Black-box AI: Does your AI analytics tool show its work?

AI is everywhere now answering questions, surfacing insights, and increasingly influencing real business decisions. But as adoption accelerates, something critical often breaks along the way: trust.

Not because AI lacks power or isn’t useful. It’s because too much of it still operates as a black box.

When an AI tool delivers a confident recommendation without explaining why, how, or on what basis it reached its conclusion, teams are left with an uncomfortable choice: accept it blindly or ignore it altogether.

In our first post on the missing layer in AI-powered analytics, we covered why AI needs context to ground it in reality. But context alone isn’t enough. Even with good inputs, teams still need to understand and verify how AI reaches its conclusions.

This is where explainability comes in.

Early waves of AI adoption encouraged speed and moving quickly. Many people using AI tools like ChatGPT treated outputs as authoritative by default, especially when they came wrapped in confident language.

But as AI tools mature and teams develop a clearer understanding of how they work, some have adopted a more cautious approach: trust, but verify. And for good reason—many modern AI analytics tools provide answers without citing data or reasoning. Assumptions are often hidden behind polished summaries, which may rely on overgeneralized assumptions or prioritize the wrong signals or data.

Even with strong context like clean data, clear metrics, and defined goals, AI models still infer, estimate, and generalize. That’s not technically a flaw; it’s just how probabilistic systems work.

The problem is when these types of uncertainties are invisible. Without transparency, product teams can’t:

The result: Teams need to slow down, re‑run analyses manually, or ignore AI outputs entirely.

In other words, black-box AI kills momentum.

Context and explainability are tightly linked. For AI analytics to be useful (and trusted), teams have to be able to access and understand its “thinking.” 

We’ve talked before about minimum viable context, or the least amount of context that’s needed to produce useful answers. This context must be objective, provide definitions of metrics, and include information about event semantics and scope. 

Explainability builds on this context and shows how it’s actually used. If context answers “what information did the AI have?”, explainability tells you “how did it reason with that information?”

With explainability, teams can:

At a minimum, an AI analytics tool should provide:

Even if an AI were built to be explainable by default, product teams can still make its outputs more transparent by probing with more targeted questions. Here are some practical prompts to try:

Start with a question like:

This helps confirm alignment with your internal source of truth (and surfaces any mismatches early). Push for specificity like time ranges and sample sizes instead of vague categories like “user behavior data.”

Ask the model to walk through its logic by asking it to summarize how it arrived at a conclusion. Look for a logical progression, not just a restated answer. A useful response should show intermediate steps or dependencies (e.g., “X changed, which influenced Y, leading to Z”). If the reasoning feels circular or skips steps, the insight might not be very robust.

The question below is asked often, especially when the initial answer feels obvious or neatly matches your expectations:

This reduces the risk of confirmation bias and helps teams avoid overindexing on a single narrative. Pay attention to whether alternatives are plausible and meaningfully different. Strong alternatives often point to experiments that are worth testing next.

To expose confidence levels, try a prompt like: 

Push the tool to describe where and why it might be wrong, such as unstable signals, small sample sizes, or edge cases. 

Try asking questions like: 

This forces the tool to explain how changes in each factor would affect the output (e.g., “If this signal increased or decreased, what would change?”). It also helps you distinguish between inputs that meaningfully influence the resulting data and those that are merely correlated or incidental.

Let’s say you have two hypotheses that you want to test. You could put the model in comparison mode by asking: 

Unlike #3 where you’re asking for more possible explanations, here you want to put two options side by side and force a comparison. For example, let’s say your signups dropped last week, and there are two possible causes but you’re not sure which is the culprit:

Try asking the model to call out which specific metrics, user segments, or behaviors would look different under A versus B. Looking at those differences helps product teams see which explanation better matches what actually happened, instead of guessing based on what sounds right.

Using the techniques mentioned above won’t eliminate all uncertainty for product teams, but they’ll make it visible, which is a vital first step towards trust. For AI analytics tools to be useful in real product decisions, transparency can’t be optional. It has to be built in.

At Mixpanel, we believe AI should be easy to understand, easy to trust, and easy to act on. That’s how trust scales: when executives can confidently stand behind decisions, and engineers can inspect, validate, and improve the systems powering them.

That means analytics that don’t just deliver answers, but also show their work. Systems that combine strong context, built‑in explainability, and human‑in‑the‑loop safeguards so teams can move quickly while being able to trust the real-time insights driving decisions across the organization.See how explainable generative AI works in practice for product analytics.

Sign up for Mixpanel and be a part of the next chapter of analytics.