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Artificial intelligence sales forecasting is an advanced approach to sales forecasting and data analytics that uses machine learning algorithms, multichannel datasets, and other high-powered compute resources to deliver more comprehensive sales forecast insights.
Sales managers often use AI-powered sales forecasting to supplement their existing sales forecasting practices with more real-time and far-reaching data, risk management profiles, and recommendations for achieving better results.
When combined with sales team experts and the strategic use of CRM or other sales tools, AI sales forecasting opens up new opportunities for businesses to act on their sales data in future scenarios. In this guide, learn more about AI sales forecasting, how it works, how you can get started with this strategy, and what leading tools you might use.
TABLE OF CONTENTS
Understanding AI in Sales Forecasting
AI models that are trained with advanced machine learning algorithms and large sales-specific datasets can provide highly intelligent supplements to marketing and sales forecasts and analytics.
Depending on available data sources and channels, artificial intelligence can identify and incorporate useful finance, chatbot, social media, and customer support conversation data into its analyses to make well-rounded predictions about future revenue, markets, and more.
AI forecasting tools are typically set up to run behind the scenes at all times, collecting new real-time data and updating existing data as conditions change. With this information, sales teams can make well-informed decisions at any point in their sales cycle and for a variety of data points.
To gain a deeper understanding of today’s AI software for sales, read our guide to AI Sales Tools and Software
AI Sales Forecasting vs. Traditional Sales Forecasting
In traditional sales forecasting, past performance data and metrics, various statistical models, and other business intelligence (BI) best practices are leveraged by sales professionals to predict how these same metrics will look in the future.
For example, traditional forecasting may predict a slump in Q2’s sales because the past three years have seen a similar dip. In contrast, AI forecasting takes these existing practices and moves beyond traditional forecasting’s capabilities with large AI/ML algorithms and models, as well as massive large language models.
With the algorithmic training that comes with AI forecasting, AI forecasting tools and solutions can take a more holistic look at all business sales data — including data that is not typically viewed as performance data — and make accurate predictions and recommendations for future outcomes.
For example, an advanced AI forecasting tool may be able to identify negative sentiments in chatbot messages and social media comments that focus on sizing for a particular clothing line; from there, the chatbot may predict a decrease in sales or an increase in returns for this clothing line unless appropriate adjustments are made.
For more information about how these two types of sales forecasting stack up, take a look at our pros and cons comparison:
| Pros | Cons | |
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| Traditional Sales Forecasting |
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| AI Sales Forecasting |
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To learn about AI for customer relationship management, read our guide: Top 8 AI CRM Software
AI Sales Forecasting Use Cases
Although sales forecasting can be used in highly specific scenarios designed for your business’s sales goals, most AI sales forecasting projects fall into the following use case categories:
- Financial data processing and analysis: Reviewing financial data like revenue, budget spent on past marketing campaigns, cost of deals closed and lost, and more to predict future sales trends.
- Chatbot customer service: Real-time customer feedback and sentiment analysis used to identify fluctuations in product demand, customer acquisition, and customer satisfaction.
- Lead generation and performance scoring: Highly specific analysis and scoring of customers in general and as individuals, looking at different details of their buyer persona, demographics, browsing and purchase history, and social media interactions to determine if they are a worthwhile lead to pursue.
- Customer sentiment analysis: Reviewing the emotions, interactions, and overall behaviors of customers and prospective buyers across different channels, including social media, chatbots, call center transcripts, and customer reviews to determine if product shifts or redesigns are necessary.
- Predictive and prescriptive analytics: Beyond simply predicting performance outcomes based on past data and incoming data, AI sales forecasting tools can often offer recommendations for next steps based on these predictive analytics, whether that’s a possible shift in marketing strategies or an update to a specific webpage.
- Opportunity pipeline and lifecycle management: Instead of looking simply at individual deals and customers, AI forecasting tools can take a look at the sales pipeline as a whole to identify patterns in deal progression and slowdowns, which can help the team make adjustments for the future.
- Risk management: Many AI forecasting tools can look at past events and external data sources — including competitors’ web properties — to identify potential business risks and contingency plans to mitigate these risks.
- Rep-specific performance management and goal-setting: AI can get into the specifics of individual team members’ performance across different metrics, time periods, and channels. Looking not just at straightforward numbers, AI forecasting tools may be able to use sentiment analysis, sales pipeline health, and other murkier or subjective datasets to identify top players and salespeople who could use more training or support.
To learn more about how AI is used in business settings, see our guide: 15 Generative AI Enterprise Use Cases
5 Ways AI Improves Sales Forecasting
With AI as an assistive resource in sales forecasting, businesses can derive new, deeper, and more useful insights at scale. These are some of the benefits that businesses realize almost immediately after incorporating AI into their sales forecasting practices:
- Deeper, more nuanced insights from existing data sources.
- Real-time, accurate updates to predictive analytics and dashboards.
- Multichannel and external data analysis, including on social media.
- Enhanced customer experience focus, with deep reviews of customer interactions and demographics.
- Intelligent performance improvement recommendations, specifically with a shift from predictive analytics to prescriptive analytics and AI-powered recommendations for improvements.
10 Steps to Implement AI Sales Forecasting Solutions
Whether you’re investing in a third-party tool or building your own generative AI model to support sales forecasting work, you’ll need to follow these steps or similar ones to develop an operational and useful tool:
1. Organize and Prepare Data
As with any AI or algorithmic solution, the outcomes are only as good as the training and input data that goes in.
If your organization’s sales data is erroneous, biased, poorly formatted, or incomplete, these problems can severely limit the accuracy and utility of any AI sales forecasts you make. That’s why it’s important to start AI sales forecasting adoption by preparing your data to meet a high data quality standard.
This work may involve cleansing existing data, sourcing new data, finding new data sources, or having multiple team members assess data for biases that may previously have gone unnoticed. This step should take a thorough look at data quality from all angles to achieve the best outcomes.
2. Set Goals and Budget
It will be easiest to measure the success of your AI sales forecasts if you go into this work knowing exactly what you want to measure, how you want to measure it, and how frequently you want to measure it. Specific metrics ensure that AI sales forecasts are focused on the right goals while also helping your team more closely judge the accuracy of the tool’s outputs.
Once measurable, big-picture goals are set, you’ll want to consider what budget is available for AI sales forecasting tools. This will help you decide if it’s time to invest in an entirely new sales platform, work with your existing vendor to embed or integrate AI capabilities, or build or fine-tune your own models for specific use cases.
3. Research AI and Sales Tools
A variety of sales platforms, AI tools, and integrated AI sales solutions are on the market today.
Based on the goals and budget you’ve set as well as your existing tool stack and other preferences, you’ll be able to make an informed decision about the best AI forecasting tool for your business. Most businesses get top value out of investing in an all-in-one sales platform that includes built-in AI capabilities, or even an AI assistant or copilot.
4. Complete Demos or Free Trials
The previous research step should be supported with hands-on testing of your top tool choices. Whether that’s through a customized demo or a free trial, multiple members of your team should test out the tool and its different capabilities.
During this phase, it’s a good idea to not only get familiar with the actual platform but also any community forums, knowledge bases, training, and customer support resources that may be helpful down the road.
5. Adopt or Build a Modifiable Solution
The prep work is done and it’s time to officially implement and customize an AI forecasting solution to your specific needs. Regardless of whether you’ve selected a prebuilt tool or are creating your own sales forecasting AI model, you’ll want to prioritize building a solution that can easily pivot or scale as your teams’ expectations change over time.
6. Start With a Pilot Program
Theoretically, your new AI forecasting sales tool could replace all existing forecast and predictive analytics work, but the problem with this approach is that you won’t be able to easily roll it back if something goes wrong. Instead, start by adopting AI forecasting on a smaller scale for a specific project or use case.
For example, if you work in an e-commerce business, you may want to test the tool first for a specific product’s predicted revenue or a specific sales rep’s expected deal closings for the next two quarters. Focusing a pilot program at a more granular level makes it possible to pinpoint when and where something goes wrong. From there, your team can more effectively make adjustments that will make widespread AI forecasting adoption run more smoothly.
7. Get Buy-In From and Provide Training to Employees
While important stakeholders across teams should already have been involved in the decision-making process, it’s likely that many sales team members are just now learning about this new tool in their stack. Take the time to clearly explain what an AI sales forecasting solution can do, how it can supplement their existing work, and how it can free up their time for the more strategic and interesting work of sales and customer experience management.
From there, take the time to train each team member, going over more general capabilities and then providing role-based training on an individual or team basis. If you take the time to fully communicate and prepare your teams to use these tools well, you’ll not only develop more consistent forecasts but also be working with a team that is more confident in how these tools can support rather than replace them.
8. Develop Monitoring and Review Processes
AI forecasting tools may be prone to error, especially when you’re first getting started. There’s also the possibility of human input errors, data issues, security issues, or customer experience shortcomings. In all of these cases, having a comprehensive performance monitoring and review process will enable you to more quickly identify and address these problems.
For model or AI-specific performance management, human-in-the-loop review workflows are a great way to ensure that each model iteration is closely reviewed and approved by a human expert.
9. Embed or Integrate with Existing Sales Tools
If the AI forecasting tool you’ve selected is not a built-in component of your sales platform or CRM, you’ll likely want to more closely connect it to that platform for smoother workflows and efficiencies. Ideally, your earlier research has already informed you about whether your sales tools natively integrate with your AI forecasting tool.
10. Update Systems and Datasets as Necessary
As your business grows and your sales goals change, you’ll want to update the AI forecasting model’s parameters, data, and other variables to help it keep up with these shifts.
You’ll also want to adjust your tool or even consider moving to a new one if your real-time monitoring results show problems that need to be fixed. Making these updates periodically and regularly will ensure you are handed credible analytical results on a consistent basis.
To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps 2024
3 Sales Forecasting Tools to Consider
AI sales forecasting tools may be standalone solutions or features built into existing sales platforms. These are some of the leading AI sales and forecasting solutions to consider today:
Salesforce
Salesforce is a leading cloud software suite that includes sales and marketing CRM capabilities as well as solutions to manage customer service experiences and business analytics. With Salesforce Einstein’s Sales AI, users can benefit from AI-powered predictive forecasting and other unique features like deal insights, call insights, a buyer assistant, and relationship graphs and insights.
Pipedrive
Pipedrive is a top CRM software provider that has recently added AI capabilities and features to its platform. Most significant to sales forecasting, the AI Sales Assistant is designed to support revenue forecasting with helpful reminders, suggestions, notifications, and updates to keep your team on track and aware of subtler opportunities.
Zoho CRM
Zoho CRM is a favorite CRM for business users of all backgrounds because of its focus on user experience. Zia, an AI companion, is a recent addition to the tool that helps users manage and optimize the data in their CRM. Some of Zia’s capabilities include conversion predictions, anomaly detection, intelligent automation, competitor alerts, and smart recommendations
7 Best Practices of AI Sales Forecasting
Similar to traditional sales forecasting processes, it’s important to approach your AI sales strategy with strong data management strategies and clear goals in mind. Here are some of the most important best practices for getting started and achieving success with AI sales forecasting:
- Don’t overlook data preparation: Prepare and maintain high-quality, well-rounded, and unbiased data, both for training and inputs.
- Blend AI with existing strategies and strengths: Strategically blend AI with traditional sales forecasting when the situation calls for it; don’t lose the strengths and unique perspectives that your sales experts can provide from years of experience.
- Follow data and AI ethics best practices: Source and use data ethically, especially when operating in industries or regions with strict compliance requirements.
- Focus on employee experience alongside customer experience and sales outcomes: Train team members on how to leverage new insights to make their jobs easier.
- Constantly monitor AI forecasting performance: A real-time monitoring strategy will help your team identify anomalies, performance aberrations, and emerging trends.
- Focus on outcomes and next steps: Don’t let the shine and excitement of a new piece of technology distract you from the goals it should help you achieve in the sales cycle.
- Improve and iterate on AI forecasting models and methods over time: Start small to make it easier to see what does and doesn’t work in short sessions of analysis.
Bottom Line: AI Boosts Sales Forecasting – With Human Help
With its speed, scalability, accuracy, and ability to look at sales data in-depth and from new angles, AI sales forecasting has become a top trend in the sales technology sphere for good reason. But is it “the future” of sales forecasting, or is it nothing more than a trend of the moment?
Right now, the buzz surrounding AI sales forecasting often discounts or downplays the manual effort that makes these tools run. Without sales and data experts preparing the right data, setting relevant goals and parameters, and reviewing performance regularly, AI sales forecasting tools would provide little value to the modern business. In short, salespeople should not worry about being replaced with AI; rather, they should prepare to upskill as their roles shift — AI will certainly take on some of the more tedious work that used to fill their daily schedules.
For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide: 150+ Top AI Companies