What AI changes for services firms: the work, the review and the price
The AI Product work group on what services firms hand to AI, who owns the review, and how to price work once drafting gets cheap.
Updated
The group expects AI to take on more of the preparation, drafting and follow-through for each client, moving a services firm's value toward checking the work, owning the outcome and pricing a result the client can use.
- One workflow covers more of the job. Sam Stone points to EvenUp's Express Demands, where legal professionals draft and edit demand packages in minutes, as a firm handling more of a case in one connected workflow while its people check facts and use judgment. Dennis Yang cites Chime's 70% of AI-powered support interactions and says cost and customer experience have to be measured together.
- Firms sell judgment and delivery. Rachel Wolan expects agencies to sell ongoing judgment and accountable delivery, with AI doing bounded pieces of the work. If she ran one, she would start with a repeatable task the client can inspect before it goes live, and measure revision time and errors as carefully as time saved.
- Quality means the client's result. Ami Vora would ask customers which result they value and check whether the faster process produces more of it, such as a recommendation the client can act on. Her test, from Faire, is whether it saves the customer time without handing them a confusing new process.
- Watch setup per client. Shishir Mehrotra saw Centrata grow like a consulting firm, even with multimillion-dollar software deals, because each deployment took so much hands-on work. He would watch how much setup each new client needs: if it stays flat, the firm still scales one specialist at a time.
- Price the accepted deliverable. Shishir would quote a defined scope and acceptance standard, and track the full cost per accepted deliverable: AI use, review, revisions and setup. For an AI-assisted demand package, Sam would define acceptance as records accounted for, facts matching the sources, checkable calculations, a professional's sign-off and a correction window. The standard cannot promise a settlement amount.
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Frequently Asked Questions
What should a first eval setup for an LLM product include?
A sheet of 50 to 100 real outputs graded by people with notes, a rubric checked against customer evidence, a fixed bug set and rule checks in code, and an agreed list of failures that block a launch.
How do you know your LLM evals measure what users care about?
Build eval cases from failures users hit on real tasks, then compare scores with what people do next: rephrasing the question, editing heavily, abandoning the answer or spending time checking it.
Which parts of a product should become agentic first?
A frequent, bounded job inside a workflow people already use, where they can review and correct the result before it goes out. Prototype it on real inputs before committing to the full agent.
How can a working professional build AI skills that last?
Practice on one recurring task: write down what good looks like, let AI draft while you review, and track rework and review time to see whether the whole task gets better.
How is AI changing the product manager's job?
Execution and coordination get cheaper, so more of the job is choosing the problem, judging output and supervising agents. Sachin Rekhi still wants PMs talking to customers themselves, and Ami Vora wants them close to those conversations.
Which operations work can AI agents handle today?
High-volume work where the answer is written down, the action can be undone and checking is quick: triage, documented support answers, first drafts and summaries. Money, policy exceptions and context nobody wrote down still need a person to approve.
Why doesn't AI adoption show up in business results more often?
Usage is an input. Results come from changing one workflow and measuring its outcome, including the time spent checking and fixing AI output and whether a decision changed.
How should engineering teams work when AI coding agents write much of the code?
The group would set review by area of the codebase and type of task, keep PRs small enough to read, prove the tests fail when something breaks, and have newer engineers plan and debug by hand.
Where does AI speed up design and content work?
Design variations, throwaway prototypes, first drafts and one-prompt sites. Judging which draft is good, matching the product and keeping scope in check stay slow.
