Agentic product strategy
Choose the customer problem and product role an agent should serve
An agent feature is proposed primarily because competitors mention AI. The decision is how to choose the customer problem and product role an agent should serve. The work begins with your actual workflow and looks for the evidence that separates intended behaviour from a consequential exception. It is particularly relevant to product executives and discovery teams. A useful outcome is an explicit account of what was examined, what remains uncertain, and which decision the evidence can support.
The operating choices relationship
- Customer problem evidence
Establish the basis for user problem.
- Interaction choice
Compare conversational and simpler structured options.
- Agentic product strategy
Retain the scoped finding and its unresolved dependencies.
Decision
Frame the decision around user problem
Questions and the evidence they need
- User problem
- Inspect customer problem evidence for the relevant constraint or observation.
- Interaction choice
- Inspect current journeys for the relevant constraint or observation.
- Validation threshold
- Inspect product economics for the relevant constraint or observation.
The decision is how to choose the customer problem and product role an agent should serve. This is relevant to product executives and discovery teams when an agent feature is proposed primarily because competitors mention AI. Name the affected workflow, the consequence of a wrong decision, and the owner who can act on the result. The initial discussion should establish which evidence is already available and which uncertainty is material enough to investigate.
The specific failure hypothesis is that agent novelty drives feature scope while the user’s actual decision or task remains poorly defined. It remains a hypothesis until examined in the deployment. Existing internal tests or operating records may already resolve it; identify the uncovered question before commissioning additional work.
Mechanism
Product strategy should test the customer's job
Relationships within operating choices
- User problem
- Ground the proposed agent in observed customer difficulty.
- Interaction choice
- Compare conversational and simpler structured options.
- Validation threshold
- Define what would support or reject the product hypothesis.
What this specialty examines
Agentic product strategy should identify the user’s job and the decision the product helps complete. Compare conversational flexibility with structured flows and existing features. The presence of a model or an agent should not become the product’s value proposition by itself.
The review should frame user jobs, compare simpler alternatives and test a bounded product hypothesis. Identify the normal route, relevant exceptions and the authoritative record of the outcome. Inspect the relevant business-system record, enforcement event or process evidence alongside any reported status. Keep incomplete cases visible, with an explanation of what their absence prevents the owner from concluding.
The tradeoff to make explicit
For this decision, conversational flexibility can support exploration while structured interactions may suit tightly bounded tasks. Compare the available design or operating choices against the same intended outcome. If one option removes a required control or changes the task boundary, describe that difference before treating its apparent simplicity, speed or lower effort as an improvement. The decision should expose the tradeoff instead of burying it in an aggregate score.
Preparation
Connect the working inputs to the boundary being examined
Evidence packet for user problem
Illustrative working format
- Customer problem evidence
- Identify provenance and the specific decision this material can inform.
- Current journeys
- Check its relationship to the relevant workflow and expose missing context.
- Product economics
- Keep workload, quality, human effort, and uncertain inputs visible in comparisons.
- Support constraints
- Identify provenance and the specific decision this material can inform.
Start with customer problem evidence, current journeys, product economics and support constraints. Identify the owner, revision and operating context of each source. Sanitised examples may support initial design, but differences from actual conditions should remain explicit. Where direct access is unsuitable, a customer-controlled evidence or execution route may be considered, with agreed handling and retention.
The scope also needs the business sponsor, agreed expectations for a representative business hypothesis, and the authority to inspect the relevant boundary. Specify what evidence would distinguish the failure in which agent novelty drives feature scope while the user’s actual decision or task remains poorly defined from acceptable behaviour. If stakeholders disagree about intended behaviour, resolve the interpretation before classifying the observed difference as a defect.
How the work proceeds
Carry user problem through to an accountable finding
From the scoped material to a decision record
- Customer problem evidence
Establish the existing process and its constraints.
- Interaction choice
Apply the scoped check to validation threshold.
- Agentic product strategy
Record the evidence, open dependencies, and the owner’s next decision.
Establish the problem and comparison baseline
Bring customer problem evidence, current journeys, product economics and support constraints into a working session with product executives and discovery teams. Separate observed process conditions from stakeholder preferences and future estimates. Describe the current approach and at least one credible alternative, including simpler process improvement where relevant. The preparation output is a comparison brief with the unresolved assumptions, accountable owner and observations that would justify advancing or rejecting the proposed option.
Examine options and challenge assumptions
Test a bounded problem hypothesis with representative users and clear success criteria. Examine quality, controllability, support effort and willingness to use the proposed interaction. Interest in a demonstration does not establish durable adoption or willingness to pay.
Choose the next bounded commitment
Develop an agentic product strategy with problem framing, scope and validation criteria from the examined alternatives. Distinguish immediate dependencies from questions that a bounded experiment can answer, and name who owns the operating or benefit measure. Recommend an advance, hold, revise or stop decision with a specific next observation. Any later expansion should depend on the agreed learning result and available capacity, rather than the completion of planning activities alone.
Indicative artefact
Make the agentic product strategy usable by its owner
Agentic product strategy
Illustrative working format
- Agentic product strategy
- Identify the assessed workflow, revision, and decision boundary.
- Problem framing
- Link supporting and contrary observations to inspectable evidence.
- Scope
- Name the unresolved condition, next action, and accountable owner.
A useful decision artefact is an agentic product strategy with problem framing, scope and validation criteria. Supporting material may include selected cases, configuration or process records, evidence references, a disagreement log and prioritised actions. Retain the case selection rationale and material disagreements alongside the result; otherwise the result may be mistaken for complete coverage.
Illustrative scenario
Examine validation threshold through a constructed exception
A constructed case and its decision paths
Illustrative scenario
- User problem
A chat interface is proposed for a narrowly defined reconciliation problem.
- Interaction choice
Compare the chat concept with a focused structured reconciliation flow.
- Validation threshold
Reject the agent interface if it does not improve the evidenced customer job.
Consider a fictional situation: a software product adds a chat interface although customers mainly struggle with a specific reconciliation step. The scenario is illustrative, with no client engagement, executed test or measured improvement implied.
For reconciliation, a focused interface with clear discrepancies and approvals may suit users better than open-ended chat. An agent could still perform supporting analysis. The product decision should follow the task evidence rather than a requirement to expose an agent persona.
Scope and next action
Keep the conclusion within the stated business hypothesis
Confirm fit before the next commitment
- User problem
- This review is limited to the stated business hypothesis.
- Customer problem evidence
- The relevant owner must confirm access and the intended operating conditions.
- Agentic product strategy
- Commitment depends on the sponsor’s evidence review.
A strategy or business-case model is decision support. It does not establish demand, guarantee financial returns, commit staffing or authorise organisational changes. Any engagement depends on an agreed scope, qualified team, authorised access and confirmed availability. Bring the affected workflow, its current configuration or process, the triggering change and the evidence already available. The immediate scope question is prompted by the situation in which an agent feature is proposed primarily because competitors mention AI; confirm which missing observation would change the owner’s next action.
Ways to engage
Related engagement scopes
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AI Agentic Vendor Selection
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AI Transformation Sprint
Move from an interesting use case to a deliberate adoption decision. Connect workflow value, organisational readiness and the changes needed to take the next step.
Continue exploring
Related insights and resources
Choose the work before the agent
Compare agentic work with simpler alternatives, expose the dependencies behind a promising demonstration, and fund a bounded experiment with a clear decision at its end.
Measure the work that improves
Connect agent activity to accepted business outcomes, establish a credible comparison, and distinguish useful capacity from realized financial value before deciding whether to scale.
Before the pilot: five questions for agent readiness
A compelling demo leaves important questions open. Use a five-dimension rubric to examine the data, authority, evidence, ownership and people behind your next agent initiative.