Why Your Best Client Work Doesn't Reach the Next Team
Consulting and FDE teams develop valuable expertise in every engagement. Here's why that knowledge often stays within one project, and how to turn it into better ways of working across the firm.
A team discovers a better approach during a client engagement. A different sequence of questions reveals the real problem sooner. An extra check before a proposal prevents rework. A clearer evidence requirement makes an AI output easier to review.
The project goes well. The client is happy.
Meanwhile, another team in the same firm is working through a similar problem from the beginning.
Nobody has failed at delivery. Yet the firm is missing an opportunity to reuse something valuable: the method and judgment the first team spent time developing.
People are gaining experience. Is the company getting better at the work?
For consulting and forward-deployed engineering firms, this is a challenge in spreading excellent work. It is also the problem we want Epismo to help solve.
Successful Projects Can Still Leave Learning with Individuals
Consultants adapt their approach to each client's situation. Forward-deployed engineers (FDEs) work through technical and business constraints to build systems people can use. Their roles differ, but both develop expertise through client work.
The final deliverable carries only part of that experience.
A presentation may explain the conclusion without showing which question exposed the problem, why an alternative was rejected, or what evidence the team waited for before making a decision.
Another practitioner needs that judgment to work at the same standard. If an experienced colleague must explain it personally every time, the reach of that expertise remains limited by their availability.
OpenAI describes an ambition for its FDE team to learn from customer work and give the next customer a better starting point. Turning lessons into product capabilities and improving a consulting methodology are different mechanisms, but both make one engagement useful to the next.
Alongside completing the project, firms have an opportunity to decide what that project should contribute to their collective method.
Make the Criteria Behind the Decision Reusable
Past work, internal wikis, and templates are important resources. Their practical value increases when they include criteria another person can use to make a judgment.
Consider an instruction to check a deal's profitability. Different practitioners might interpret that differently.
Has the team estimated ongoing support as well as development costs? Which assumptions could change the estimate? If the answer is unknown, what needs investigating before a decision can be made?
Making these questions explicit lets another practitioner review the work from the same perspective without waiting for the original expert. It also helps an AI agent understand what to investigate and what evidence to preserve.
Expertise becomes a working asset when someone else uses it to judge real work.
APQC emphasizes validating lessons, making them actionable, and applying them in future decisions and workflows. Firms need a connection between recording a lesson and using it.
Review in Practice: Two Examples
In an enterprise deal review, Epismo flagged a missing evaluation of development and support costs. The team investigated and declined a request it otherwise would have accepted.
The value was in bringing missing evidence into the decision. Review helped identify what needed investigating; the team used the findings to decide.
In a separate example, a consulting partner applied improved deal-review criteria across multiple client engagements. A better method reached beyond the project and practitioner where it originated.
The first shows review supporting a better-informed decision. The second shows improved criteria reaching multiple client engagements.
Together, they point to the opportunity for a firm: help teams apply useful criteria in real work, then use what they learn to strengthen those criteria.
Connect Review of the Work to Review of the Method
A playbook in a document tells a team what to check. Connected to real work, it helps the team see the evidence behind those checks, and what the method itself may be missing.
A case preserves purpose, evidence, decisions, results, and next steps so people and AI can understand and continue the same work.
A playbook makes the firm's approach reusable: the important questions, guidance, and expected outputs that help a team do good work.
AI review examines recorded work against its purpose and the guidance attached to it. It helps surface gaps in evidence, contradictions, and issues worth investigating. Teams can record their findings and reevaluate as the work develops.
What review reveals can have two destinations. A missing piece of evidence calls for follow-up in this case. A recurring gap may call for a change to the shared method.
Was an existing check missed, or was an important question absent from the approach itself? The first improves this piece of work. The second can improve how the firm works.
AI review can point to potential improvements. The team decides which lessons merit a playbook change, checks where they apply, and makes the revised guidance available to colleagues.
Review the work. Then use what it reveals to review the way the work gets done.
Shared Standards Leave Room for Client-Specific Judgment
Different clients need different answers. Shared criteria give teams a consistent basis for asking questions and examining evidence while leaving room to adapt their approach.
A specialist can contribute through the projects they deliver and the method colleagues use. Those colleagues can refine that method through their own experience. Client-specific information stays within its intended audience; generalizable learning can inform other work when sharing is permitted.
Great work shouldn't depend entirely on which expert happens to be on the project. The standards behind good work should be available to every team, applied to real decisions, and improved through experience.
The Quality Layer for Human and AI Work
That is the connection between shared expertise and quality: making the criteria behind good work available, using them to examine actual evidence, and improving them when experience reveals a gap.
This is the role we want Epismo to play.
The quality layer for human and AI work.
Where people and AI collaborate, review, and improve how work gets done.
Collaboration gives the team shared context. Review brings the method into the decision. Improvement carries validated learning into future work. The team remains responsible for its judgments, with a stronger foundation to make them.
A firm should be able to grow stronger through the successful work it already does.
We are building Epismo to help that learning become part of everyday work.