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AI Prompts for Consultants
Consulting runs on a handful of artifacts under deadline — the issue tree, the interview synthesis, the board-ready storyline, the weekly status. A language model won't do your client's thinking, but with the right structure it turns raw inputs into a partner-reviewable draft fast. These prompts are built around real casework — problem structuring, synthesis, and communication. Paste them into ChatGPT, Claude, Copilot, or Gemini, and pressure-test every conclusion against your evidence.
3 free prompts you can run right now
Issue tree builder: from vague client ask to testable hypotheses
New engagement, vague problem statement. Build the issue tree and the week-one analysis plan before the team burns days boiling the ocean.
You are an engagement manager structuring a new client problem. I will paste the raw ask and context. Produce: A) PROBLEM STATEMENT — restate the ask as one specific, measurable, time-bound question, using the client's own words where possible. List what the question deliberately excludes. B) ISSUE TREE — 3 levels, MECE at each level, every branch phrased as a testable hypothesis (not a topic). Present as a table: hypothesis, data needed to prove or kill it, likely source, effort (S/M/L). C) WEEK-ONE PLAN — the 5 analyses ranked by kill-power: which would change the overall answer fastest if the hypothesis fails. Inputs: [PASTE CLIENT ASK / RFP EXCERPT / KICKOFF NOTES] · [INDUSTRY + CLIENT CONTEXT] · [ENGAGEMENT LENGTH + TEAM SIZE] Rules: Do not invent client facts or market numbers — mark every assumption as an assumption and keep them in a separate list. Verify data availability with the client before committing the plan. Never include client-confidential material beyond what I pasted, and anonymize the client in your output.
Hypothesis tree: break a fuzzy client question into testable branches
The client asked something huge and vague. Turn it into a MECE tree of hypotheses you can actually test this week.
You are a strategy consultant structuring an ambiguous client question into a testable hypothesis tree — a thinking aid, not a conclusion. Produce: A) CORE QUESTION — the single decision the client actually needs to make, stated in one sentence. B) HYPOTHESIS TREE — a MECE breakdown into 3-4 branches, each a falsifiable hypothesis (not a topic), and under each the 1-2 sub-hypotheses that would prove or kill it. C) TEST PLAN — for each leaf hypothesis, the specific analysis or data that would confirm or refute it, and roughly how hard it is to get. D) FIRST CUT — which 2 branches to test first for the fastest read on the answer, and why. Inputs: [CLIENT QUESTION] · [WHAT WE ALREADY KNOW] · [DATA / ACCESS AVAILABLE] · [DEADLINE] Rules: Keep branches genuinely mutually exclusive and collectively exhaustive — flag any overlap. Do not assert conclusions; these are hypotheses to test. Keep confidential client data out of consumer AI tools. The structure is a scaffold; the analysis and judgment stay yours. Do not invent facts, numbers, or details you weren't given; verify anything uncertain against the source before relying on it.
Interview synthesis: 12 transcripts into findings your partner will sign
Two weeks of expert interviews sit in a folder. Get to defensible findings — with quote-level evidence — before the midpoint readout.
You are a consulting research lead synthesizing qualitative interviews. I will paste anonymized interview notes or transcripts. Produce: A) FINDINGS TABLE — maximum 6 findings, columns: finding (one sentence, so-what phrasing), supporting evidence (interviewee number + short verbatim quote), count of interviewees supporting it, confidence (strong / emerging / single-source). B) CONTRADICTIONS — where interviews genuinely disagree: both positions, who holds each, and what evidence would resolve it. C) GAPS — the 3 questions still unanswered and which type of interviewee could answer each. Inputs: [PASTE ANONYMIZED NOTES/TRANSCRIPTS, LABELED BY INTERVIEWEE NUMBER + ROLE TYPE] · [THE ENGAGEMENT QUESTION THIS RESEARCH SERVES] Rules: Do not invent, merge, or paraphrase quotes into stronger claims — every finding must trace to at least one pasted line, and single-source findings must carry the single-source label. Verify counts before the readout. Keep all names out — interviewee numbers and role types only.
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Interview synthesis: turn ten transcripts into three defensible findings
You ran a week of expert interviews and have a pile of notes. Distill them into findings you can defend, with the dissent kept visible.
You are a research lead synthesizing qualitative interviews into findings for a client deck. Produce: A) FINDINGS — the 3-5 patterns that appear acr
Pyramid-principle storyline: from findings to a board-ready narrative
The analysis is done; the deck is not. Turn findings into an action-titled storyline the partner can review in 10 minutes.
You are a senior consultant drafting a deck storyline using the pyramid principle. I will paste the findings and the recommendation. Produce: A) GOVE
Storyline first: build the pyramid before you build a single slide
You're tempted to start making slides. Build the governing thought and the argument pyramid first, so the deck writes itself.
You are a communications lead building a Minto-pyramid storyline before any slides get made. Produce: A) GOVERNING THOUGHT — the single sentence the
Workshop designer: an agenda engineered to end in a decision
You own a 3-hour steering workshop next week. Design the agenda, exercises, and pre-read so it ends with a decision instead of a parking lot.
You are a facilitation designer engineering a client workshop that must end in a decision. I will describe the decision and the room. Produce: A) AGE
Workshop design: an agenda engineered to produce a decision, not a discussion
You have a room of senior stakeholders for three hours. Design an agenda that ends in an actual decision, not a vibes session.
You are a facilitation designer building a decision-oriented workshop agenda. Produce: A) OUTCOME — the one decision or artifact the room must leave
Weekly client status: progress, risks, and the scope-creep firewall
Friday status is due. Turn the team's raw week into a status the sponsor actually reads — and a scope conversation before it becomes a scope fight.
You are an engagement manager writing the weekly status to the client sponsor. I will paste the team's raw updates. Produce: A) STATUS ONE-PAGER — se
Scope guard: a risk register that catches creep before it eats the margin
The engagement is drifting and the budget is tightening. Build a risk register and a scope guard that surfaces creep early.
You are an engagement manager building a risk register and scope-control read. Produce: A) RISK REGISTER — the top risks to this engagement from wha
Frequently asked
Can AI build an issue tree or hypothesis structure?
Yes — that's one of its strongest uses. Paste the vague client question and have the model break it into a MECE issue tree with testable hypotheses and the data each one needs. It gives you a structured starting point in minutes instead of a blank whiteboard. You still own which branches matter and whether the logic holds; the model just makes the structure explicit.
How do consultants use ChatGPT for client deliverables?
To compress the drafting layer, not the thinking. Paste your findings and have the model arrange them into a pyramid-principle storyline — governing thought, supporting arguments, evidence — that a partner can mark up. It's also useful for turning workshop notes into a decision log. The insight is yours; the model speeds the packaging. Keep client-confidential data out of consumer tools.
Which AI model is best for consulting work?
They're model-agnostic and work in ChatGPT, Claude, Copilot, or Gemini. A larger context window helps when you paste a stack of interview transcripts or a long research dump. What matters far more than the model is the prompt structure and your discipline in verifying every synthesized claim against the source.
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