuracy and coherence of the generated content produced by each AI writing tool. Tools that could generate clear, well-structured, and error-free content received higher scores.

Cost | 20 percent

We examined the different pricing plans offered by each AI writing tool. This included evaluating the cost of the tool on a monthly or annual basis, as well as any additional fees or hidden costs. We compared each tool’s cost to its value, looking for tools that offer a high level of functionality for a reasonable price.

Support | 15 percent

We assessed the availability and responsiveness of customer support channels, such as email, live chat, or phone support. Prompt and helpful customer support is essential for users who may encounter issues or need assistance with the tool. We also considered the availability of resources and documentation, such as user guides, tutorials, or knowledge bases.

Ease of Use | 10 percent

We looked for tools that have an intuitive and user-friendly interface, allowing users to navigate and utilize the tool’s features easily.

FAQs (Frequently Asked Questions)

Is there a free AI writing tool?

There are plenty of free AI writing tools for content creation, editing, and paraphrasing, including some of those reviewed in this guide. While many of the AI writing tools’ advanced features are restricted to paid users, some writers will find the feature sets in the free plans sufficient for their needs.

How can companies benefit from AI writing tools?

Companies can benefit from AI writing tools by using them to streamline the content creation and editing process across various marketing channels, from social media to SEO blog content. These tools can automatically generate content, help with brainstorming, and suggest edits to improve your copy. Overall, AI writing tools help businesses improve efficiency and increase content marketing output and quality.

Will the content created by AI writing tools seem robotic?

The output of AI writing tools will not have the compelling quality of writing created by human writers. After all, AI writing tools are merely generating text—they’re not thinking, considering personal anecdotes, or making unique connections between ideas. Ultimately, the output of AI writing tools need a human touch to create quality writing.

Bottom Line: Top AI Writing Tools Improve Efficiency

This guide highlights our recommendations for the best AI writing tools to help with your writing needs, but they may not fit every use case perfectly. AI is a tool that can help with idea and content generation and quality improvement, but the best results still require a skilled human touch. When used properly, these tools can boost efficiency and are likely to continue driving changes in any market sector that relies on written content.

Read next: for more assistance with your projects, read our analysis of the best AI chatbots and the top conversational AI tools.

The post 10 Best AI Writing Tools (2024): Enhance Your Writing with AI Magic appeared first on eWEEK.

5 Benefits of Using AI Personalization for Marketing

Personalizing your marketing campaigns can give your content a human touch and boost revenue, letting you deliver tailored experiences to better engage individual customers, make data-driven campaign decisions, and increase sales.

Highly Targeted Ads

Targeted advertising lets you reach the right audience with the right message at the right time. Customer data plays an important role in personalizing ad experiences. AI algorithms can analyze vast amounts of data to identify patterns and preferences. Unlike traditional advertising, AI helps brands enhance ad targeting and segment audiences based on demographics, interests, buying histories, and more. With AI, marketers can launch hyper-targeted advertising campaigns that resonate with individual consumers.

Engaging and Relevant Customer Experience

While AI solutions don’t replace human support agents, they optimize workflows and deliver customized content to customers. Businesses can use AI technologies such as natural language processing (NLP), machine learning, and sentiment analysis to understand individual preferences and behavior across various touchpoints. Marketers can identify patterns and factors used to build more valuable relationships, so they can tailor recommendations for products and services or deliver personalized support.

Increased Sales and Conversion Rates

AI can analyze customer preferences, purchase histories, and browsing behaviors to help you customize offers and discounts for individual customers. With AI, your business can implement statistical data modeling and machine learning techniques to predict buying patterns, sales opportunities, and market trends. Understanding customer needs better enables brands to make products more appealing to customers and can significantly increase the likelihood of conversion.

Enhanced Customer Loyalty

Personalization helps companies develop comprehensive and customized profiles of customers to better understand how to serve and retain them. Businesses can use them to tailor loyalty programs and relevant rewards. AI can also predict future purchases or the likely churn rate, which refers to the percentage of subscribers who discontinue subscribing to a service or product. AI also automates campaigns, so personalized content can reach customers at the right time via the right marketing channel.

Optimized Marketing Efforts

AI algorithms can consistently monitor and analyze campaign performance, helping marketers adjust their strategies in real time to maximize results. AI generates data-driven recommendations so businesses can optimize their resources and enhance campaigns for specific marketing channels, increase brand awareness, and engage with customers effectively. AI can also predict future customer behavior and preferences, letting you know which products, services, or content you should offer over a certain period.

Practical Applications of AI-Based Personalization in Marketing

For marketers, AI is a game-changer. As we’ve shown, it can analyze vast amounts of customer data to create targeted campaigns. But according to Babak Hodjat, CTO of technology solutions provider Cognizant, AI’s real power in marketing comes from orchestrating it alongside your business data to help make decisions that drive your key performance indicators. Your business has a range of options for how it might best implement AI-based personalization.

Watch eWeek’s interview with Cognizant CTO Babak Hodjat to hear more of his insights about the power of AI to reshape marketing.

Personalized Emails and Messaging

AI email personalization uses AI algorithms to customize email content for individual customers, which can significantly improve open rates, click-through rates, and conversions. According to HubSpot’s 2023 State of Generative AI Report, around 95 percent of marketers who use generative AI for email creation find the technology effective—and 54 percent rate it as very effective. Email marketing platforms analyze customer data, including past purchases, browsing history, demographics, and social media data, to send targeted emails with personalized product recommendations, offers, and content.

Tailored Product Recommendations

An AI-based product recommendation engine can analyze vast amounts of customer data to suggest products that align with individuals’ interests and preferences. By generating new data points from learned patterns, AI can recommend products disseminated on websites, apps, and by email to enhance the customer experience, increase sales, or improve retention.

Dynamic Website Content

Machine learning algorithms analyze huge amounts of data to understand a consumer’s behavior and preferences. With this data, AI can personalize website content and display product recommendations, customized banners, and relevant call-to-actions, leading to a more engaging experience.

Predictive Analytics and Forecasting

Using AI for predictive analytics is more efficient than doing it manually because you can quickly collect, organize, and analyze data from multiple sources. Businesses use AI-powered predictive analytics models to forecast future sales volumes, trends, purchase likelihood, churn risk, and more. You can use the patterns and correlations in past sales data to launch targeted campaigns to encourage purchases and retain customers.

Language and Sentiment Analysis

AI sentiment analysis employs AI and deep learning techniques to determine the overall sentiment, opinion, or emotional tone behind textual data. AI-powered sentiment analysis tools can analyze customer feedback, social media posts, and reviews to understand sentiment and identify areas for improvement, so businesses can address concerns and improve their products or services.

Assistive Search Features

AI-powered search engines play an important role in enhancing the user experience, using advanced algorithms to analyze user queries and provide personalized search results based on user history, preferences, and context. This helps users find relevant information more quickly and accurately.

Challenges and Considerations for Implementing AI Personalization

While AI personalization provides numerous benefits for marketers and businesses, there are also several challenges and considerations—including costs, ethical issues, and data privacy and security concerns—that you need to be aware of before implementation.

Data Privacy and Security Concerns

AI personalization relies primarily on collecting and analyzing customer data, which can raise concerns about data privacy and security. Businesses need to ensure that customer data is collected and used appropriately in compliance with relevant regulations. Because AI technology isn’t confined to one state or jurisdiction, it can be challenging to create and maintain standard privacy practices and governance. Common data privacy and security concerns in AI personalization include the following:

• Data Collection and Consent: AI personalization relies heavily on collecting vast amounts of personal data, and it’s necessary to inform customers about what data is being collected and how it’s being used.
• Data Security and Breaches: Securing vast amounts of data increases the risks of data breaches and cyberattacks, making it essential to implement robust security measures.
• Data Bias and Discrimination: AI algorithms can cause biases if they learn from data that is unbalanced or reflects discrimination; AI models must be trained on diverse and representative data to avoid discrimination against certain groups.
• Filter Bubbles: One of the most common implications of AI-driven personalization is the creation of filter bubbles caused by excessive personalization, which happens when users are only exposed to information that aligns with existing beliefs, reinforcing existing biases.

Implementation Costs and Resources

AI implementation for marketing personalization requires a significant investment of financial and technical resources. However, the cost can vary depending on the complexity of the AI solution, business size, and marketing personalization goals. Common costs involved in implementing AI personalization include the following:

• Software and Platform: Licensing fees for solutions can range from hundreds to thousands of dollars per month depending on features, customization, and users, and some platforms offer pay-as-you-go pricing models or annual subscriptions; determine which pricing structure fits your budget.
• Infrastructure and Hardware: For customized AI solutions, consider the cost of servers, storage, and networking infrastructure; for more cost-effective solutions, consider cloud-based AI platforms to avoid the additional cost of on-premises infrastructure.
• Data Preparation: Before you can use customer data you need to collect, clean, duplicate, and format it. This process can be time-consuming and expensive if you need to deal with a large amount of data.
• Technical Expertise: Creating a team of experts—including data scientists, machine learning engineers, and AI specialists—can be expensive, and you’ll also need to train your marketing team to understand and use personalization platforms, which can incur additional costs.

Ethical Considerations

While AI personalization can enhance marketing strategies, it also poses several ethical concerns that businesses should carefully consider. Here are the most common:

• Transparency and Consent: Companies must be transparent about how they use AI solutions for personalization and should obtain explicit consent from users; customers should be fully aware of how their data is being used and should have control over what they want to share.
• Data Minimization: Businesses or researchers should collect only the necessary data for personalization and use it solely for the intended purpose; avoid gathering excessive or irrelevant information that might cause privacy concerns.
• Avoiding Manipulation and Coercion: Respect users’ autonomy and avoid manipulating them; manipulation and coercion might include restricting the available and acceptable options for users or offering material incentives.
• Bias Mitigation: This involves employing various techniques and methodologies to reduce the impact of biased data and algorithms; companies should also regularly audit and monitor algorithms to detect and correct biases.

Best Practices for Effective AI Personalization Marketing

Using the right AI personalization tools can improve marketing effectiveness. However, businesses should implement the solution strategically and ethically to optimize the investment they make.

Identify Audience and Goals

Before implementing your AI personalization strategy, clearly define your goals and identify your target audience. Be as detailed as possible in determining the outcomes you want to achieve, the resources you can invest, and the customer segment you need to reach.

Collect Data Thoroughly and Ethically

Ensure that your data collection practices are ethical and adhere to privacy regulations. Integrate data from other sources, like CRM systems, social media tools, and analytics platforms, to get a more comprehensive view of your customers.

Use the Right Tools

Invest in the right AI solutions or marketing tools to fit your goals, needs, and budget. Also consider the solution’s usability, scalability, built-in AI features, and customization options.

Monitor and Refine

Continuously test different strategies and monitor your results, so you can refine your marketing campaign based on data-driven insights.

Add a Human Touch

Incorporate human touch into your personalization strategy—for example, use AI to segment customers quickly but let a human customer support representative follow up on questions or concerns.

Remain Compliant

Comply with data privacy regulations such as the European Union’s General Data Protection Regulation (GDPR) and AI Act and the California Consumer Privacy Act (CCPA).

5 Essential Tools for AI Personalization Marketing

A wide range of tools are available to help marketers analyze customer data and use AI to personalize campaigns. They can generally be sorted into five types: customer data platforms, machine learning algorithms, marketing automation tools, personalization engines, and analytics and reporting tools.

Customer Data Platforms

Customer data platforms (CDPs) centralize customer data from various sources to create a unified customer profile. They allow businesses to pull customer data from any channel, system, or data stream and combine it into an accessible database to help companies create personalized customer experiences.

For example, Salesforce Data Cloud is a leading CDP deeply embedded in Salesforce’s Einstein 1 platform that combines CRM, AI, data, and transparency. This solution allows users to integrate an external data lake or warehouse directly into your CRM platform and offers features such as real-time data unification, AI-powered insights, and easy integration with Salesforce Marketing Cloud.

Machine Learning Algorithms

Machine learning algorithms are the backbone of AI personalization, as they allow users to view trends in customer behavior and business trends. Machine learning tools enable marketers to easily identify patterns, predict behaviors, and segment their customer base.

For example, Alteryx provides a powerful platform for users to employ machine learning models to reduce bottlenecks and scale data science processes. This ML solution has easy-to-use machine learning models, making it easier for the user community to share knowledge and collaborate. Alteryx is also flexible, providing users with a robust solution for building and deploying custom machine learning models specifically for marketing use cases.

Marketing Automation Tools

AI marketing tools streamline marketing tasks and campaigns to let marketers focus on more creative endeavors and strategic planning. Integrating AI into marketing efforts helps you automate repetitive tasks, improve targeting and segmentation, enhance personalization, and predict trends.

For example, Jasper AI helps marketers generate personalized content for email marketing, social media, and customer service. Marketers can ideate and execute campaigns, communicate better with their teams, and optimize content with Jasper AI’s tools for automation, copywriting, analytics, and more.

Personalization Engines

Personalization engines help marketers deliver and measure the optimum experience for customers by using AI algorithms to personalize content based on past interactions, current engagement, and predicted intent.

For example, MarketMuse is an AI-powered content strategy platform that helps marketers create personalized content relevant to their target audiences. Marketing teams can back their content planning, creation, and optimization with data-driven insights.

Analytics and Reporting Tools

AI-powered analytics and reporting tools make it easier for marketers to deliver personalized experiences by analyzing marketing campaigns, tracking personalization efforts, and determining opportunities for improvement. The right analytics and reporting tools empower businesses to make informed and strategic decisions for their marketing efforts and campaigns.

Microsoft Power BI is a robust AI-powered analytics and reporting tool specifically designed for business intelligence. This platform allows marketers to collect, visualize, and analyze marketing data to measure the impact of their personalized marketing solutions almost in real-time.

3 Recommended Coursera Courses for Learning AI Personalization Marketing

A number of online education providers offer training on all manner of artificial intelligence technologies. We recommend three in particular, provided by Meta, the University of Virginia, and the University of Colorado.

Artificial Intelligence in Marketing, University of Virginia

Developed at the Darden School of Business, this course delves into the fundamentals of AI and its application in marketing. You will learn about the three important factors that enable AI in marketing strategies: algorithms, networks, and data. You can explore how to harness data-driven AI solutions for improving customer experiences and see real-world examples of successful companies shaping their respective industries using AI. No prior experience is needed to participate in the course, making it an ideal introductory course for beginners.

Meta Social Media Marketing Professional Certificate, Meta

This professional certificate course helps prepare you for a career in social media marketing. You can receive professional-level training from Meta for using Meta Ads Manager to launch Facebook and Instagram ad campaigns and interpret results from these campaigns. After completing the six-course series, you can demonstrate proficiency in portfolio-ready projects and earn an employer-recognized certificate from Meta. Beginners who want to qualify for in-demand job titles such as social media manager, social media specialist, and social media coordinator will find this professional certification beneficial for their careers.

Machine Learning for Marketers, University of Colorado

This course helps you apply machine learning techniques to marketing and strategic decision-making, and you’ll learn to analyze and forecast customer behaviors using advanced algorithms. As part of the Data Science for Marketing Specialization, enrollment in this course also enrolls you in this specialization. Upon completion, you’ll gain skills such as campaign analysis and testing, predictive analytics in marketing, machine learning, personalized marketing strategies, and more. This course is offered at an intermediate level, so experience in marketing and a basic understanding of data analytics are recommended.

Bottom Line: The Future of AI Personalization Marketing

AI personalization is transforming how marketers and businesses connect with their customers, offering tailored experiences that boost conversions and drive growth. AI personalization in marketing entails various challenges and ethical considerations, necessitating an in-depth understanding of how to balance the use of AI with protecting customer privacy. The right AI tools and strategies will not only help businesses harness the full potential of AI in campaigns but also create a future where marketing is truly personalized for every individual.

Read our list of the top AI companies in 2024 and the leading AI startups to learn more about the businesses driving AI forward in marketing and other industries.

The post AI Personalization Marketing: The Future of Customized Advertising appeared first on eWEEK.

Top 5 Benefits Of Using AI In Content Marketing

Utilizing AI in content marketing provides a range of benefits that change the way you create, distribute, and deliver your content.

Boosting Content Marketing ROI

AI marketing tools help campaigns achieve superior quality, boosting the return on investment. Through advanced data analysis, it helps you understand which strategies yield the best results for better resource allocation. AI solutions also automate content distribution to give you full confidence that your targeted message will reach your intended audience at the most opportune moment.

Saving Time and Resources

AI tools can perform repetitive tasks such as keyword research, content scheduling, and performance monitoring to streamline your content marketing. By handling routine activities, it frees up valuable time to focus on the creative aspects of your work. AI can also help generate content ideas, draft articles, and create visuals, reducing the need for extensive human effort and cutting down on operational costs.

Improving Content Quality

You can use AI content recommendations to refine your writing and make sure it aligns with audience preferences. AI solutions can help you analyze which type of content will have the most impact with your target demographics, providing insights that guide you to produce relevant, accurate, and compelling content.

Ranking Higher In Search

AI algorithms can identify trending keywords, evaluate competitor strategies, and predict changes in search engine algorithms to optimize SEO content. With AI, you can also enhance the crawlability and indexability of your content by adjusting on-page elements like meta tags, headings, and alt text.

Making Your Content More Engaging and Consistent

AI analyzes user behavior and preferences, enabling you to tailor content that appeals to the individual tastes of your audience and increasing engagement. It helps maintain a consistent brand voice and messaging, even when content is produced at scale or by different team members. This uniformity contributes to building a stronger brand identity.

How To Use AI In Content Marketing: Top 5 Uses

There are many ways to use AI in content marketing, from brainstorming ideas to automating content-related processes.

Researching Content Ideas

AI excels at researching content ideas by analyzing vast amounts of data from social media, forums, and search trends. It helps you determine emerging topics, forecast what subjects will appeal to the audiences, and uncover gaps in existing content. It can also help you craft interesting and timely ideas that will help you connect with your target audience.

Creating Content

Natural language processing (NLP) and machine learning (ML) can support you in drafting high-quality articles, blog posts, and social media updates quickly and at scale, often indistinguishable from human-written text. By automating the writing process with AI content creation tools, you can compose a large volume of content, meeting the increasing demand for fresh material while freeing up human writers for more complex tasks.

Rewriting And Optimizing Your Content for SEO

Optimizing content for SEO by rewriting text to better align with search engine algorithms is another thing AI does well. It analyzes your current content’s performance, detects keyword opportunities, and offers necessary adjustments in structure, readability, and keyword density. You can make sure that your content includes the right mix of keywords, meta tags, and backlinks, increasing its visibility and ranking on search engine results pages.

Building A Content Marketing Strategy

You can develop a content marketing strategy with data-driven insights and recommendations from AI. By analyzing past performance, audience behavior, and market trends, AI solutions can suggest the most effective content types, distribution channels, and posting schedules. It can also predict future trends and audience needs, allowing you to plan proactive and responsive AI content strategies.

Common Challenges In AI Content Marketing And How To Overcome Them

Using AI for content marketing presents challenges that require preparation. Addressing these challenges will help you harness its full potential and maintain an effective and authentic strategy, while considering AI content ethics.

Ensuring Content Originality

AI-generated content can sometimes lack originality, producing repetitive material. This can diminish the uniqueness and value of your content, potentially harming your brand’s reputation and SEO performance. Use AI solutions with plagiarism detection features, and input diverse data during content generation. Combine AI capabilities with human creativity for unique perspectives, experiences, and originality.

Balancing Automation And Human Touch

Although AI can automate many aspects of content creation and marketing, over-reliance on this capability can make your content feel impersonal and mechanical. Achieve a balance by using AI to accelerate data analysis and idea creation, but use human input for final editing and adding a personal touch. Leveraging the strengths of both AI and human creativity will help ensure your content is both efficient and impactful.

Algorithm Bias

AI systems can inherit biases present in the data they are trained on, leading to biased content. Carefully select diverse data sets to train AI and conduct regular audits of AI outputs to find and correct biases. You can also implement ethical AI guidelines that can further help mitigate this issue.

Tips For Using AI In Content Marketing To Get the Best Results

It is essential to approach AI thoughtfully and strategically to get the most out of it for content marketing. Here are some tips to help you effectively use this powerful technology to your advantage:

• Maintain Human Oversight: Ensure that a human keeps an eye on the AI-enhanced content to maintain quality, authenticity, and relevance. By having a human monitor the output, you can catch any errors and ensure AI content accuracy.
• Integrate Personality Into AI Content: Add your brand’s unique personality into AI-generated content to make it more relatable. Use your brand’s voice, values, and style to develop content that strengthens your brand identity.
• Highlight First-Hand Experience and Expertise: Demonstrating your firsthand experience and expertise in your AI content establishes credibility and authority in your industry. By showcasing your knowledge, you can position yourself as a thought leader and differentiate your content from competitors.
• Structure Content To Maintain Traffic: By organizing your content into easily digestible sections through headings, bullet points, and visuals, you can improve readability, user experience, and traffic volumes. This engages readers and encourages them to explore more of your content.
• Use AI To Analyze Audience Behavior: Employ AI to understand your audience’s preferences, browsing habits, and engagement patterns so you can customize your content strategy to meet their needs effectively. As a result, you can deliver relevant content that interests your audience.

The Top 3 AI Content Creator Tools

There are several AI content creator tools on the market to help you fine tune your content and marketing strategies, including our top three recommendations: Jasper AI, Grammarly, and Writesonic.

Jasper AI

Jasper AI automates content marketing processes to save time and resources while maintaining quality, strengthening content marketing efforts. Its key features include AI content generation through NLP for blog posts, social media updates, and marketing copy. In addition, it suggests relevant keywords for better content visibility and ranking.

Jasper AI provides only limited customization options compared to other AI content marketing tools, but its intuitive interface and robust keyword suggestions make it an excellent choice if you want to improve your content marketing strategies.

Grammarly

Grammarly is an AI writing tool renowned for refining written content quality, which is needed to maintain credibility in content marketing. Its AI-powered assistance ensures error-free, clear, and compelling content, optimizing communication with the audience. Its grammar and spelling check features detect and correct errors and give suggestions to make your sentences clearer and easier to read. Grammarly also brings insights into tone and style to help you maintain a consistent brand voice and audience connection.

A downside of this AI tool is that it primarily focuses on language and grammar corrections and lacks in-depth content optimization features. But Grammarly’s precise error detection makes it an indispensable tool for producing polished and professional content.

Writesonic

Writesonic’s AI-driven capabilities facilitate creating different content types for a variety of content marketing strategies. It has content generation features through ML algorithms based on user inputs and preferences, and it provides templates for social media posts, product descriptions, and email campaigns. This AI content marketing software also offers SEO recommendations and headline analysis to elevate content engagement.

Writesonic doesn’t offer as many advanced features as some other AI writing tools on the market, but its versatile templates can equip your business in generating a wide range of engaging content.

Frequently Asked Questions (FAQs)

What Is The Future Of AI In Content Marketing?

The future of content marketing using AI is promising and transformative. The technology will continue to drive personalized content delivery, elevate content creation and curation, and optimize SEO and content. However, it will also raise ethical considerations such as transparency, bias, and data privacy. Striking a balance between leveraging AI’s capabilities and upholding ethical standards will be of utmost importance in building trust with your audiences.

Will AI Take Over Content Creators?

AI stands to complement the efforts of content creators rather than replacing them outright. While AI can handle specific tasks like text generation and data analysis, human creativity, intuition, and empathy are irreplaceable for crafting relatable narratives, comprehending audience intricacies, and staying abreast of shifting trends. Rather than displacing content creators, AI serves as a beneficial aid, streamlining workflows and increasing productivity.

Is There A Free AI Content Generator?

Yes. Among the many free AI content generators are the following:

• Squibler: This AI story writer helps you develop book chapters or screenplay scenes and assists with character creation and setting development.
• NeuralText: This AI platform facilitates content creation and SEO with features like live SERP analysis and a writing assistant.
• Copy.ai: This AI-powered writing tool uses ML to generate blog headlines, emails, and social media content.

Bottom Line: Advancing Strategies With AI In Content Marketing

AI enhances content marketing by facilitating personalized, efficient, and engaging content creation. You can use it for brainstorming ideas, writing SEO-friendly articles, and making sure your content is relevant and consistent. However, despite the benefits AI brings to the table, using this advanced technology has its challenges that require human intervention.

No matter how valuable AI is in content marketing, it is imperative to recognize that AI remains just that—a tool. Its primary function is to augment human capabilities, not replace them entirely. While AI can automate certain tasks, it’s ultimately the creativity and strategic thinking of humans that breathe life into content, making it impactful. By combining AI’s efficiency with human intuition, your content marketing strategies can thrive.

Read our article on the top 150 AI companies for 2024 and find out the front runners in the AI industry today.

The post AI for Content Marketing: Strategies, Tools, and Best Practices appeared first on eWEEK.

How to Create Prompts for AI Sales Prospecting

Understanding the foundational elements of AI prompts lets you maximize your use of AI in your sales process. AI prompts serve as the building blocks for engaging potential leads, ultimately leading to conversions, and mastering their creation and use takes deliberateness and practice.

A skillfully designed AI prompt translates your business goals into actionable tasks for your AI. Learning how to develop clear and effective prompts leads to better results from your AI tools. There are a number of key points to keep in mind when creating prompts for AI sales prospecting.

Be Clear and Concise

Ensure your prompt is straightforward and easy to understand, avoiding ambiguous language to make sure that the AI can quickly understand and act on your request without confusion.

• Bad Prompt: “Analyze sales data.”
• Good Prompt: “Analyze monthly sales data for the past year to identify seasonal trends.”

Specify the Context

Provide context to help the AI filter and find the most relevant information, making the results more accurate.

• Bad Prompt: “Find marketing managers.”
• Good Prompt: “List marketing managers who attended our 2024 digital marketing conference.”

Define Your Desired Outcome

Clearly state what you want to achieve with the prompt to get specific results, saving time and effort in the AI sales prospecting process.

• Bad Prompt: “Provide leads interested in cloud technology.”
• Good Prompt: “Identify 50 potential leads who are interested in cloud-based software solutions.”

Include Relevant Details

Include relevant details to refine the AI’s search and get results that are pertinent to your needs.

• Bad Prompt: “Find small business owners in retail.”
• Good Prompt: “Get contact details of small business owners in the retail sector interested in e-commerce platforms.”

Focus on a Single Task

Overloaded prompts can confuse the AI; concentrate on one task at a time for better efficiency and effectiveness.

• Bad Prompt: “Find IT managers and generate a list of potential cybersecurity clients.”
• Good Prompt: “Generate a list of IT managers looking for cybersecurity solutions.”

Set Parameters For Analysis

Define the parameters for the AI to focus on, narrowing down the search criteria and leading to more targeted insights.

• Bad Prompt: “Find companies interested in data analytics.”
• Good Prompt: “Identify companies with annual revenues over $10 million that are interested in data analytics.”

Indicate Your Preferred Output Format

Be specific on how you want the information to be presented, making it easier to integrate into your workflows.

• Bad Prompt: “Generate some information about countries and their GDPs.”
• Good Prompt: “Provide the top five countries by GDP in a table format.”

7 Ways To Use AI For Sales Prospecting

AI sales tools assist with a variety of sales prospecting tasks, from finding potential leads to predicting customer behavior. Relying on AI lets you optimize your time, focus on high-value activities, and grow conversion rates.

Automated Discovery and Research

AI tools can accelerate the process of identifying potential customers by scouring a variety of online sources, databases, and social media platforms for leads. This automation saves valuable time and ensures a comprehensive collection of relevant data. You can gather detailed profiles of prospects, uncovering insights that manual research might miss and laying a solid foundation for effective sales strategies.

Intelligent Targeting

AI algorithms can analyze vast amounts of data to pinpoint the most promising prospects. These algorithms consider demographics, past behaviors, and purchase history to find leads with the highest conversion potential. This intelligent targeting allows you to focus your sales efforts on prospects who are more likely to respond positively, boosting the effectiveness of your outreach.

Lead Scoring and Qualification

AI can refine lead-scoring by evaluating and ranking leads based on their likelihood to convert. By analyzing patterns and key data points, AI tools prioritize leads that exhibit behaviors and characteristics indicative of high-potential prospects. Through this targeted approach, you can spend time and resources on leads that are more likely to result in successful conversions, raising overall sales productivity.

Social Media Monitoring

You can monitor social media platforms for mentions of your brand, industry keywords, and competitor activities with AI. Identifying these mentions brings valuable insights into your potential prospects’ interests and needs. Additionally, integrating AI into your CRM lets you engage with prospects at the right moment with the most relevant information, increasing your chances of building meaningful connections.

Personalized Content Creation

AI can analyze your prospects’ online behavior, social media activity, and other digital footprints, guiding you in crafting highly personalized outreach strategies. With these insights, AI can generate tailored, compelling messages that resonate with individual prospects. Personalized content in the form of emails, social media posts, and other communications drives engagement and fosters stronger relationships with potential customers.

Customer Behavior Prediction

AI-powered predictive analytics equips you to anticipate future customer actions by examining historical data, trends, and patterns. As a result, you can make informed decisions and proactively address customer needs. By understanding and predicting customer behavior, you can tailor your sales strategies to meet evolving demands, driving higher conversion rates and customer satisfaction.

Sales Forecasting

You can use AI to analyze historical sales data and forecast future sales trends with greater precision, enabling you to strategize more effectively. This forward-looking approach will fine-tune your resource allocation and set realistic targets that align with anticipated market conditions for sustained growth and success.

5 Benefits of Using AI For Sales Prospecting

Incorporating AI in sales prospecting elevates how your business approaches lead generation and customer engagement. By using AI’s data analysis, machine learning (ML), and automation capabilities, you can gain a wide range of benefits and grow revenue. Here are five of the most common benefits:

• Saving Time and Increasing Efficiency: AI can automate tedious tasks like data collection and lead searching, giving you more time to focus on captivating potential customers and successfully closing deals.
• Reducing Costs: By automating data analysis and lead qualification, AI can lower the costs associated with these processes, letting you allocate more resources to activities that directly drive sales and growth.
• Prioritizing the Best Leads: Scoring and ranking leads become simpler, letting you concentrate on the most promising prospects and ensuring that you’re always targeting the right people and maximizing your chances of success.
• Crafting Personalized Messages: With the deep insights into customer behavior and interests from AI, you can write personalized messages that will allow you to connect with your audience and boost engagement rates.
• Targeting The Right Audience: Predictive analytics from AI can pinpoint companies and individuals that match your ideal customer profile, refining your targeting efforts and improving your chances of reaching the right audience.

3 Best AI Tools For Sales Prospecting

The market is saturated with AI tools to help with sales prospecting, some of which specialize in finding and prioritizing high-value leads while others focus on automating tedious tasks such as data entry and lead research. Finding the right tool depends upon your specific needs and budget, but generally speaking, we recommend three that meet a wide range of needs in this area.

Apollo.io

Apollo.io is an all-in-one sales platform with built-in AI features for effective sales prospecting. It generates rich buyer data, with access to over 275 million verified contacts and more than 65 filters, including buyer intent, job postings, and headcount growth.

The platform stands out for its AI-powered writing assistant that lets you create hyper-personalized emails for every stage of your sales pipeline. It also has lead-scoring, contact database management, and email outreach solutions.

Apollo.io can occasionally generate incorrect contact information in its vast database, making some of the emails bounce. But this AI tool remains a top choice despite this drawback due to its solid automation capabilities and personalized outreach features.

Wiza

Wiza is an AI sales prospecting and engagement platform that extracts verified email addresses and phone numbers from LinkedIn profiles, making it a valuable resource for sales professionals and marketers.

The platform can turn LinkedIn search or saved lists into valid email lists, scrape leads in bulk, and produce a clean spreadsheet with the contact info. It also has real-time prospecting and email verification with a high deliverability rate.

Wiza lacks the extensive integrations offered by some competitors, but its ease of use and ability to quickly and efficiently provide high-quality leads compensates for its shortcomings.

Seamless.AI

Seamless.AI is a sales prospecting tool that applies AI to streamline the process of finding and connecting with potential customers. It empowers you to build a sales pipeline, shorten your sales cycle, and close more deals.

This AI tool comes with a search engine for B2B sales leads, firmographic filters, and business insights that aid in building leads lists. It also has AI recommendations that continuously find prospective buyers as well as a Chrome extension for searching contact information anywhere on the web.

It’s worth noting that some users have reported issues with the quality of data and limited data source from Seamless.AI. However, its sophisticated AI sales prospecting features—like automated list-building and real-time data verification feature—ensure that your contact database is error-free, making it a worthy investment.

AI Ethics and Best Practices in Sales Prospecting

AI continues to revolutionize sales prospecting by enabling businesses to more efficiently discover and engage potential customers. However, using this technology requires a strong commitment to ethical practices to ensure transparency, fairness, and respect for privacy. It’s important to keep ethical considerations in mind and follow best practices for using AI in sales prospecting.

Data Privacy and Management

To ensure your business complies with data protection laws like the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), collect and process data transparently and strictly within the bounds of prospect consent. Implement robust data anonymization techniques and encryption protocols to safeguard personal information. High-quality data management is paramount—make sure your AI training data is accurate, relevant, and up-to-date, as low-quality data can produce inaccurate predictions and insights.

Bias, Fairness, and Personalization

To promote fairness, audit your AI models regularly for biases that could cause unfair treatment of certain groups. Using diverse datasets and fairness-aware algorithms helps you mitigate biases and prevent AI hallucinations. Make sure you obtain explicit consent from prospects before making use of their data for personalized outreach, respecting their preferences and privacy choices.

Transparency and Monitoring

Your AI-driven decisions should be interpretable so that stakeholders can understand how AI models operate. To build trust and align with ethical standards, inform your prospects when AI is being used in the engagement process and keep an eye on AI models to make sure they perform as expected and do not drift from ethical standards.

Accountability and Training

Maintaining human oversight in AI-driven processes is of utmost importance to uphold ethical standards. AI tools should assist human judgment rather than replacing it. Establish clear accountability for AI outcomes and put protocols in place to address any negative impacts caused by AI decisions for responsible AI use. Conduct regular training sessions for your sales teams on ethical AI use and best practices to make sure that all team members understand their significance.

Frequently Asked Questions (FAQs)

How is AI Used to Forecast Sales?

AI forecasts sales using ML algorithms. These algorithms analyze historical data and external factors like seasonality and market trends and give accurate future sales predictions by detecting patterns and relationships in the data.

Can I Use AI for Marketing Strategy?

Yes, AI tools can enhance marketing strategies. They analyze customer data to identify patterns and preferences, segment audiences for targeted campaigns, and predict customer behavior. AI can also automate personalized content delivery, optimize ad placements, and accurately measure campaign effectiveness.

Bottom Line: AI Sales Prospecting Can Unlock New Opportunities

By using AI for sales prospecting, you can improve your workflows, find high-value leads, and personalize outreach efforts—but you must craft effective prompts that guide the AI’s decision-making process. When developing your own prompts, keep them specific, relevant, and well-defined to ensure precise and actionable insights. Mastering basic prompts for AI sales prospecting lets you uncover new opportunities, elevate your sales performance, and stay ahead of the competition.

AI is a powerful tool for prospecting and other sales-related efforts. It simplifies pinpointing and engaging potential customers for various businesses, including retail organizations. Read our comprehensive guide on top AI retail solutions to discover the best AI tools for retailers today.

The post AI for Sales Prospecting: Master Basic Prompts to Drive Engagement appeared first on eWEEK.

In this TweetChat, held on the X platform, experts will share their insight on where today’s emerging technology is heading. We’ll talk about AI and generative AI, of course, and we’ll also look at the other major technologies that are reshaping business, from cloud computing to data analytics to cybersecurity.

Our goal is to provide businesses with advice and guidance on how to best navigate today’s constantly shifting tech landscape.

See below for the resources you need to participate in the eWeek TweetChat.

Expert Panelists

The list of experts for this month’s TweetChat currently includes the following – please check back for additional expert guests:

• Eita Yanagisawa, Sr. GM of Systems Solutions, Sony Semiconductors Solutions
• Victor Coustenoble, Staff Solution Architect & SEMEA Team Lead, Starburst
• Evan Kirstel, Chief Digital Evangelist, eViRa Health
• Shipra Sharma, Head of AI and Analytics, Bristlecone
• Sven Winnefeld, VP of AI & Innovation, Hotwire Global Communications
• Nick Haase, Co-Founder, MaintainX 
• Andi Mann, Global CTO and Founder, Sageable
• Zeus Kerravala, Founder and Principal Analyst, ZK Research
• James Maguire, Editor-in-Chief, eWeek [moderator]

TweetChat Questions: Mid-Year Tech Predictions

The questions we’ll tweet about will include the following:

1. First, be honest: what tech development has most surprised you so far this year? From cloud to AI to data, what’s a development you weren’t expecting?
2. Because it’s 2024, let’s talk AI. What do you see in the future of generative AI over the next 6-12 months?
3. Cloud doesn’t get as many headlines, but it’s bigger than ever. What about the future of cloud over the next year or so?
4. Cybersecurity has only grown in importance. Your view on the near term evolution of security?
5. What’s a major worry you have about tech, and/or a challenge that deserves more attention?
6. Winner and losers over the next year or so in the tech sector? Not companies, but technologies, trends, industry efforts?
7. How about the fortunes of the average tech professional? How does the next 6-12 months look for the careers of tech pros?
8. What advice would you give to companies struggling to keep pace with today’s rapid tech changes?
9. What’s one last key point that executives and managers need to know about the near term future of technology?

How to Participate in the TweetChat

The chat begins promptly at 11 AM PT on June 18th. To participate:

1. Open Twitter in your browser. You’ll use this browser to Tweet your replies to the moderator’s questions.

2. Open Twitter in a second browser. On the menu to the left, click on Explore. In the search box at the top, type in #eweekchat. This will open a column that displays all the questions and all the panelists’ replies.

Remember: you must manually include the hashtag #eweekchat for your replies to be seen by that day’s TweetChat panel of experts.

That’s it — you’re ready to go. Be ready at 11 AM PST to participate in the TweetChat.

NOTE: There is sometimes a few seconds of delay between when you tweet and when your tweet shows up in the #eweekchat column.

TweetChat Tentative Schedule for 2024*

January 16: Governing Generative AI
February 13: Data Analytics Best Practices
March 12: How Tech Pros Get the Most From AI
April 16: Managing Multicloud Computing
May 14: Optimizing Generative AI
June 18: Mid-Year Look Ahead: Future of Tech

*all topics subject to change

The post eWEEK TweetChat, June 18th: Mid-Year Forecasts for Tech’s Future appeared first on eWEEK.

Machine learning (ML), on the other hand, helps computers learn tasks and actions using training modeled on results from large datasets. It is a key component of artificial intelligence systems. Let’s examine the question of generative AI vs. machine learning, dig deep into each, and lay out their respective use cases.

Key Takeaways: 

Comparison Chart: Generative AI vs. Machine Learning

Generative AI and machine learning are closely related technologies, as the chart below illustrates. While generative AI excels at creating content, machine learning is geared for data analysis and statistical models.

History and Evolution of Generative AI vs. Machine Learning

Over the course of several decades, the evolution of both generative AI and machine learning has been driven by the continuous development of algorithms designed to perform specific tasks.

Over the past decade, these algorithms have grown exponentially more complex, and at their most advanced, can gain functionality without continued human input. This ability to grow without human input is the very core of artificial intelligence, which is the foundation for both generative AI and machine learning.

In the last few years, advancements in computing power and data availability – enabled by cloud computing platforms like Amazon Web Services and Microsoft Azure – have enabled significant progress. Machine learning emerged first, focusing on identifying patterns and making data-driven decisions. Generative AI built upon these foundations, introducing models capable of creating new, original content by learning from vast datasets.

The major breakthrough for generative AI came in November of 2022, when OpenAI launched ChatGPT, an application that creates content based on text prompts and natural language queries.

What is Generative AI?

Generative AI is an emerging technology that uses artificial intelligence, algorithms and large language models to generate several types of content, from text to images to video.

Machine learning makes use of deep learning and neural network techniques to generate content that is based on the patterns it observes in a wide array of other content. Although this content is classified as original, in reality generative AI uses machine learning and AI models to analyze and then replicate the earlier creativity of others. It taps into massive repositories of content and uses that information to mimic human creativity.

With generative AI you can perform tasks like analyzing the entire works of Charles Dickens, JK Rollins or Ernest Hemingway to produce an original novel that seeks to simulate these authors’ style and writing patterns. Thus, generative AI ventures well beyond traditional machine learning. By utilizing multiple forms of machine learning systems, models, algorithms and neural networks, generative AI offers a new foray into the world of creativity.

Use Cases for Generative AI

Generative AI is used to augment but not replace the work of writers, graphic designers, artists, and musicians by producing fresh material. It is particularly useful in the business realm in areas like product descriptions, and can create many variations to existing designs. It can also help an artist explore novel concepts across various mediums. Here are some of its use cases, ranging from generative AI enterprise use cases to smaller scale implementations.

Retail

Generative AI significantly enhances retail operations by automating the creation of product descriptions, generating personalized marketing content, and optimizing inventory management.

For example, Amazon uses generative AI to create unique and SEO-friendly product descriptions, which improves search engine rankings and ultimately attracts more customers. This technology also enables hyper-personalized promotional messaging that adapts content to individual customer preferences based on their purchase history and browsing behavior​​. While machine learning focuses on analyzing data to provide recommendations, generative AI takes it a step further by creating the content itself.

Business

Generative AI in business can be seen in operations like generating reports, visualizing data, and creating marketing materials. Businesses can automatically generate business reports by analyzing large datasets and extracting key insights, which reduces errors and the time taken for these tasks. This allows business analysts to focus on strategic decision-making rather than mundane report preparation.​ In comparison, machine learning is typically used to analyze data patterns and provide predictive insights, while generative AI builds on these insights to produce comprehensive reports and presentations.

Synthesia’s video generation tool can be used for generating marketing content.

Healthcare

In healthcare, generative AI aids in the creation of synthetic medical data for research, developing personalized treatment plans, and enhancing diagnostic accuracy. Again, generative AI builds upon the foundation laid by machine; while machine learning excels in identifying patterns in medical data, generative AI goes further by generating new data and treatment scenarios that can aid in medical research and diagnostics.

Manufacturing

Generative AI is a welcome technology when it comes to prototyping. It optimizes product design and manufacturing processes by generating multiple design prototypes based on specific constraints and inputs.

Multiple companies today use this technology to improve product designs and improve their manufacturing process, which leads to cost reductions and enhanced product performance​​. While machine learning is used to predict maintenance needs and optimize production schedules, generative AI enables the creation of innovative designs and solutions that can be directly implemented in manufacturing.

Financial Services

In the financial sector, generative AI enhances services by generating custom financial reports, automating the detection of fraudulent activities, and improving risk management. For example, PayPal uses generative AI to analyze transaction data from emails, social media posts and phone calls to identify potential fraud in real-time, resulting in a secure transaction environment for its users​​. Where machine learning typically detects patterns and anomalies, generative AI not only identifies these issues but also creates detailed reports and strategies to address them.

Customer Support

Generative AI improves customer support through advanced chatbots and virtual assistants. Companies are adopting generative AI-powered chatbots to handle a wide range of customer inquiries, from product recommendations to order tracking. These chatbots continue to become increasingly similar to humans.

They can understand and respond to customer queries in a natural and conversational manner. The result: customers are more likely to leave the interaction satisfied and human agents will enjoy a lighter workload, at least in terms of customer queries. Generative AI can create personalized and contextually appropriate responses, making interactions feel more human-like and more effective than simple ML-based agents.

What is Machine Learning?

Machine learning uses artificial intelligence to learn and adapt automatically without the need for continual instruction. Machine learning is based on algorithms and statistical AI models that analyze and draw inferences from patterns discovered within data. Once set up, the ML system applies itself to a dataset or problem, spots situations and solves problems. It utilizes algorithms to parse data, learn and make decisions. Machine learning models train on large amounts of data to gradually learn and improve their accuracy rates over time.

The ML models used can be supervised, unsupervised, semi-supervised or use reinforcement learning. Regardless of how the model operates, it’s all about recognizing patterns and making predictions and drawing inferences, addressing complex problems and solving them automatically. Algorithms are procedures designed to solve well-defined computational or mathematical problems to complete computer processes. Modern ML algorithms go beyond computer programming as they require understanding of the various possibilities available when solving a problem.

Machine learning algorithms, then, can be regarded as the essential building blocks of modern AI. Machine learning finds a pattern or anomaly among the noise of data and finds paths to solutions within a time frame that no human could. Furthermore, machine learning helps impart autonomy to the data model and emulate human cognition and understanding.

Use Cases for Machine Learning

Machine learning has a great many use cases – and the use cases are continually expanding. In fact, machine learning has crept into just about every conceivable area where computers are used. Machine learning is found in data analytics, rapid processing, calculations, facial recognition, cybersecurity, and human resources, among other areas. Machine learning use cases include:

Retail

Personalization is one of the key use cases of machine learning. In retail, machine learning personalizes shopping experiences by recommending products based on customer behavior. For example, Amazon’s recommendation engine suggests products that match a user’s previous purchases to drive a sizable portion of its sales and enhance customer satisfaction. This personalized approach explains how Amazon has maintained a competitive edge and improved user loyalty.

Other uses include dynamic pricing, with algorithms that adjust prices in real-time based on market demand and competition to guarantee competitive pricing strategies. Additionally, ML optimizes supply chain and inventory management. It predicts stock levels to reduce costs and improve efficiency. Retailers also use customer behavior analytics to gain insights into preferences, enabling targeted marketing and personalized shopping experiences​.

Amazon’s recommendations for popular luggage choices; behind the scenes, these recommendations are driven by machine learning.

Business

Machine learning (ML) improves business operations by improving efficiency, reducing costs, and driving growth. One key application is predicting lead conversion, where ML algorithms analyze lead pipelines to prioritize and target potential customers effectively, which in turn enhances sales processes and boosts revenue.

Revenue forecasting benefits from ML by analyzing historical data and market trends to get accurate financial predictions that help with strategic planning and cash flow management. Additionally, product demand forecasting uses ML to predict customer demand accurately, optimizing inventory management, reducing overstock and stockouts, and aligning production with market needs.

A use case of machine learning for enhancing decision-making through predictive analytics can be seen in IBM’s Watson. IBM’s Watson leverages machine learning to analyze vast datasets, providing actionable insights and recommendations that help companies optimize operations, improve customer service, and drive innovation. For instance, Watson assists in various industries, from healthcare to finance, by offering predictive maintenance solutions and risk management​.

Healthcare

In healthcare, machine learning is revolutionizing diagnostics, treatment plans, and patient care. For instance, convolutional neural networks (CNNs) are used to diagnose diseases like skin cancer with high accuracy. Predictive analytics help healthcare providers anticipate patient outcomes and optimize treatment plans.

Personalized medicine is another significant application, where treatments are tailored to individual patients based on their genetic makeup and health data. ML also plays a critical role in medical imaging, assisting in the analysis of images for conditions such as arrhythmias and arterial plaque buildup, thus enhancing diagnostic accuracy and speed​.

Google’s DeepMind Health uses machine learning algorithms to analyze medical records and imaging data for early detection of diseases like diabetic retinopathy; its goal is to provide more accurate treatment recommendations.

Manufacturing

Machine learning is driving efficiency and quality in manufacturing through predictive maintenance, quality control, and supply chain optimization to optimize production processes. Companies like General Electric (GE) use machine learning to predict equipment failures and schedule maintenance, thereby reducing downtime and increasing efficiency.

By analyzing data from various sensors and historical maintenance records, GE’s machine learning models help in proactive maintenance planning, ensuring smoother and more cost-effective operations​.

Financial Services

Machine learning is crucial in detecting fraud and managing risks in financial services. For instance, PayPal uses machine learning algorithms to analyze transaction patterns and identify fraudulent activities in real-time. Such a proactive approach helps to mitigate risks and ensure secure transactions for millions of users worldwide.

Additionally, machine learning models assist in credit scoring and risk assessment, providing more accurate evaluations of financial profiles​. It’s also not uncommon to find machine learning used to provide personalized investment advice that’s adapted to individual financial goals and risk tolerance.

Customer Service and Support

Machine learning enhances customer service through the deployment of chatbots and virtual assistants. Companies like H&M and Sephora use machine learning-driven chatbots to offer instant support, product recommendations, and virtual try-on experiences.

The outcome? Improved customer engagement and satisfaction. These chatbots leverage natural language processing to understand and respond to customer queries, freeing up human agents to handle more complex issues​. Sentiment analysis of customer feedback helps businesses improve service quality and address concerns proactively.

Marketing

In marketing, machine learning analyzes customer data to create targeted campaigns. This helps marketers to understand and optimize conversion and retention rates. For example, Netflix uses machine learning to analyze viewing habits and preferences, generating personalized recommendations for each user. This targeted approach not only improves user engagement but also increases retention rates by providing content that aligns with individual tastes. Machine learning helps marketers design more effective campaigns by predicting customer behavior and preferences.

By understanding and optimizing customer interactions, ML reduces acquisition costs and increases conversion rates. Content curation tools like Curata use ML to personalize content based on user preferences, boosting engagement and ROI.

IT

IT operations benefit from machine learning in network security, predictive IT operations, and automated support. For a widespread use case like infrastructure management and monitoring, companies use machine learning models to predict potential system failures, optimize resource allocation, and automate routine maintenance tasks. For instance, Google’s Site Reliability Engineering (SRE) team employs machine learning to analyze logs and performance metrics, identifying anomalies and preventing outages before they occur​.

Banking and Finance

Algorithmic trading, customer insights, and compliance automation are some of the use cases often seen in finance. ML models analyze financial data to inform trading strategies and predict market trends. It’s typical to find modern banks using ML to gain deeper insights into customer behavior and as a result adapting financial products and services accordingly. Compliance checks and monitoring of financial transactions are also automated using ML.

A common use case of ML streamlining banking operations is the automation of processes like loan approvals and customer service. JP Morgan Chase uses machine learning algorithms to review legal documents and extract key information, significantly reducing the time required for contract analysis. Additionally, machine learning models help in detecting money laundering activities by analyzing transaction patterns and flagging suspicious behaviors​.

7 Key Differences

There are a number of key differences between generative AI and machine learning, ranging from the data/content outputs to the typical use cases.

1. Objectives

Machine learning primarily focuses on analyzing data to identify patterns, make predictions, and provide insights based on learned relationships. It is often employed for tasks such as classification, regression, and clustering. On the other hand, generative AI wants to create new, original data that mimics the patterns and structures observed in the training data. Generative AI models are used to produce text, images, music, and other forms of content that are becoming more and more indistinguishable from human-created data​.

2. Outputs

The outputs of machine learning models typically consist of predictions, classifications, or decisions based on the analysis of input data. For example, a machine learning model might predict future sales based on historical data or classify emails as spam or not spam.

In contrast, generative AI models produce entirely new data instances, such as generating an original image or writing a coherent piece of text. This capability allows generative AI to be used in creative and artistic applications where new content creation is required​​.

3. Performance Metrics

Machine learning models are generally evaluated based on predictive accuracy metrics such as precision, recall, and F1 score. With these metrics you can measure just how well the model’s predictions match the actual outcomes. Generative AI models, on the other hand, are assessed using qualitative metrics that evaluate the realism, coherence, and diversity of the generated content. Quantitative metrics like loss functions can also help in fine-tuning the performance of generative AI models​​.

4. Use Cases

Machine learning is widely used in applications like predictive modeling, recommendation systems, image and speech recognition, and fraud detection. These applications benefit from the model’s ability to learn from data and make accurate predictions.

Generative AI, meanwhile, excels in creative tasks such as generating text, with the most popular example being ChatGPT. Other leading examples are tools like DALL-E, Midjourney and Stable Diffusion, composing music and even generating video content. This makes generative AI suitable for applications in entertainment, content creation, and any field requiring innovative and original outputs​.

5. Complexity and Resource Requirements

Generative AI models are often more complex because of their creative nature and the diversity of outputs they produce. It’s normal for them to need lots of computational resources and extensive training times to achieve high-quality results. In comparison, ML models, depending on the specific algorithm and application, can vary in complexity and resource needs. Some ML models are relatively simple and efficient, while others, like deep learning models, can also demand significant computational power​.

6. User Interfaces

The user interface (UI) for machine learning applications typically involves dashboards and visualizations that display analytical results, predictions, and trends. These interfaces are designed to help users interpret data insights and make informed decisions.

In contrast, generative AI interfaces often include tools for content creation, such as text editors, image generators, and design software. These tools allow users to input parameters and generate creative outputs, providing a more interactive and exploratory experience​.

7. Learning Curve

The learning curve for implementing machine learning solutions is generally steep, which means that you’ll need a solid understanding of statistics, data science and algorithm development. You may also need to be proficient in data preprocessing, model training, and evaluation. However, even though generative AI, while also complex, may need expertise in specific generative models, many generative AI tools are becoming more user-friendly, with interfaces that allow non-experts to create content through simple prompts and parameters.

Advantages and Limitations of Generative AI

For all of generative AI’s advantages and capabilities, it’s plagued by a number of limitations. Here are some of the pros and cons to expect with generative AI.

Advantages

• Produces diverse content: Generative AI can produce diverse content such as text, images, music, and videos, making it valuable for tasks like generating articles, marketing copy, and design prototypes​.
• Automates tasks: Generative AI automates repetitive tasks, optimizing operational workflows and reducing human error, especially in software development​.
• Understands nuance: Generative AI performs well in understanding and responding to contextual nuances in natural language, making it suitable for applications like conversational agents and customer support​.
• Saves time: By expediting processes like content creation and decision-making, generative AI saves time and optimizes resource utilization​ for users.
• Analyzes vast datasets: Generative AI can analyze extensive datasets to understand customer preferences, which leads to unprecedented product or service personalization that yields improved customer experiences​​.

Limitations

• Prone to bias: Generative AI models aren’t safe from bias as they can perpetuate biases present in their training data to put out biased or discriminatory outputs​​.
• Lacks originality: While it can create new content, generative AI often relies heavily on existing patterns, meaning its ability to produce truly original content​​ is limited.
• Require huge compute resources: Training and running generative AI models require substantial computational power, making it resource-intensive and potentially inaccessible for smaller businesses​​.
• Raises ethical issues: The powerful capabilities of generative AI are associated with numerous risks and raise ethical issues, such as the potential for creating deepfake content and spreading misinformation​​.
• Needs constant inspection: Generative AI’s output may vary in quality and sometimes contains errors; it needs constant careful inspection and validation​.

Supporting Technologies

While generative AI and machine learning are advanced technologies, they still require the support of related AI-based technologies such as transformer networks, GANs and neural networks.

Transformer Networks

Transformer networks are a critical technology for both generative AI and advanced machine learning models, especially in natural language processing (NLP).

Transformers use self-attention mechanisms to process input data non-sequentially, capturing long-range dependencies and relationships within the data. This allows them to generate coherent text and improve translation accuracy, as seen in models like GPT-3 and BERT. The transformer architecture has revolutionized NLP by providing more accurate and contextually aware models compared to earlier architectures like recurrent neural networks.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) consist of two neural networks—the generator and the discriminator—that work in opposition to create realistic data. GANs are essential in generative AI for tasks such as image and video synthesis, where they generate high-quality, realistic outputs.

In machine learning, GANs are used for data augmentation and anomaly detection, enhancing model robustness by generating synthetic data to balance training datasets. This dual-use makes GANs a versatile tool in both creative and analytical domains​​.

Neural Networks

Neural networks are fundamental to both generative AI and machine learning, serving as the backbone for many AI models. In machine learning, neural networks are used for tasks such as image recognition, speech processing, and predictive analytics.

Deep learning, a subset of neural networks with multiple layers, is particularly effective in handling complex data and extracting high-level features. In generative AI, neural networks are used to create new content, from generating realistic images with GANs to producing coherent text with transformers. The layered structure of neural networks allows them to process extensive data and perform complex tasks with high accuracy.

Ethical Considerations

It’s true that generative AI and machine learning have brought significant advancements across various fields. However, they also raise critical ethical issues that can’t be ignored if we want to have responsible development and deployment. Let’s go over some of ML and generative AI’s ethical issues.

Bias and Fairness

Both generative AI and machine learning models can inherit and perpetuate the biases in their training data. This often yields discriminatory outcomes, such as facial recognition systems failing to recognize individuals of certain races.. To address these biases, we need diverse training datasets and ongoing monitoring to ensure AI applications are fair.

Data Privacy

Generative AI and machine learning systems need large amounts of data to function effectively. This raises significant concerns about data privacy and security. For instance, if sensitive personal information is used to train these models, there is a risk of data breaches and misuse. AI companies must implement robust data governance policies and comply with data protection regulations to safeguard user information​​.

Misinformation and Deepfakes

Generative AI has the potential to create highly realistic fake content, such as deepfake videos and fabricated news articles. This leads to misinformation, which breeds distrust, as it becomes increasingly difficult to distinguish between real and fake content. To mitigate these risks, ethical guidelines and verification mechanisms should be set up to ensure the responsible use of generative AI technologies.

Intellectual Property and Ownership

The outputs of generative AI, such as text, images, and music, raise questions about intellectual property rights and ownership. Since these models are often trained on existing works, there’s a risk of infringing on the intellectual property of original creators. The most direct solution to this would be to institute clear policies and frameworks to address these issues and protect the rights of content creators​​.

Explainability and Transparency

Machine learning models, particularly deep learning models, can be complex and opaque. It can be challenging to understand how they arrive at specific decisions, which can hinder trust and accountability. Implementing explainable AI techniques can help demystify these models, providing insights into their decision-making processes and enhancing transparency​​.

Ethical Use in Sensitive Applications

Both generative AI and machine learning are increasingly used in sensitive areas such as healthcare, finance, and legal systems. The challenge is ensuring they are used ethically; these areas have no margin for error as mistakes can lead to harmful outcomes. For example, inaccurate AI-driven medical diagnoses or biased financial assessments can have severe consequences. Rigorous testing, validation, and ethical oversight are necessary to ensure the safe and fair deployment of AI in such fields​.

Future Trends

With their track record of constant disruption, it can be tough to predict where AI and ML will go next. Nonetheless, based on current patterns, we can expect to see more adoption of both technologies, greater transparency of models, and more multimodal implementations, among other trends.

Generative AI Trends

• Hyper-personalization: Generative AI is increasingly being used to create personalized experiences for individual preferences and we can expect to see more of such. This includes personalized product recommendations, custom marketing content, and individualized user experiences in applications like e-commerce and digital media​​.
• Conversational AI: Conversational agents and virtual assistants are getting more life-like with the advancements of generative AI. These AI-driven systems can now understand and generate human-like responses, making customer service interactions more natural and efficient​​.
• Multimodal learning: Generative AI models are evolving to handle multiple forms of input, such as text, images, and audio for more versatile and engaging interactions. This will enhance the effectiveness of AI in various applications, including virtual assistants and customer service bots that can see, listen, and respond to users in a more human-like manner.
• Wearable AI devices: The development of wearables powered by generative AI is expected to grow, though many will face challenges related to privacy, security, and user experience. Successful iterations of these devices could revolutionize personal computing and interaction with technology.
• Retrieval-augmented generation (RAG): This technique, which combines search capabilities with large language models (LLMs), is maturing and is expected to become mainstream. RAG reduces hallucinations and enables users to verify answers through citations embedded in the model’s responses.

Machine Learning Trends

• Edge AI: The deployment of more effective and efficient ML models on edge devices like smartphones and IoT devices is becoming more prevalent. This enables better real-time data processing and decision-making at the source of data generation.
• Automated machine learning (AutoML): AutoML platforms are making it easier for non-experts to develop ML models by automating the end-to-end process of model selection, training, and deployment. This democratizes access to powerful ML tools and accelerates the adoption of AI technologies across different sectors​.
• Explainable AI (XAI): As AI systems become more integrated into critical decision-making processes, there is a growing demand for models that can provide transparent and interpretable outputs. Explainable AI techniques should evolve to make the decision-making process of ML models more understandable to humans.
• Customized local models: There is a growing trend towards developing custom AI models using open-source tools and proprietary data. This allows organizations to create highly specialized AI solutions based on their needs, which can be deployed on local infrastructure to maintain data privacy and reduce dependency on external providers​​.

Training Courses To Consider

As generative AI and machine learning continue to evolve, staying updated with the latest knowledge and skills is crucial for anyone looking to advance in these fields. Should you be seeking to understand these technologies at a still deeper level, here are three courses from Coursera that provide in-depth guidance.

Generative AI for Everyone by DeepLearning.AI

This course, taught by Andrew Ng, provides a complete introduction to generative AI. It covers the basics of how generative AI works, its applications, and its potential impact on various industries. The course includes practical exercises to help you apply generative AI concepts in real-world scenarios; it’s a good fit for beginners and professionals looking to enhance their understanding of generative AI​.

Introduction to Generative AI by Google Cloud

Offered by Google Cloud, this course focuses on the fundamentals of generative AI, which include its model types, its working and its applications, and sets you up for follow up courses like the more specialized Introduction to Generative AI Learning Path. It suits those looking to understand the basics of generative AI and explore its applications using Google Cloud tools like Vertex AI​​.

Generative AI with Large Language Models 

This course delves into the use of large language models (LLMs) for generative AI. It covers the working of generative AI, insights from AWS experts who build and deploy these models, as well as the latest research on generative AI​.

Bottom Line: Generative AI and Machine Learning Are Different Yet Closely Related

Comparing generative AI vs. machine learning shows that while both technologies use advanced algorithms and vast datasets, their applications and outcomes are substantially different. Machine learning excels in data analysis, identifying patterns, and making predictions, which are critical for optimizing operations and decision-making in industries like finance, healthcare, and retail.

In contrast, generative AI focuses on creating new and original content, making it an asset for content creation, entertainment, and personalized user experiences​. As different as they may be, they are in fact closely related: both technologies are a subset of AI, and will continue to work together. As they continue to evolve, their convergence promises to unlock new possibilities.

To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps

The post Generative AI vs. Machine Learning: Key Differences and Use Cases appeared first on eWEEK.

Certainly some industry observers see these two technologies as an unusual marriage. Business intelligence has a long legacy as a steady platform to rely on for critical data analysis. Major financial decisions are made based on the insights derived from BI applications.

By comparison, generative AI is brand new emerging technology. It offers vast potential – creative output based on mere text prompts – yet sometimes slips into hallucinations. Is this new player a good fit for the starchy world of business intelligence?

Indeed, Phong Le said, the combination of generative AI and BI is nascent. In contrast, “MicroStrategy has been doing business intelligence for over 30 years. We invented the sector as you know it, and there haven’t been that many major innovations in the space in the last 10 years.

“Generative AI has, in my opinion, breathed new life into the BI space and redefined what’s important.” Generative AI’s ability to answer in natural language is useful in both consumer and business contexts, he said. “When you get into the core of what businesses need, they need the answers to numerical data.”

“What I want to know when I ask a question to anyone, an analyst or eventually a gen AI bot…I don’t want a predictive answer – I want the answer. And that’s what gen AI plus BI starts to do – and it solves a problem that isn’t solved today.”

Watch the full interview or jump to select interview highlights below.

Interview Highlights: Phong Le on How Generative AI Improves Business Intelligence

These interview highlights have been edited for length and clarity. 

Adding generative AI is “the biggest change, the biggest additive change to business intelligence that we’ve seen in decades. And look, gen AI has done that for every sector of software, but BI especially. So I am really excited about what the future portends.”

Over the past decade or so, companies have worked to distribute access to BI insights out to the “edge” workers in the corporation, the retail managers, the sales people on the go.

“That came in the form of dashboards and applications,” Le said. “And what people have found is the further you go away from the corporate core, the less likely people are to be comfortable consuming that information in a traditional dashboard or a grid report.”

In other words, a finance person in corporate is comfortable using a BI program, while a retail store manager typically isn’t.

“The store manager is going to use instinct rather than consume the data in a grid report. So what we tried to do over the last three years is [figure out]: how do we get the edge workers to use BI more and more?

“They’re not really reading their emails. So let’s give it to them in the web where they can self-serve. Well, they don’t really go to the web, so let’s give it to them in a mobile device. Now we’re moving somewhere.

“What gen AI does, for the retail store worker, rather than all of these consumption paradigms that they don’t use now, it just lets them ask a question. Natural language: what’s my inventory at this particular point?

“So the employee at the front line can now use BI and data to make decisions. And I think gen AI solves that last mile problem.”

To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps

The post MicroStrategy CEO Phong Le on Generative AI and Business Intelligence appeared first on eWEEK.

Integrating generative AI into marketing personalization has supercharged this field by enabling real-time data processing that was previously impossible. Generative AI personalization leverages advanced algorithms to analyze vast amounts of data, predict consumer behavior, and automate personalized content creation. At its best, generative AI gives businesses the tools to cater to customers at an unprecedented scale.

According to a report by McKinsey, generative AI stands to create roughly $4.4 trillion in annual global revenue, with the combination of sales and marketing set to capture up to 75 percent of this value. These statistics indicate that generative AI is completely reshaping marketing.

How Generative AI Enables Personalization in Marketing

Clearly, generative AI is fast becoming a cornerstone of modern marketing strategies. It enhances the marketing landscape by offering brands the tools to create deeply personalized content at scale. Some of the ways in which generative AI facilitates personalization include:

• Customized content creation: Generative AI can analyze consumer data and generate content that resonates personally with each recipient. This extends from crafting tailored email messages to developing unique blog posts that speak directly to an individual’s preferences and behaviors.
• Dynamic product recommendations: By leveraging predictive analytics and machine learning, generative AI can suggest products that are in line with the user’s past behavior, established needs, and potential preferences, resulting in better customer experience and increased likelihood of sales.
• Optimization of incentives: Generative AI enables marketers to optimize offers, discounts, and incentives in real time based on the customer’s engagement level and purchasing history. This ensures that promotions are timely and relevant.
• Enhanced customer insights: Through the continuous analysis of customer interactions, generative AI provides actionable insights that help marketers refine their strategies and personalize further communications.
• Automation of personalized marketing campaigns: This technology automates the segmentation and targeting process, which yields more efficient campaign management and a focus on creative strategy and customer service.

6 Benefits of Generative AI in Personalized Marketing 

Generative AI is is capable of performing both routine and complex tasks. It offers several key advantages in personalized marketing, including greater efficiency, better customer experience, deep insights, real-time adaptation, and a 360-degree view of customer interactions.

Enhanced Personalization

Generative AI far surpasses the artificial intelligence (AI) personalization marketing techniques that preceded it. It leverages customer data to generate content adapted not just to segments but to individual preferences and behaviors. The power of generative AI in personalization is visible in many forms of communication, from emails to social media posts. This makes every message more relevant to its recipient.

Increased Operational Efficiency

By automating the creation and personalization of marketing content, marketing teams are free to focus on strategy and creativity. Such automation also speeds up the production process, as capabilities like real-time marketing responses that adapt to the latest consumer behaviors take center stage.

Improved Customer Experience

Using generative AI in marketing enhances the customer journey by providing a seamless and highly personalized experience. Every touchpoint is optimized for engagement and satisfaction, from initial contact to post-purchase interactions.

Better Data-Driven Insights

We’ve established that generative AI can not only process but also analyze vast quantities of data. The biggest win for marketers based on this capability is the deep insights into consumer behavior and preferences. As a marketer, with these insights, you can drive more informed decisions, from product development to cross-selling strategies.

Faster Real-Time Adaptability

Generative AI excels in adapting marketing strategies in real-time. It enables real-time responses to changes in customer behavior and market conditions and allows for the dynamic adjustment of campaigns and personalization of customer interactions.

For example, if a product garners significant interest on social media, generative AI can instantly modify campaign parameters to optimize engagement and increase visibility. Additionally, it can be used to provide predictive recommendations and personalized follow-ups based on a customer’s recent activities and historical data.

Clearer View of Customer Interactions

By integrating data from sources like social media, customer service interactions, purchase history and online behavior, generative AI offers marketers a fine-grained view of customer interactions. As a marketer, it becomes much easier to understand and manage customer relationships, deliver consistent and personalized messaging, anticipate customer needs, and optimize engagement strategies in real time.

The result is more effective offerings by businesses, more accurate predictions of the future, and ultimately higher customer satisfaction and loyalty.

To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps

Generative AI Personalization Use Cases

The uses case for generative AI in marketing personalization are numerous and growing, but a few current stand-outs include email and SMS marketing, social media and chatbots.

Email Marketing

Previous attempts at automating email marketing were hit or miss. Fully AI-created emails often felt rigid, generic or relatively dull. On the other hand, not automating email marketing meant more work for marketing teams – it’s very labor intensive to create messaging that will appeal to the many sub-groups of customers.

That’s where generative AI comes in. Generative AI driven personalization can personalize content for each subscriber based on their behavior, purchase history, and engagement with previous emails. Marketers can craft emails that connect with prospective customers on a personal level and increase open rates and click-throughs.

SMS Marketing

Similar to the approach for email marketing, in SMS marketing, generative AI personalization automates personalized messages that are not only contextually relevant but also timely.

By analyzing user data, AI can determine the optimal time to send messages that are most likely to be read and acted upon. Customer engagement improves by ensuring that promotions, updates, and alerts are received when users are most receptive, thereby maximizing the impact of every SMS sent.

Social Media

Generative AI revolutionizes social media marketing by not only automating content creation but also personalizing it for diverse audience segments. AI social media tools can rapidly produce textual and visual content tailored for niche customer groups, maintain a consistent brand voice across posts, and dynamically adjust strategies based on real-time audience data analysis.

This AI-driven personalization improves engagement by optimizing post timings and personalizing interactions, making sure that communications are timely and relevant. The outcome: higher engagement rates, more effective brand communication, and an overall increase in follower loyalty.

Chatbots

Arguably one of the most revolutionary use cases of personalization AI, AI-driven chatbots provide instant, personalized responses to user queries. These chatbots can adapt conversations based on the user’s history and preferences, offer tailored recommendations, and seamlessly escalate complex issues to human agents.

Additionally, they are increasingly capable of mimicking humans, meaning that conversations with chatbots powered by generative AI can form a connection with end users more easily. This enhances customer satisfaction while freeing human agents to focus on what the chatbots cannot, allowing managers to optimize the allocation of customer service resources.

Product Recommendations

Personalization AI is great outlet for product recommendations, and generative AI can programmed to deliver personalized product recommendations at scale. By leveraging AI’s ability to analyze a user’s past behavior and similar customer profiles, generative AI can personalize predictions and suggestions for products that are likely to interest any specific user. This not only drives up sales but also enhances the user experience by making shopping feel tailor-made to the customer.

Video Messages

Marketing is all about creating a connection with prospective customers, and one of the most effective ways to achieve this is through video content. With just a text prompt, generative AI can create personalized video content. This approach enhances customer engagement by delivering unique and compelling video messages that are directly relevant to each viewer based on their previous interactions and preferences.

Voice and Audio Content Generation

Using generative AI, brands can produce voice-overs for promotional videos or automated customer service responses that are becoming increasingly indistinguishable from human speech. This technology can adapt tone, style, and even accents to fit the target audience. It also reduces the costs previously associated with generating engaging audio and voice content.

8 Steps to Implement AI in Marketing Personalization

Given the many advantages of AI marketing personalization, marketing teams can benefit from creating a clear plan to deploy this technology. Here are eight steps you should consider when implementing AI in personalizing your marketing:

1. Assess Your Current Marketing Processes

Start by evaluating your current marketing strategies and processes. Understand what is working and identify areas that could benefit from increased automation and personalization. This step will help pinpoint the specific functions where AI can deliver the most impact, such as customer segmentation, content personalization, or behavioral targeting.

For generative AI, you can examine tasks that involve content creation, customer interaction, or data analysis where the unique capabilities of generative AI, such as generating text or images, can significantly enhance output quality and engagement.

2. Set Clear Goals and Objectives

Define what you seek to achieve with AI-driven marketing personalization. Objectives might include increasing customer engagement, improving conversion rates, or enhancing customer retention. Setting clear, measurable goals will guide your AI implementation strategy and help quantify its success.

3. Collect and Organize Data

AI thrives on data. Gather and consolidate data from all available sources—customer interactions, transaction history, social media activities, and more. Ensure your data is clean, organized, and accessible. High-quality, comprehensive data is critical for training AI models to deliver accurate and effective personalization.

4. Choose the Right AI Technology

Select generative AI tools and technologies that are geared for your marketing needs. This might involve predictive analytics tools, machine learning platforms, or AI-powered content management systems. Consider factors like scalability, integration capabilities, and ease of use. This applies to generative AI as well, as you should have generative AI tools that best fit your marketing personalization needs in mind. This might include those tools capable of creating personalized promotional content, generating product descriptions, or automating responses in chatbots.

Consider tools that integrate easily with your existing technology stack and can scale with your business, as well as partnering with top AI companies that offer generative AI tools that would be an appropriate fit for your company.

5. Integrate AI With Your Marketing Platforms

Seamlessly integrate AI tools into your existing marketing platforms, like email marketing software, AI CRM systems, and content management systems. This integration ensures that you embed AI-driven insights and personalization capabilities throughout your marketing processes.

6. Train Your Team

This cannot be overstated. Equip your marketing team with the knowledge and skills to utilize AI tools safely and effectively, especially in this age of generative AI. This might involve training sessions, workshops, or collaborating with AI specialists. An AI-savvy team can better leverage AI capabilities to refine marketing strategies and create more engaging customer experiences.

7. Monitor, Measure and Optimize Performance

Once AI is integrated into your marketing processes, continuously monitor its performance against your set objectives. Use AI-driven analytics to measure effectiveness and gather insights that can further refine and optimize your marketing efforts. This ongoing process will help keep your marketing strategies relevant and highly personalized to your audience.

8. Stay Updated on AI Advancements

AI technology evolves rapidly. Keep your marketing strategies ahead of the curve by being in the know concerning the latest AI developments and emerging trends. Regularly update your AI tools and strategies to ensure they can leverage emerging capabilities and enhance your marketing personalization efforts.

5 Tools for Personalized Marketing

Jasper AI

Jasper AI is AI software that stands out as an end-to-end marketing copilot, especially suited for content creation and ideation. Its advanced generative capabilities allow marketers to quickly produce high-quality, engaging content that resonates with targeted audiences. By automating the content generation process, Jasper AI enables teams to focus more on strategy and less on the mechanics of content production, enhancing both efficiency and creativity in marketing campaigns.

Grammarly

Grammarly uses AI to offer advanced writing assistance, making it an essential AI writing tool for marketing teams looking to perfect their messaging. It provides suggestions on grammar, tone, and style to ensure content is not only correct but also compelling and tailored to the audience. Grammarly’s generative AI features can also assist in rewriting or expanding on existing content, ensuring all communications are optimized for engagement and clarity.

HubSpot

HubSpot integrates AI across its marketing, sales, and service hubs to streamline interactions and enhance customer relationships. Its AI features include automating repetitive tasks such as email responses and data entry, allowing teams to focus on more strategic activities. HubSpot’s AI also provides valuable insights into customer behavior that help tailor marketing efforts more effectively and predict future trends.

Brand24

Brand24 employs AI to offer comprehensive media monitoring and analytics, giving marketers the tools they need to track brand mentions and customer sentiment across various channels in real-time. This immediate insight allows brands to react swiftly to customer feedback and market changes, ensuring marketing strategies remain relevant and proactive.

AdCreative AI

AdCreative AI uses AI to generate high-converting ad creatives. It provides tools for optimizing visual elements and copy, creating advertisements that are not only aesthetically pleasing but also highly effective at engaging target audiences. This tool is particularly valuable for teams looking to maximize the ROI of their advertising efforts through data-driven creative strategies.

6 Best Practices for Integrating Generative AI into Marketing Strategies

Foster Creative Ideation with AI

Utilize generative AI as a tool to enhance creativity and ideation within your marketing team. Generative AI can help brainstorm and visualize solutions to complex marketing challenges, generating innovative ideas for content, campaigns, and customer interactions. Encourage your team to use AI not just as a task automator but as a partner in creative processes.

Boost Ethical & Strategic AI Integration

Adopt a strategic approach to integrating generative AI that considers generative AI ethical implications, particularly around data usage and customer privacy. You should develop guidelines that ensure AI applications respect user privacy and are compliant with relevant regulations. It should be your priority to use AI to enhance customer experiences without compromising on ethical standards.

Balance AI Automation with Human Oversight

While generative AI can automate many aspects of marketing, it should be balanced with human oversight. Ensure that AI-generated content and strategies are reviewed and refined by human marketers to make sure content is accurate, relevant, and has that human touch. With this, you’ll prevent over-reliance on AI and keep your brand voice consistent.

Customize AI Tools to Fit Brand-Specific Needs

Select and customize generative AI tools to specifically suit your brand’s needs and marketing goals. Set up AI functionalities to enhance your existing marketing strategies, as they should complement and amplify your brand’s strengths rather than replace the human elements that define your brand’s voice.

Monitor AI Impact and Adapt Strategies

Implement continuous monitoring mechanisms to track the effectiveness of generative AI in your marketing efforts. Use insights gained from AI analytics to adapt and refine strategies in real-time to keep that alignment with overall business objectives and market dynamics.

Invest in Ongoing AI Education and Training

Invest in training programs to keep your marketing team on par with the latest generative AI developments and best practices. This will empower them to seamlessly and effectively use AI tools and integrate new AI capabilities into your marketing strategy as they become available.

Ethical Considerations for Generative AI in Marketing

Integrating generative AI into marketing strategies brings up several ethical considerations and privacy concerns that need to be handled with the utmost care.

Data Privacy and User Consent

AI thrives on data. The use of generative AI in marketing often involves large amounts of personal data to train generative AI models. It is crucial to ensure that this data is collected, stored, and processed with the highest standards of privacy and security. Always obtain explicit consent from users before collecting their data, and make sure to comply with data protection regulations such as GDPR and CCPA. Transparently communicate what data is being collected and how it will be used to build trust with your audience.

Bias and Discrimination

The outputs of AI systems, including generative AI, can unintentionally amplify biases present in their training data. The outcome can be unfair or discriminatory marketing practices, such as targeting or excluding specific demographics. To combat this, continuously monitor and audit AI models for bias and take corrective measures to ensure fairness. Implementing diverse datasets and employing techniques for bias detection and mitigation are essential steps in this process.

Misinformation and the Authenticity of Content

One of the greatest challenges that have come to the fore due to the generative AI boom is misinformation. Generative AI is capable of creating realistic and persuasive content, which can be misused to spread misinformation. It can also generate persuasive content that’s realistic but not factual.

Marketing teams must ensure that the content generated by AI aligns with factual accuracy and ethical advertising standards. Having rigorous review processes and maintaining human oversight are crucial to preventing the dissemination of misleading information.

Intellectual Property Concerns

The use of generative AI in creating content can raise questions about copyright and intellectual property rights. Cases where generative AI is trained on data that’s copyrighted aren’t scarce. It’s important for marketing teams to understand the legal implications of using AI-generated content, especially regarding ownership and the use of copyrighted materials in training AI models. You can always consult legal experts to navigate these complexities and ensure you comply with copyright laws.

Transparency and Explainability

There is a growing demand for transparency in how AI systems make decisions, especially when those decisions affect consumers directly. Marketing teams should strive for transparency by making it clear when and how AI is being used in their strategies. Additionally, being able to explain the decisions made by AI systems can help build trust and credibility with both customers and regulators.

Bottom Line: Generative AI Personalization Boosts Marketing Effectiveness 

Generative AI personalization is making waves in marketing by enabling brands to deliver highly customized and engaging content at scale. It not only enhances the efficiency of marketing campaigns but also significantly improves the accuracy and relevance of customer interactions.

Follow the steps to implement AI in your marketing workflows and pay attention to the ethical considerations and issues we’ve explored to make your marketing personalization more effective and offer unique experiences to drive both engagement and customer satisfaction.

For more information about generative AI providers, read our in-depth guide: Generative AI Companies: Top 20 Leaders

The post How Generative AI Personalization Transforms Marketing appeared first on eWEEK.

Sectigo issues certificates that enable customers to encrypt traffic between websites and users, among other purposes. “So we not only issued 255 million certificates last year, we moved into the certificate lifecycle management space where we help people automate certificates,” Weiss said.

The company’s automation service includes managing SSL certificates issued by both Sectigo and other certificate vendors, on both the public and private side of the market, ranging from web sites to servers to enterprise workloads.

“When you think about all the servers and hardware that are out there, there are probably 10 times as many machines as there are people. And then when you think about workloads, it’s infinitely greater than the number of machines. So the proliferation of certificates and the need to encrypt traffic and transactions – inside the firewall and outside the firewall into servers – has gone up exponentially over the last five to ten years. It’s really exploded.”

Watch the full interview or jump to select interview highlights below.

Interview Highlights: Kevin Weiss on Key Trends in CLM

This interview took place at the recent RSA Conference in San Francisco. The comments below have been edited for length and clarity. 

The Need for Automation

“The certificate lifecycle management business has been around for probably 10 years,” Weiss said. The problem is that as new certifications have proliferated, certificate management hasn’t kept up. Plenty of companies are still trying to manage their certs using Excel spreadsheets.

“Good old Excel still works very, very well,” he said. “But as you get more and more certificates and as people change jobs or somebody leaves the business, the ability to access the spreadsheet and know when a certificate is going to expire becomes a real challenge. We see this every day.

“If you look at last year, for example, Starlink went offline and it was offline for maybe three or four hours. And the next day Elon [Musk] tweeted that, ‘apologies, we had an expired certificate. We’ll do better.’

“So expired certificates can be very, very problematic for making your services available. Certificates are left out there in an environment and aren’t focused on. And if a bad actor gets a hold of it and begins to compromise your environment, that’s a problem. So the need to know where all of your certificates are in an environment, both on the public facing side and inside the firewall, is critical. And that’s what is really driving the need for this automation.”

Sectigo and SCM Pro  

“What I like to say is: perfection is the enemy of the good enough. And so what Sectigo is trying to do is tackle 80 to 90 percent of what most enterprises need and then help them fill in the gaps later on. For us, the real goal is to continue to make [service] available.

“We just announced a product recently for the small end of the market, which we call SCM Pro, and it’s a certificate lifecycle management solution for small businesses. And basically we automate the entire lifecycle of that certificate. Once you sign up for the service, we’ll manage it forever for you.”

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