I talk to a lot of analysts, consultants, and Excel professionals about AI. And one pattern shows up again and again.
Teams want to use AI, but they tend to start at the wrong end of the problem. The questions usually sound like: how can we use Copilot here, can AI replace this workflow, should we be using AI for this task. Those questions almost always come after a tool has already been chosen. AI shows up as a solution looking for a problem, which is usually where things start to drift.
If you want AI to create real value inside Excel workflows, you have to start somewhere else. Not with features or prompts, but with the work itself. This post is about how to identify real AI opportunities in Excel by looking closely at where analyst effort is being spent today, and where that effort produces diminishing returns.
Start by ignoring AI entirely
The fastest way to miss good opportunities is to start by asking what AI can do. That framing leads to hypotheticals, novelty projects, and workflows that feel modern but fragile.
A better starting point is to ignore AI entirely and look at your existing Excel work as if AI did not exist.
Where does work slow down? Where do analysts hesitate? Where does the same explanation get rewritten slightly differently every cycle?
Real AI opportunities rarely appear as brand new workflows. They almost always hide inside work that already happens on a predictable cadence.
Monthly reporting, forecast updates, performance reviews, recurring analysis requests. The structure is familiar, but the questions never land exactly the same way twice.
That tension is where AI becomes interesting.
Think in terms of analyst time, not automation
Traditional Excel automation is about rules. You define them once and execute them repeatedly. Power Query, formulas, data models, and scheduled refreshes excel at this. When the rules are stable, they are hard to beat.
AI is not a replacement for that.
AI becomes useful when analyst time is being spent on reasoning that cannot be fully encoded. A helpful way to think about this is marginal effort.
Early effort in a workflow often creates insight. Later effort often preserves it. Analysts end up re-deriving explanations, rephrasing narratives, and reconstructing context that already existed last month. When analyst time is spent maintaining understanding rather than creating it, you are close to a real opportunity.
The kinds of Excel work where opportunities tend to hide
In practice, the opportunities that hold up tend to fall into a few recognizable patterns.
One is translational work. The analysis itself is solid, but it has to be explained to different audiences. Numbers turn into narratives. Charts need framing. Technical findings have to be made legible to people who were not involved in the analysis.
Another is re-derivation work. The reasoning is familiar, but it is not formally encoded, so analysts rebuild it each cycle. Variance explanations, root cause analysis, and forecast commentary often fall into this category. The logic is known, but the path is rediscovered every time.
A third pattern is exploratory compression. Large amounts of analysis lead to a very small final output. Many pivots, hypotheses, and drafts collapse into one chart or one paragraph. The compression itself is the work, and it resists traditional automation.
These patterns show up constantly in real Excel teams, even if they are rarely named.
A simple workflow lens that helps
One way to make these opportunities visible is to stop thinking in terms of tools and start thinking in terms of function.
| Workflow step | What dominates the work |
|---|---|
| Data ingestion and cleanup | Rules and volume |
| Calculation and modeling | Rules with edge cases |
| Analysis and interpretation | Judgment and context |
| Explanation and narrative | Translation and framing |
| Packaging and delivery | Communication |
AI opportunities tend to cluster after calculation and before final delivery. That is the zone where Excel stops being purely computational and starts becoming interpretive. It is also where analysts spend a surprising amount of cognitive energy.
Excel is a good place to see this boundary because it is visible. You can literally watch where formulas end and sentences begin.
A short checklist for identifying real opportunities
When scanning recurring Excel work, I usually ask a small set of questions.
- Does this task recur on a predictable cadence?
- Is the logic similar each time, but never identical?
- Does the work involve explanation, interpretation, or narrative?
- Is the output consumed by people who were not in the analysis?
- Does effort scale with questions rather than data size?
If most answers are no, AI is probably the wrong tool. If two or three are yes, it may be worth experimenting. If most are yes, you are likely looking at a real opportunity. This is not a guarantee of success, but it is a much better filter than asking whether AI could do something.
Where AI actually fits in Excel workflows
Even when a task looks promising, placement matters.
In healthy Excel workflows, AI lives upstream. It supports exploration, sense making, drafting, and communication. It helps analysts think faster and explain more clearly. It does not replace final calculations, production logic, or systems of record. Those still belong to deterministic tools, for all the boring and important reasons that made them reliable in the first place.
AI accelerates judgment. It should not become the authority.
A final way to think about it
If a workflow feels expensive because it requires humans to execute fixed rules, the answer is better encoding, not AI.
If it feels expensive because it requires humans to repeatedly think, explain, and contextualize, AI may help.
Real AI opportunities in Excel are not found by scanning feature lists. They are found by noticing where human reasoning is being spent again and again just to keep the same understanding alive.
That is where leverage lives.
A practical next step
If this framework resonated, the simplest next step is to apply it to one real workflow.
Pick a recurring Excel task you worked on in the last 30 days and ask yourself where judgment showed up, where explanation took longer than calculation, and where you felt like you were rethinking something you had already thought through before.
If you can point to a specific step where analyst reasoning is repeatedly being rebuilt, you have likely found a real AI opportunity.
If you want help applying this lens to your own Excel workflows, reporting cycles, or analytics stack, I offer structured training and advisory work for teams. These engagements focus on clarifying which workflows should remain fully deterministic, identifying safe and high-leverage places for AI support, and reducing analyst fatigue without introducing reporting risk.
You can learn more about my training and advisory options here:
If you’re looking for individualized tips or one-off guidance, that’s best handled through my membership, where questions can be asked and answered in a structured way. I don’t provide ad-hoc troubleshooting or custom implementation help via comments or DMs.