Transformation 05 / AI product discovery
The project began with possibilities, not requirements.
I led discovery that reduced broad AI enthusiasm into a coherent workflow, product boundary, and direction toward implementation.
scale0implementation-ready requirements at the start
Discovery began before a shared, buildable product definition existed.
scale6connected discovery modes
Interviews, workshops, workflow framing, assumption mapping, feasibility interpretation, and solution direction.
scale1coherent direction toward implementation
The result was a product direction, not a claim of a completed production AI system.
reportedContinuingproduct-development engagement
Discovery contributed to continuation. It did not independently secure or deliver the whole engagement.
The actual project
What the business needed, and what I delivered.
- The business
- A client wanted to pursue an AI product but entered discovery with broad possibilities and no shared definition of the user task, information boundary, trust model, or buildable first release.
- The product mandate
- Run a pre-study that could reduce the idea space into a coherent workflow, expose assumptions, test feasibility early, and give product development a direction it could actually evaluate.
- My work
- I led stakeholder interviews and workshops, framed the user decision and workflow, mapped assumptions and information needs, brought feasibility and human oversight into scope discussions, and organized the output for delivery planning.
- Business effect
- The engagement moved from open-ended AI enthusiasm to one implementation-oriented direction. The discovery contributed to a continuing product-development relationship; it is not claimed as an independently secured commercial result.
Act 1 / The pressure
Good discovery should remove possibilities.
Change one condition and watch the consequence travel through the system. The model uses representative data and preserves the real decision.
Help an account team prepare a client brief
Act 2 / The model
The workflow had to come before the model.
Stakeholders could describe what AI might do, but not yet the workflow, user decision, information boundary, or feasible first version. Every conversation could create another attractive feature while hiding incompatible expectations.
The purpose of discovery was therefore subtraction. Each question removed ideas that lacked a clear user task, information basis, trust model, or implementation path.
- 01User task
- 02Information
- 03Trust boundary
- 04Feasibility
Underlying structure: I led discovery that reduced broad AI enthusiasm into a coherent workflow, product boundary, and direction toward implementation.
Act 3 / The decisions
The product changed when the decisions became explicit.
Start with the workflow
The pressure. Model capability was being treated as the product definition.
The move. Frame opportunities around a user task and operational decision.
Inspect the tradeoff
Rejected: Choose a model first and search for a workflow afterward.
Consequence: Technical possibility stayed connected to a job worth improving.
Make assumptions visible
The pressure. Stakeholders used the same AI language to describe different expectations.
The move. Externalize assumptions through structured questions and workflow models.
Inspect the tradeoff
Consequence: The group could disagree with something concrete instead of nodding at a concept.
Bring feasibility into discovery
The pressure. A polished concept could survive without information or oversight needed to operate safely.
The move. Test data, trust, human review, and implementation constraints while scope was fluid.
Inspect the tradeoff
Consequence: The direction became smaller and more credible.
Define the transition to delivery
The pressure. Open-ended discovery can produce insight without a decision about what happens next.
The move. Organize findings into an implementation-oriented direction and remaining questions.
Inspect the tradeoff
Consequence: The engagement could move toward product development with uncertainty named.
Contribution boundary
My work turned interpretation into buildable behavior.
What I contributed
- Led pre-study and discovery activities.
- Facilitated stakeholder and solution-framing discussions.
- Interpreted AI capabilities through operational workflows.
- Structured requirements, assumptions, and feasibility questions.
- Supported client onboarding and the transition toward delivery planning.
What remained outside my ownership
- I did not independently build the resulting AI product or own all technical feasibility decisions.
- Continuation was a combined commercial and product outcome to which discovery contributed.
Act 4 / The change
A cloud of AI ideas became one direction that could be challenged.
Evidence-qualified outcomeDiscovery contributed to a continuing product-development engagement
The engagement moved from broad AI exploration toward a clearer implementation-oriented direction. Ideas were filtered through task, information, trust, and feasibility rather than accumulated into a feature catalogue.
That discovery contributed to the relationship continuing as a product-development engagement. The case makes no fabricated accuracy or productivity claim.
Read the evidence boundary
The idea field and workflow are representative. No proprietary prompts, models, client data, or commercial terms are shown.
Act 5 / The lesson
A polished concept can make a room feel aligned before anyone agrees on the problem. Discovery becomes valuable when assumptions are concrete enough to disagree with.
Start a conversation
What are you trying to untangle?
Bring the unclear process. We can make the next decision obvious.