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Questions from Austin SaaS product teams.

What does a production-ready AI product spec include that a regular feature spec does not?

A production-ready AI product spec adds five sections that a standard feature spec omits. First, an evaluation framework: the metrics that define whether the AI output is good enough to ship, the labeled dataset used to measure those metrics, and the pass threshold. Second, a failure mode matrix: what the feature does when the model returns low confidence, an API times out, or the input is outside the expected distribution. Third, a build/buy/fine-tune decision log: a written record of which model approach was selected and why, including the alternatives evaluated. Fourth, a monitoring spec: what signals are tracked post-launch, what threshold triggers a review, and who owns the review. Fifth, a model versioning policy: what happens to users when the underlying model version changes and how model updates are tested before deployment. These sections prevent the four most common causes of AI feature stalls: undefined success criteria, unhandled edge cases, silent model regressions, and vendor lock-in without an exit path.

How do you prioritize which AI features to spec first when you have 8 ideas and engineering capacity for 2?

Prioritizing AI features uses a four-part filter. First, data readiness: does the company have labeled data or sufficient interaction data to train or evaluate a model for this feature? Features with no data path have a longer lead time than they appear. Second, failure cost: if the AI output is wrong, what is the consequence for the user and for the business? Low failure cost features are better candidates for early shipping than high failure cost features, because early AI features will have errors. Third, competitive differentiation: is this feature table stakes (every competitor has it) or a genuine differentiator? Table stakes features can often be addressed with third-party APIs rather than custom model work. Fourth, engineering dependency: does this feature require infrastructure that does not exist yet? A Q2 Holdings team adding AI to a lending product needs a data pipeline before any model work starts. Features with missing infrastructure upstream are not first-in-queue candidates.

What does vendor evaluation for an AI feature look like. How long does it take?

Vendor evaluation for an AI feature takes 2–3 weeks when run in parallel with spec work. The evaluation covers four dimensions. Capability fit: does the vendor's model perform acceptably on a sample of your specific use case data? Generic benchmark scores from the vendor's website are not a substitute for running your data against their API. Pricing model: what is the cost per call at your projected usage volume in 12 months? Several vendors price favorably at low volume and become expensive above 10 million calls per month. Data handling: where are your inputs processed, does the vendor use your data to train their models, and what is the data retention policy? For a WP Engine or Procore customer, these questions are non-negotiable. Exit path: how hard is it to swap this vendor if their pricing changes or their model quality degrades? Evaluations that skip the exit path produce vendor dependencies that are expensive to unwind. The output of vendor evaluation is a written decision memo, not just a verbal recommendation.

How do you hand off to a full-time AI PM. What does the handover package include?

A handover package for an incoming full-time AI PM includes five items. First, the AI roadmap: a prioritized list of the next 6–12 AI features with a one-paragraph brief on each covering the business case, data requirements, and the build/buy/fine-tune recommendation. Second, the active product specs: every spec produced during the fractional engagement in the company's standard spec format, with the evaluation framework and failure mode matrix included. Third, vendor contracts and evaluation memos: a summary of every vendor evaluated, the decision made, and the contractual terms that apply. Fourth, the evaluation framework and labeled test sets: the datasets built during the engagement and the methodology used to evaluate model quality, so the incoming PM can apply the same standard. Fifth, a 30-day brief: a written overview of the team dynamics, the stakeholders who need to be in each type of decision, and the open questions that remain unresolved at handoff date. A handover package without the labeled test sets forces the incoming PM to rebuild evaluation infrastructure that already exists.