How to Evaluate an AI Discovery Tool

Every presales leader is hearing the same pitch right now: “Our AI will transform your discovery.”

Most of these tools will give you a cleaner meeting summary. Very few will help you win.

The same thing is happening inside many companies. Technical overlay teams are building their own AI wrappers for presales: a prompt layer on top of a general-purpose model, paid for with AI credits. The intent is good, but the result is often a faster way to produce the same summaries. What grows is the monthly AI bill, not the win rate.

The problem isn’t the model. It’s that neither approach starts from how presales actually works: the discovery framework, the opportunity context and the path to a Technical Win.

After 20 years in enterprise presales, and now building an AI platform for Sales Engineers, I’ve come to believe one test matters more than any other:

“Will this help my SEs move the opportunity closer to a Technical Win?”

Here’s how to find out.

Start with the right questions

Good discovery is about asking the right kinds of questions, not more of them. I group them into three:

• Reality questions: What is true today? The current environment, the pain points, the business drivers, what’s already been tried.
• Risk questions: What could derail this? Blockers, dependencies, competition, internal politics, gaps in technical fit.
• Decision questions: How will they decide? Decision criteria, success criteria, stakeholders, buying process, timelines.

Most SEs are strong on reality questions. Deals are usually lost on risk and decision questions, because those are the ones nobody asked.

A note-taking AI tells you what the customer said.
A presales AI tells you what you still need to find out.

The right question depends on the customer’s moment

The same question can land very differently depending on what the customer is going through.

Take an enterprise that has just reported a quarterly loss. The CFO is reviewing every line of spend, and the IT team is under pressure to justify each project. If your SE walks in asking:

❌ “What budget have you set aside for expanding your infrastructure this year?”

the conversation is over before it starts. That question sounds like spend, and spend is the last thing they want to hear about.

Reframe it around what the customer cares about right now:

✅ “Where are you seeing the highest cost of running your current environment today?” (Reality)
✅ “If a project gets paused this quarter, what operational risk does that create?” (Risk)
✅ “What payback period would leadership need to see to approve an initiative right now?” (Decision)

The discovery goal is the same. The framing is completely different: cost avoidance, risk reduction and fast payback instead of new investment.

Great SEs do this by instinct. A good AI discovery tool should do it for every SE by reading the customer’s current situation (earnings, news, leadership changes, account history) and shaping the questions to fit.

Where agentic AI changes the game

Most AI discovery tools are reactive: you give them a transcript, they give you a summary.

Agentic AI works differently. It can work toward a goal without being prompted at every step. It understands the context, reasons about what’s missing, and acts to move the opportunity forward. It knows who you’re talking to and what matters to them.

Before the meeting, it researches the account, checks what was discussed last time, picks up signals like a quarterly loss or a new CIO, and prepares a brief with questions framed for the customer’s current situation.

After the meeting, it understands what happened, not just what was said: who attended, what changed, which concerns came up, and what the customer avoided answering.

Then it guides the next step. It updates the opportunity, flags the gaps and tells the team what to do next:

“The customer confirmed the pain but avoided the timeline question twice. The CFO wasn’t in the room. Next step: request a 30-minute session with finance, and lead with a payback analysis rather than a pricing proposal.”

The shift is from “here’s what happened” to “here’s what to do next, and why.” The SE stays in control, but they never walk out of a meeting unsure of the next move.

Five tests for any AI discovery tool

1. Does it find the gaps? It should show you which of reality, risk and decision is still uncovered.

2. Does it read the customer’s situation? Questions for a company in a cost-cutting quarter should look very different from questions for one in a growth phase.

3. Does it remember the opportunity? Context should carry from meeting one to meeting five, so your SE never re-asks what the customer has already answered.

4. Does it guide the next action? The morning after a call, the SE should know exactly what to do and who to talk to.

5. Does it connect discovery to the Technical Win? What you learn should flow straight into the pitch, architecture, sizing, TCO and competitive position. The right system keeps asking: “What is still stopping us from winning technically?”

How to run the evaluation

Skip the vendor demo. Test it on 3–5 of your own live deals, including one where the customer is under financial pressure. Put it in the hands of a senior SE and a newer one, and run it for a full discovery cycle.

If your team has built an internal tool, hold it to the same tests, and compare its AI spend against the deal outcomes it has actually influenced.

Measure what matters: gaps caught, how well the questions fit each customer’s situation, how useful the next-step guidance is, and whether the deal moves toward a Technical Win.

Red flags: every customer gets the same generic questions, it ignores what’s happening in the customer’s business, AI costs keep rising but nobody can point to a deal it helped move, or your SEs stop opening it after week two.

The ultimate test

At the end of the pilot, ask your SEs one question:

“Do you understand the customer better, and do you know exactly what to do next?”

If the answer is yes, you’re not looking at a note-taker. You’re looking at an experienced presales coach sitting beside every SE on your team.

Because the goal of discovery was never to collect more information. It’s to ask the right question at the right moment, expose what’s risky, understand how the decision will be made, and move the deal toward a Technical Win.