A method for applying AI to specific commercial processes: first clarify the problem, then organize the information sources and responsibilities, and only then design a test with decision criteria.
A framework for redesigning a process, organizing its information sources, and introducing AI where it can reduce friction, improve workflow continuity, and support commercial decisions.
It is not intended to replace the team, impose a single platform, or automate decisions without oversight. It adapts to existing tools and keeps human accountability for decisions, commitments, and risks.
The goal is not to add another technology layer. It is to turn a specific commercial problem into a way of working that can be tested, measured, and governed.
A clear process, authorized information, defined controls, and enough evidence to decide the next step.
The journey preserves the order of the E2M method and brings assessment, workflow design, the pilot, and the follow-on decision into one sequence.
Define the commercial problem, the decision that needs to improve, and the people involved.
Review the current workflow, sources, available systems, and real constraints.
Choose the first use case and agree on scope, inputs, outputs, and human review.
Design and test a module with real users and a previously agreed workflow.
Document the workflow, review its use, and decide whether to adjust, expand, or stop.
The operating layer turns the agreed scope into an assessable workflow. Each step produces a concrete output so the team knows what is being tested and against which criteria.
Problem, decision, and people involved.
Baseline for time, quality, errors, follow-up, or adoption.
Sources, permissions, owners, inputs, outputs, and review.
First workflow integrated with existing tools.
Real users, limited scope, and known criteria.
Compare results: scale, correct, or stop.
A pilot is not complete just because something has been built. It ends when there is enough evidence to make a responsible decision.
Each company can begin with a different level of clarity. The stages organize the conversation, and each ends with an explicit decision about the next step.
Confirm that there is a specific problem, an owner, and a minimum foundation for moving forward.
Review fit → 02Understand the process, sources, controls, and the use case worth prioritizing.
View the Assessment → 03Test a bounded workflow with real users, human review, and known metrics.
View the Pilot → 04Extend what demonstrates value, document its use, and establish clear responsibilities.
View Scale and Adoption →Speed is not enough when an output can affect a proposal, a customer relationship, or a commercial decision.
Define which information may feed the workflow and which information must remain outside it.
Decisions, commitments, and risks retain an identifiable human owner.
Access is aligned with each person’s actual work and the sensitivity of the information.
Use and results are reviewed so the workflow can be adjusted before its scope is expanded.
Commercial AI is not a chatbot: it is a way of working built around information, decisions, and follow-up.
Share the commercial problem, the users involved, and the decision that needs to improve. The initial fit check helps determine whether there is a fit and what scope would be appropriate.
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