Zohe Mustafa recently shared something that caught my attention: "AI doesn’t create insight. It exposes your thinking." They followed it with a simple test: give two teams the same data and the same AI, and they will still make very different decisions. Not because the tools differ, but because the framing does.

That idea lands because it explains a pattern many leaders feel but struggle to name. When an organization rolls out AI, it often expects clarity to appear automatically: cleaner reporting, faster answers, tighter alignment. Sometimes that happens. Other times, AI seems to accelerate the chaos: more dashboards, more metrics, more analysis, and somehow less confidence.

Zohe’s point is that AI is not the cure for confusion. It is an amplifier. If your team has a shared intent and clear decision rules, AI makes them faster. If your team is misaligned or unclear on what matters, AI makes that louder too.

AI does not replace thinking, it reveals it

Zohe Mustafa’s line "AI just turns the volume up on whatever intent is already there" is the most practical way I have seen to describe why AI initiatives succeed or fail.

"AI doesn’t create insight. It exposes your thinking."

A model can summarize, forecast, cluster, classify, and generate options. But it cannot choose what success means for your business. It cannot decide what tradeoff you are willing to make. It cannot resolve disagreement about which customer segment matters most, or whether growth beats profitability this quarter.

So when two teams use the same AI and arrive at different recommendations, the difference usually comes from human choices upstream:

Which question they asked (and which they avoided)
What assumptions they embedded in the prompt, the metric definitions, or the analysis
Which constraints they treated as fixed vs negotiable
What they consider "good enough" evidence to act

In other words, interpretation.

The framing gap: same data, different decisions

Zohe Mustafa wrote: give two teams the same data and the same AI, and they will still make very different decisions. That is not hypothetical. It happens constantly in organizations with multiple functions.

Consider a shared dataset: acquisition, activation, retention, revenue, support tickets, churn reasons.

A Growth team frames the problem as: "Where can we remove friction to increase signups?" AI highlights steps with the biggest drop-off and suggests experiments.
A Finance team frames the problem as: "Where are we leaking margin?" AI highlights discounts, high-cost channels, and unprofitable cohorts.
A Support team frames the problem as: "Why are customers unhappy?" AI clusters complaints and surfaces recurring issues.

All of them are "right" within their frame. The conflict emerges when the organization has not agreed on the decision that matters most right now. AI did not create the conflict. It exposed it.

Why more dashboards do not fix confusion

Zohe Mustafa also said: "More dashboards don’t fix confusion. More data doesn’t create clarity." That is worth repeating because it cuts against the reflex many teams have: when things feel unclear, build another dashboard.

Dashboards are valuable when:

The business has a small number of agreed leading indicators
Definitions are consistent (for example, what counts as an "active user")
There is a clear decision cadence (weekly, monthly) tied to the metrics

Dashboards create confusion when they become a substitute for alignment. You end up with:

Conflicting metrics (same concept, different definitions)
Faster confusion (AI generates more views, more charts, more summaries)
Activity without direction (lots of reporting, little decision-making)

AI accelerates the production of analysis. If analysis is not tied to decisions, the output becomes noise, even if it is technically correct.

What high-performing teams do differently

Zohe contrasted two outcomes:

Some teams get conflicting metrics, faster confusion, and activity without direction.
Others get fewer metrics, clearer decisions, and action they trust.

The difference is rarely that the second group has a better model. The leverage is interpretation: the ability to frame the question, define the decision, and choose the smallest set of signals that matter.

Here are the practices I see in teams that get "action they trust" from AI.

1) They start with the decision, not the data

Instead of asking AI, "What does the data say?" they ask:

"Should we invest in onboarding or acquisition next quarter?"
"Which three segments should we prioritize?"
"What is the minimum change that will reduce churn by 10%?"

Then they use AI to support that decision with evidence, scenarios, and risks.

2) They define metrics in writing

Conflicting metrics are a governance problem, not a tooling problem. High-performing teams write down:

Metric name
Formula
Data source
Owner
Refresh cadence
Known limitations

AI can help draft and validate these definitions, but it cannot decide them for you.

3) They separate exploration from execution

Exploration is where AI shines: generating hypotheses, scanning patterns, summarizing qualitative feedback.

Execution requires discipline: selecting a hypothesis, designing a test, assigning ownership, and committing to a next action. Teams that blur the two live in endless analysis.

4) They treat prompts and assumptions as first-class artifacts

If AI is an amplifier, then prompts and assumptions are the volume knob.

Strong teams standardize:

The business context included in prompts
The time window and cohort definitions
The constraints (budget, compliance, brand)
The evaluation criteria (speed, cost, quality, risk)

They also review prompts the way they review code or financial models: as something that shapes outcomes.

A practical way to apply Zohe Mustafa’s idea

If you want to test whether your organization is ready to benefit from AI, run a simple alignment drill inspired by Zohe’s post.

Pick one decision you need to make this month.
Give two teams the same dataset and the same AI tool.
Ask each team to deliver:
- their recommended decision
- the top 3 metrics they used
- the assumptions they made
- what would change their mind
Compare outputs.

If the recommendations differ, do not argue about the model. Ask:

"What did you optimize for?"
"What tradeoff did you assume we prefer?"
"Which definition of success did you use?"

That conversation is where clarity is created. AI simply makes the conversation unavoidable.

The real promise of AI: faster learning, not automatic wisdom

Zohe Mustafa’s closing line, "AI is an amplifier," is the anchor. AI can help you move faster, but it cannot tell you where to go.

"The leverage isn’t the model. It’s interpretation."

If your team invests in framing, shared definitions, and decision discipline, AI becomes a force multiplier. If those foundations are missing, AI will faithfully multiply the mess.

So the next time you feel tempted to add another dashboard, consider a different move: reduce the metrics, write down the definitions, and agree on the decision your metrics are supposed to serve. That is how you turn AI from louder noise into trusted action.

This blog post expands on a viral LinkedIn post by Zohe Mustafa. View the original LinkedIn post →