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2026 guide

AI for Consultants

The synthesis in an hour instead of a weekend — and the number on slide 4 checked before the client checks it.

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No credit card · all four major AI models in one place

The risk in consulting AI is not that it writes badly. It is that it writes plausibly. A market size with no source, a benchmark from 2023 quoted as current, a recommendation that only holds for one customer segment — all of it in clean McKinsey prose that reads as though someone checked it.

Nobody in the room can tell the difference by reading. They can only tell when the CFO asks where the number came from. Which is why the useful question is not which model writes the best deck, but what happens between the draft and the meeting.

What AI does for consultants

Use case 1

30 stakeholder interviews into a synthesis that holds up

Upload the transcripts. You get themes with attributed quotes — and, separately, the places two models read the same interviews differently. That second list is where the real finding usually is: one model heard consensus, another heard three people saying the same words about different problems.

Try this prompt

Attached are [N] stakeholder interview transcripts from a [type] engagement at [company type]. Produce: (1) five dominant themes, each with two verbatim quotes and the speaker's role; (2) three contradictions between what different levels of the organisation said; (3) anything said once, by one person, that would change the recommendation if true; (4) the question I should have asked and did not. Do not smooth over disagreement between interviewees — surface it. Cite the transcript and line for every quote.
Use case 2

Size a market without the number falling over in the room

Ask for both methods, not one. Top-down from industry reports and bottom-up from customer counts almost never agree, and the gap is the conversation. One model sizes it each way, a third reports where they diverged, and the verification pass tells you which figures have a source behind them and which do not.

Try this prompt

Size the [market] for [geography] for a client deck. Do it twice: top-down from published industry figures, and bottom-up from an estimate of addressable customers × realistic spend. Show both numbers and the assumptions behind each. Where they disagree, say which assumption is doing the work. Mark every figure that you cannot attach a current source to — do not estimate silently.
Use case 3

Have the recommendation attacked before the steering committee does

Debate mode, with two models assigned opposite sides. What comes back is not general pushback but the specific condition your recommendation depends on and nobody stated — the churn rate, the segment, the assumption that the pilot generalises.

Try this prompt

Here is my recommendation and the reasoning behind it: [paste]. Argue the strongest case against it as the client CFO who has seen three consulting engagements fail. Then, separately: what would have to be true for this recommendation to be correct? List those conditions explicitly and say which of them we have evidence for and which we have assumed.
Use case 4

Executive summary that survives a 90-second read

The version a partner will not have to rewrite. Then a second model reads it cold and tells you which sentence a client will stop on — usually a claim you inherited from the analysis three weeks ago and never re-checked.

Try this prompt

Write a one-page executive summary of the attached report. A CEO reads it in 90 seconds: recommendation first, then the three reasons, then the two risks, then the specific ask. No throat-clearing, no 'in today's rapidly evolving landscape'. Then list every factual claim in your summary and mark whether the source document actually supports it.
Use case 5

Turn the findings into the deck, not into chat text

Presentation Studio builds the deck from the analysis you have already done in the project, so the numbers in the slides are the numbers that were checked — rather than re-typed from a chat window at 11pm.

Try this prompt

From the analysis in this project, build a 12-slide findings deck for [audience]: verb-led action titles, one message per slide, the evidence for each finding in the speaker notes rather than on the slide, and a final page listing every assumption the recommendation depends on.

The best AI model for consultants

Claude reads long qualitative material best. ChatGPT is faster on frameworks and tables. Perplexity is the one that comes back with a source.

That is a real difference and worth knowing. But on an engagement you are not choosing — you are drafting with one, having another read it cold, and sending anything with a number in it through a source check. The models disagree most often on market sizing method, on whether a pilot result generalises, and on how firmly to state a recommendation. Those three disagreements are worth more than picking correctly between the models.

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What you might be worried about

My firm's MSA says client data does not go into third-party tools

Read the clause before you read the vendor page. On paid plans nothing you enter trains any model and the providers do not retain it, which satisfies most standard wording — but 'no third-party processing' clauses are absolute and no assurance gets you around them. Practical habit: work with initials, figures and anonymised roles. Synthesis and stress-testing rarely need the client's name.

A partner will spot AI prose immediately

They will, if you publish a first draft. The tell is even rhythm, hedged claims and no point of view. The fix is not a better model — it is feeding it your own notes and your own argument rather than asking it to have one, and then cutting the third of it that says nothing.

What happens when a number in the deck turns out to be wrong

That is the actual professional risk and it is why the verification record matters. When a figure is questioned six weeks later, the chat shows which models were asked, what the fact check found, what it could not support, and where the models disagreed. That is a considerably better answer than 'the analyst pulled it from a report'.

Is AI-assisted work billable?

Firms are split and it is moving fast. The defensible position most have landed on: you bill for the judgement, the client relationship and the accountability, not for keystrokes. Engagements that used to need three analysts for the scan now need one — which changes the pyramid before it changes the rate card.

How to start in the next 10 minutes

1

Open MultipleChat and turn on AI Collaboration in the model selector. Put ChatGPT, Claude and Gemini on the task and give one of them the disagreements role.

2

Create a Project for the engagement. Drop in the transcripts, the data room extracts and the client's own language — so you stop re-pasting background every session.

3

Start with interview synthesis. It is internal, it is the most tedious thing on your desk, and it is where the cross-check earns its place fastest.

4

Before any deck goes out: read the Disagreements list, then check every figure that carries a claim. That is fifteen minutes, and it is the fifteen minutes that matters.

FAQ for consultants

Can it read a full data room or a 200-page report?

Yes, including scanned PDFs. Ask for page references on every claim so you can trace anything back — a summary you cannot trace is not usable in a deliverable.

What does AI actually get wrong in consulting work?

Three things, repeatedly: market figures with no source, benchmarks quoted from an outdated year as though current, and recommendations stated with more certainty than the evidence supports. All three are invisible on the page and all three are what the verification pass and the disagreement list are for.

How do I stop the deck sounding like every other deck?

Give it your point of view to work from instead of asking it for one, ban your own worst clichés explicitly in the prompt, and use the humanize pass on anything client-facing. The consulting monotone comes from prompting with nothing.

Can it build the actual slides?

Presentation Studio produces a real deck from the analysis in your project, which you then take over. It gets you past structure and first-draft copy; the story and the visual judgement stay yours.

Is one model enough for internal work?

For a summary nobody acts on, yes. The moment a number goes in front of a client, one model is a figure you cannot defend when someone asks where it came from.

Use AI for market, customer, and competitor research

For professional work, the valuable output is not just an answer. It is a researched brief, report, proposal, or presentation that helps someone make a business decision.

Ready to try AI for your work?

MultipleChat gives consultants access to ChatGPT, Claude, Gemini and Grok — one login, one bill, side-by-side.

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