The Best Way To Forecast Is Not To Forecast

Published on November 13, 2023 by David Zhang

The Best Way To Forecast Is Not To Forecast

Forecasting sales is like reading tea leaves—trying to divine the future from patterns that are as often as not simply chaotic. Traditional sales forecasting methods, such as looking at historical data, conducting pipeline reviews, and using intuition or "gut feelings," have long been the status quo. But in an age where data is abundant and artificial intelligence is on the rise, the notion that the best way to forecast is not to forecast is gaining ground.

In the dynamic landscape of sales, attempting to predict the future based on past events can be fraught with inaccuracies. Market conditions shift rapidly, competitors emerge and decline with little warning, and customer preferences evolve. Relying on traditional forecasting methods in such a volatile environment is like trying to navigate by the stars in broad daylight – the old methods are simply not equipped to handle the complexity of today’s market.

This article explores the limitations of traditional sales forecasting, the power of AI in sales, and the emerging philosophy that to truly excel in sales forecasting, one must adopt a system that is flexible, responsive, and powered by real-time intelligence.

The Limitations of Traditional Sales Forecasting

Traditional forecasting methods operate under a key assumption: that the future will behave like the past. But there are several limitations to this approach:

  • Past Performance May Not Indicate Future Results: It’s a common adage in investing, but it's equally applicable to sales forecasting. The future sales landscape can look nothing like the past, particularly in rapidly innovating industries.
  • Static Data Can Lead To Inaccurate Predictions: Most conventional sales forecasting methods rely on static data. At best, this data provides a snapshot of where the company has been, not where it's going.
  • Subjective Judgments Lead to Variability: When sales leaders or reps rely on their intuition to make forecasts, the results are often inconsistent. Different people will come to different conclusions based on the same set of data, which makes this method unreliable and skewed.
  • Time-Consuming Process: Traditional forecasting is time-consuming, requiring substantial effort from sales reps and managers to manually compile, organize, and analyze data, often taking away from time that could be spent selling.

Embracing a New Philosophy: Forecasting Without Forecasting

The statement "the best way to forecast is not to forecast" could be provocative, but it reflects the emerging belief that there are more effective methods at our disposal today.

The most prominent of these methods is the employment of artificial intelligence (AI). AI has the capacity to analyze vast amounts of data in real-time, spotting trends, and patterns that are imperceptible to the human eye, and making predictions based on continuously updated models.

Here’s how AI is changing the sales forecasting game:

  • Agility: AI algorithms can rapidly adapt to new information, updating their predictions on the fly. This means that the moment a new trend is detected or a significant market shift occurs, your sales forecast is automatically adjusted accordingly.
  • Objective Analysis: AI removes the subjective bias that can creep into traditional forecasting methods. By relying on data rather than intuition, AI creates forecasts based on what's happening rather than what we think or hope will happen.
  • Real-Time Insights: Unlike traditional methods that look backward, AI takes into account live data streams, providing a more accurate picture of the current sales environment.
  • Time Efficiency: AI can free up significant time for sales teams by automating the data gathering and analysis process, allowing them to invest time and energy into building relationships and closing deals.

The shift away from forecasting in the traditional sense is also a move towards what is often called "scenario planning" or "predictive analytics." This approach does not try to predict the future but rather prepares for a variety of future states, enabling companies to be nimble and responsive to whatever the market throws their way.

Implementing AI for ‘Forecasting’

To move away from forecasting as usual, companies can implement AI-driven sales tools that offer a robust set of capabilities for real-time market intelligence and actionable insights. Here’s how to get started:

  1. Incorporate AI and Machine Learning Tools: The first step is to adopt the right tools that can harness the power of AI. There are solutions available today that take the heavy lifting out of sales predictions and can offer greater accuracy and real-time adaptability.
  2. Integrate Comprehensive Data Streams: AI tools can only be as good as the data they analyze. Ensure that you are feeding these tools with comprehensive data from both internal (such as CRM data, sales interactions, and outcomes) and external sources (market trends, economic factors, etc.).
  3. Educate Your Sales Team: There’s often resistance to new technologies due to fear of the unknown, so it’s crucial to teach your team how these AI tools can be used to their—and the company’s—advantage.
  4. Continuous Monitoring and Adjustment: Use the insights from AI for ongoing refinement. AI is most effective when it is tuned continuously based on outcomes, learning, and improving over time.
  5. Combine Human Ingenuity with AI Precision: Use AI-generated insights as the starting point and combine them with the human experience to develop coaching plans, sales strategies, and customer engagement models.


In a rapidly changing sales landscape, clinging to old methods of forecasting is no longer sufficient. The best way to forecast is, paradoxically, to embrace systems that avoid traditional forecasting altogether. By leveraging the power of AI, sales teams can count on more accurate, real-time predictions and spend more time doing what they do best – selling.

Adapting to this new model requires a cultural shift and an openness to technological advances, but the upsides - increased agility, objective analysis, time efficiency, and more accurate forecasting - make it an evolution that B2B sales can no longer afford to ignore. Integrating AI into your sales strategy offers a competitive edge, and as sales intelligence platforms like Aomni become increasingly sophisticated, they are paving the way towards a future where sales forecasts are continuously evolving, highly informed, and deeply interconnected with day-to-day sales operations.

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