Client and market research
Compiles background, market context, and problem framing into a structured brief before a pitch.
Guide — — by Mahmoud Zalt
A practical guide to where a consulting firm should start with AI: begin with one AI Employee on research and proposals, not a firm-wide rollout.
Most consulting firms approach AI backwards. They form a committee, debate a firm-wide strategy, worry about which model is best, and six months later nothing has changed except the number of meetings. The firms that actually benefit do the opposite. They pick one task that drains time on every engagement, put an AI Employee on it, and measure the hours it gives back. Strategy follows evidence, not the other way around.
The reason to start narrow is that consulting value is judgment, and judgment does not automate. What does automate is the work around the judgment: the desk research before a workshop, the first draft of a proposal, the meeting notes turned into an action log, the status update nobody wants to write. An AI Employee is well suited to exactly this layer, so you hire it for that layer, keep the judgment yourself, and free your consultants to spend billable time on the thinking clients actually pay for.
The best first task has three traits: it happens on nearly every engagement, it is repetitive enough to describe clearly, and it does not require the partner's judgment to be done well. Research and proposal support fits all three. Every new opportunity needs background on the client, the market, and the problem, and every engagement produces a proposal or a scope document that starts from a blank page. That blank page is where an AI Employee earns its keep, drafting the structured first version so a consultant edits instead of writes.
Meeting-to-memo is the close runner-up. Consultants sit in client conversations all day and then lose an hour turning each one into notes, actions, and a follow-up. An AI Employee briefed on your format can take the raw notes and produce the clean summary, the action list, and the draft follow-up email, leaving the consultant to check and send. Pick whichever of these two bleeds more time in your firm, and make it the single job of your first hire.
Compiles background, market context, and problem framing into a structured brief before a pitch.
Turns your methodology and scope notes into a formatted proposal a consultant refines instead of writes.
Converts raw notes into clean summaries, action lists, and follow-up drafts in your firm's format.
Assembles recurring engagement updates so partners review a draft rather than starting from scratch.
Once you have chosen the task, the rollout matters as much as the choice. The firms that succeed treat the first AI Employee like a new junior hire: they onboard it carefully, review its early work closely, and expand its responsibilities only as trust builds. The firms that fail try to switch on ten use cases at once, cannot supervise any of them properly, and conclude that AI is not ready when the real problem was the rollout. The steps below are the sequence that keeps you in the first camp.
A safe rollout is a small rollout. You want one champion, one task, one engagement type, and a short review loop, so any mistake is caught before it reaches a client. That discipline is what turns a pilot into a habit. The five steps below are how I would introduce the first AI Employee into a professional services firm without upsetting delivery or spooking partners.
Two cautions specific to consulting. First, be deliberate about client confidentiality: brief the Employee on what it may and may not use, and keep sensitive engagements under the same access discipline you would apply to a junior hire. Second, never let a draft reach a client without a human review in the early weeks, because your reputation is the product and one careless proposal costs more than the tool saves. Trust is earned on reviewed work, then extended.
The honest framing for a firm is that AI does not replace consultants, it removes the unbillable drag around them. The research that used to eat an afternoon, the proposal that started from nothing, the memo that stole an hour after every meeting, that is the work an AI Employee absorbs. What remains is the judgment, the relationships, and the recommendations, which is exactly the work your clients hired you for. Start there, on one task, and let the results decide how far you take it.
Research and proposal support. It appears on nearly every engagement, it is repetitive enough to describe clearly, and it does not require a partner's judgment to produce a strong first draft. That combination makes it the lowest-risk, highest-frequency place to start.
No. You brief the AI Employee in plain English, the way you would onboard a junior hire, and you correct its work by editing drafts. There is no model selection, no prompt engineering, and no integration project required to get the first role producing useful output.
Sistava starts at 49 per month with credits bundled into the plan and no per-seat surcharge. For a firm billing at professional-services rates, one AI Employee that returns a few hours per week per consultant typically pays for itself quickly.
Treat the AI Employee like any junior hire with access to sensitive material. Brief it on what it may use, scope its access to the documents and tools it needs, and keep confidential engagements under the same review discipline you already apply to your team.
No. The firms that succeed start with one task, one champion, and a tight review loop, then expand as trust builds. A firm-wide rollout you cannot supervise is the most common way these projects stall and get written off as not ready.
Where to start with AI in a consulting firm is a question of focus, not ambition. The firms that win do not launch a program, they hire one AI Employee for one draining task, review its work like a junior's, and let measured hours decide the next move. Pick research and proposals or meeting-to-memo, whichever bleeds more time, brief the Employee in plain English, and keep a human on the review for the first few weeks. Prove it on one workflow, and the rest of the practice will tell you where it wants help next.