Testing AI recommendations takes three checks. Can you see the sources, does the tool state its confidence, and can you verify the answer yourself?
Picture a review where you share an AI-generated target list, and a colleague asks, “What’s this based on, and how do you know it’s right?” The recommendation might have come from a chatbot, a scoring model, or an agent. Either way, you own the answer.
Four questions to ask before you act
Run any recommendation through these questions before you put time or budget behind it. They work on any AI tool, from a general-purpose chatbot to a scoring model inside your marketing platform. Each one covers why it matters, what a good answer sounds like, and what should slow you down.
1. What is this based on?
Start with the inputs. Every recommendation draws on something, and you want to know what.
Say you ask an AI assistant which accounts to prioritize next quarter. One answer lists accounts and says they “show strong buying signals.” Another lists the same accounts and names the pages they visited, the topics they researched, and the CRM fields the model used. Only the second gives you something to check.
Why it matters: Bad training data produces bad outputs, no matter how good the model is. If you can’t see what a recommendation draws on, you can’t tell whether it found a real pattern or made an educated guess.
What a good answer sounds like: It names sources you can open and check, such as pages visited, topics researched, or the CRM fields used. It also says how recent those sources are.
What should slow you down: Vague references to “signals” with no examples, sources that are months old, or an answer that cites different data each time you ask.
2. How confident is it, and how do you know?
AI often sounds sure of itself. A good tool can tell you how sure it actually is.
A confidence score is a number that shows how likely a recommendation is to be right, often on a scale of 0 to 100. A recommendation scored 80 is a stronger call than one scored 50. Some tools show a score. Others use words like “high” or “low,” and some say nothing at all.
Why it matters: Without a stated confidence level, every recommendation looks equally solid. Some are strong calls and some are best guesses, and your team needs to know which is which.
If the tool shows a score: Treat it as a likelihood. It’s never a promise. Ask what an 80 means in practice. If you took 100 recommendations at that score, how many turned out right? Ask which score your team should treat as “act now” and which as “look closer.”
If the tool shows no score: Ask it which part of the answer rests on the thinnest evidence. Then ask the same question again in a new session. If the answer shifts a lot, treat it as a best guess.
What a good answer sounds like: It defines confidence in plain terms, ties it to past outcomes, and names a threshold for action.
What should slow you down: High confidence with no evidence behind it, or a score nobody on your team can explain.
3. Does it match what you know?
Your experience is a check the model doesn’t have.
Why it matters: The person closest to the work, whether a rep, campaign manager, or ops analyst, knows things no model can see. They know which executive just joined, which deal stalled last quarter, which contact never returns email, and what happened on yesterday’s call. A recommendation that clashes with that knowledge deserves a closer look. One that agrees with it earns a bit more trust.
What a good answer sounds like: The recommendation lines up with what that person already sees, or it surfaces something new they can check in a few minutes. When it clashes, they can say why, and the tool can show what it saw.
Keep a simple tally: For each recommendation you act on, log whether it was a hit, a miss, or too early to tell. When someone overrides one, capture the reason in a CRM field with a short pick list, such as wrong buying group, budget frozen, or false positive. Review the overrides monthly. After a quarter, you’ll see where recommendations are strong, where they need a second look, and whether the data behind them is going stale. That record also answers a reviewer who asks how well the tool performs.
What should slow you down: Recommendations that contradict what your team knows, with no way to find out why.
4. Can you explain it in one sentence?
This is the final test.
Why it matters: You’re the one who has to explain the decision. If you can give the reason in one sentence, you can act on the recommendation and defend it afterward.
What a good answer sounds like: “We’re prioritizing this account because three people there researched pipeline analytics this month and the account fits our best-performing customer profile.” It names the evidence and the action in plain words, with no jargon.
What should slow you down: An explanation that needs jargon to hold together, or one that comes down to “the model said so.”
Share the checklist with your team and reviewers
Forward this checklist to anyone who reviews or signs off on a new tool, such as sales leadership, finance, legal, or security. It takes a minute to scan and works on any AI tool. If you’re championing a tool, send it early so their questions shape the evaluation from the start.
☐ Sources: Can we verify what the recommendation is based on?
☐ Confidence: Is the score or confidence level defined and tied to past outcomes?
☐ Feedback: Do we log overrides and review them?
☐ Plain language: Can someone explain the recommendation in one sentence?
Make the questions a habit
Every AI recommendation your team receives is a chance to ask these questions, and the habit pays off more as agents take on more of the work. Over time, you’ll learn which tools show their work and which ones ask for your trust before earning it.
If you’re building systems around these questions, the Agentic GTM 101 guide covers trust and verification in more depth.
