Why AI Should Do the Heavy Lifting and Humans Should Do the Thinking

French novelist Marcel Proust once said, “The real act of discovery consists not in finding new lands but in seeing with new eyes.”

That’s exactly what AI enables when it comes to win and loss analysis.

For years, firms knew win–loss reviews mattered. But the way they were done made them slow, incomplete, and easy to deprioritize.

How Win–Loss Reviews Used to Work

Traditionally, understanding why a deal was won or lost meant interviews. In my boutique agency days, I would chase down loss interviews, often weeks after a decision had been made. Most prospects never responded. When they did, the feedback was thin, polite, or vague. And when push came to shove, the work always fell down the priority list. A new opportunity in the pipeline felt more urgent than dissecting a deal that was already gone. That meant win–loss analysis happened inconsistently, if at all.

The Real Limitations of the Old Model

Even when interviews happened, the data set was small and biased. Only a handful of buyers participated. The analysis relied on memory and hindsight. Important signals were lost, filtered, or softened. As a result, insights were incomplete and often reinforced assumptions instead of challenging them. Of course you could have gone out to hire a third party to do this for you, but that was costly and often not much more successful.

What Changes in This Era

AI changes the process entirely. Instead of relying on interviews, firms can now analyze the entire sales motion. Email exchanges. Call transcripts. Proposals. Objections. Language used throughout the process. Every deal can be included, whether or not a buyer agrees to an interview. The machine does the collecting and analysis. Patterns, themes, and decision drivers surface quickly and consistently. What used to take weeks or months now takes hours, with a few prompts and clicks. Employees with no coding skills can build an agent to do this for them and report findings at any frequency they choose.

From Writer to Editor

This shift elevates the human in the loop. Before, someone had to chase data, compile notes, and attempt to interpret fragmented feedback. Now, AI handles the collection and analysis. The human oversees the output. The role moves from writer to editor. You review what surfaced. You decide what matters. You determine how the firm should respond.

Why This Matters

When win–loss reviews become fast and comprehensive, they become actionable. Teams can fine tune the Ideal Client Profile based on real buying behavior, not gut instinct. Sales methodology can evolve as objections and rebuttals show up in the data, not months later. Messaging improves because it reflects what buyers actually say and respond to. And because the effort is low, this can happen regularly, not once or twice a year.

The Bottom Line

AI doesn’t just make win–loss reviews easier. It makes them usable and actionable. By shifting collection and analysis to the machine and elevating humans into an editorial role, firms can finally turn win–loss reviews into a true improvement engine.

That’s what it looks like to see this process with new eyes.