In today’s rapidly evolving business landscape, the ability to accurately forecast sales is more critical than ever. Yet, traditional sales forecasting methods are increasingly proving inadequate, as they consume significant seller time and often fall short in accuracy.
According to recent Gartner research, only 7% of sales organizations achieve a forecast accuracy of 90% or higher, and a staggering 69% of sales operations leaders report that forecasting is becoming more challenging. However, advances in AI offer a promising solution, reducing the burden on sellers while significantly enhancing forecast accuracy and actionability.
The Challenges of Traditional Sales Forecasting
Sales forecasting has long been a time-consuming process that diverts sellers’ attention from engaging with customers. Despite its critical importance, traditional forecasting methods often yield limited accuracy and actionability. Gartner research indicates that the median forecast accuracy among surveyed organizations is between 70% and 79%. This lack of precision hampers decision-making and strategic planning, ultimately impacting business performance.
Moreover, the complexity of forecasting is increasing. Sales teams face an ever-growing volume of data, heightened uncertainty and more demanding stakeholders. In response, many organizations have attempted to improve data quality control, yet this approach has not translated into better forecast accuracy. In fact, organizations with rigorous quality control processes often achieve only moderate accuracy.
AI-Augmented Forecasting: A Game Change
AI-augmented sales forecasting emerges as a powerful alternative, leveraging AI to capture detailed data, make predictions and provide actionable insights. This approach not only alleviates the burden on sellers but also enhances forecast accuracy and simplifies the forecasting process.
AI can significantly improve the capture of buyer activity details, which are often lost in traditional forecasting. Two key AI capabilities — activity intelligence and conversation intelligence — play a crucial role in this transformation:
- Activity Intelligence: This capability detects buyer interactions across various systems, such as email, calendars and collaboration tools, logging them automatically without human intervention. An AI sales assistant can further identify and recommend moments to capture data, reducing the risk of human error and oversight.
- Conversation Intelligence: By extracting insights from buyer interactions, conversation intelligence converts this information into actionable guidance. Paired with GenAI, it can provide comprehensive call summaries and next steps, offering a level of detail and objectivity beyond what sellers can provide.
To successfully implement these capabilities, organizations should personalize their vision for AI-augmented data capture, audit existing processes to quantify potential benefits, explore vendor offerings and encourage sales leaders to renew their focus on data-driven leadership.
Enhancing Prediction Accuracy with AI
With AI’s ability to process vast amounts of structured and unstructured data objectively, organizations can achieve more accurate predictions than ever before. While many organizations already use AI-based predictive analytics, results have often fallen short of expectations. However, notable advancements by revenue technology vendors are pushing accuracy levels to impressive heights.
To fully leverage AI in predictive analytics and forecasting, sales teams must build trust in AI-augmented forecasts. This involves demonstrating reliability and consistency at each stage of maturity, whether transitioning from traditional reports and spreadsheets or fully delegating forecasting to AI.
Turning Predictions into Actionable Guidance with GenAI
Automated data capture and improved prediction accuracy are only beneficial if they lead to actionable insights. GenAI excels in this area. The following capabilities are capable of revolutionizing forecasting:
- Discovery: GenAI assistants can intuitively find relevant data within systems, allowing users to extract insights using natural language questions. This capability extends beyond internal data, incorporating public data for a more comprehensive analysis.
- Interpretation: GenAI identifies patterns and trends within data, democratizing analytics by enabling users to share insights without specialized skills.
- Investigation: By conducting statistical tests and simulations, GenAI acts as a data scientist, saving time and effort for human analysts.
- Storytelling: GenAI drives actionable insights by contextualizing forecast results for different roles, converting findings into strategic recommendations.
To maximize the impact of GenAI, organizations must clearly understand the decisions driven by forecast information. Defining metrics and creating prompts that lead to actionable advice, rather than merely sharing data, is essential.
AI-augmented forecasting represents a paradigm shift in sales forecasting, offering a solution to the perennial challenges of accuracy and actionability. By capturing detailed buyer activity, enhancing prediction accuracy and turning insights into actionable guidance, AI empowers sales teams to make informed decisions that drive growth and profitability. As AI technology continues to advance, its role in sales forecasting will become increasingly integral, providing businesses with the tools they need to thrive in a complex and competitive market.
Steve Rietberg is a VP Analyst and Tyler Huguley is a Senior Director, Analyst in the Gartner Sales Practice, focused on various sales enablement and strategy topics. Join Gartner analysts to learn more about sales analytics and decision-making at the 2025 CSO & Sales Leader Conference, May 20-21, in Las Vegas.