Product strategy

Acts as a product manager for AI features who starts from the user problem, defines quality with evals, designs for wrong answers and uncertainty, and watches cost, latency and user trust.

Reframes a nonprofit or public programme as a product - who it serves, the outcome they need, evidence, riskiest assumptions and small tests - adapted to funder reporting and volunteer capacity.

Assesses how general AI tools and AI-native competitors could erode a product's value - task by task - separating real threats from hype, and plans defensive and offensive moves with signals to watch.

Assesses how exposed a product is to a platform it depends on - app store, marketplace, API, AI model vendor or social network - scoring each dependency and planning mitigations and early warnings.

Assesses product-market fit from retention curves, the very disappointed survey, usage depth and qualitative signals, by segment, and recommends what to do next.

Audits a product's features on usage, revenue tie, support cost and complexity, and recommends which to cut, merge, hide or keep, with the evidence and the customers affected.

Lays out all current and proposed product bets by horizon, risk and expected payoff, shows where capacity actually goes, and recommends a target mix with what to start, scale, pause or stop.

Chooses the first customer segment for an early product by scoring candidates on pain, reachability, ability to pay, word of mouth, competition and expansion path.

Chooses between self-serve, inside sales, field sales and partner-led selling from contract size, buyer, complexity and onboarding needs, with what each demands of the product.

Decides whether to sell several products as one suite, separately or both - by buyer, usage overlap, pricing, sales motion and engineering cost - with a recommendation and migration plan.

Decides whether a product should specialise for one industry or stay general, from win rates, retention and needs by segment, and spells out what the choice changes in roadmap and sales.

Sorts a competitor feature gap list into match, beat, ignore or reframe using lost-deal evidence, customer jobs, strategy and cost, so the team stops chasing parity lists.

Cuts a feature list down to the smallest testable MVP, with the riskiest hypotheses, success criteria set before launch, the cheapest MVP type and a deferred list with re-entry triggers.

Designs a free plan, free trial or reverse trial with limits tied to the value metric, conversion triggers, abuse controls, cost to serve and the metrics to judge it.

Designs a product-led growth motion for a sales-led product - self-serve signup, time to value, upgrade paths, product-qualified signals for sales and the team changes it needs.

Designs how to split product teams - by customer journey, segment, platform or capability - with ownership boundaries, dependencies, metrics per team and when to change it.

Evaluates whether a company should build a second product next to its core one - customer overlap, right to win, distraction cost, core health gate - and recommends go, test first, or not yet.

Evaluates whether and where to add an AI feature, covering problem fit, quality bar and evals, failure modes, cost, trust and a staged rollout, ending in a build, shrink or skip verdict.

Compares building, buying or adopting open source for a capability on total cost, time to value, strategic fit, lock-in and risk, then recommends one with triggers for revisiting the decision.

Decides whether to build a feature a big customer or deal demands, weighing revenue, reuse by others, roadmap cost, precedent and alternatives to a custom build.

Evaluates whether and how to open-source a product or component, weighing goals, scope, licence models, competition, community expectations, maintenance cost and business model fit.

Acts as a product manager for physical products who thinks in BOM cost, margin stack, design for manufacture, tooling lead times, EVT/DVT/PVT gates, certification, packaging and returns.

Maps a subscriber's journey from trial through activation, conversion, expansion and renewal, with the metric, triggers, touchpoints and risk signals per stage and the moments that move revenue.

Turns an hourly or ad-hoc service into two to four packages with hour budgets, scope boundaries, add-ons, a margin and capacity check per tier, and a plan to move existing clients across.

Plans how to reduce churn in a subscription product by splitting churn into its causes and ranking product, pricing, onboarding and payment levers by impact and effort.

Plans a response to a competitor's launch or price move with a facts check, impact by segment, ranked options, internal and customer messaging, a watch plan and what not to do.

Builds a ranked menu of expansion plays - seat growth, tier upgrades, add-ons, usage overage and services - each with trigger, segment, message and expected impact, plus an NRR target.

Plans retiring a feature with user and revenue impact, migration paths, a dated timeline, communications by segment, support preparation and data handling. Use when reducing product surface.

Decides whether to invest in, maintain, harvest or retire a legacy software product with an installed base, and plans pricing, support, staffing and migration for that choice.

Plans serving smaller customers with a simpler self-serve offer - what to strip out, price points, onboarding without sales, support at low cost and cannibalisation risk.

Plans moving a product to larger customers - enterprise requirements, sales and support changes, pricing, readiness gaps and the cost to existing customers and the roadmap.

Plans a platform or API product strategy covering ecosystem participants, value exchange, cold start, developer experience, monetisation, governance and a phased plan with metrics.

Plans discontinuing a physical product with a last-time buy, stock run-down, spare parts and repair support, obligations to check, firmware or app support, take-back and customer communication.

Plans how to combine an acquired product with your own - overlap map, keep, merge or retire decisions, customer migration, a unified roadmap and naming.

Plans how a challenger product wins against a dominant incumbent - the underserved segment, the wedge, where not to fight, switching costs and the incumbent's likely response.

Prepares a product manager for an executive product review with the narrative, an opening, metrics against goals, framed decisions, likely hard questions with answers and a pre-wire plan.

Prices an AI feature whose cost grows with usage, choosing between bundling, credits, usage limits and add-ons, with margin per user, heavy-user risk and abuse controls.

Takes a pricing change through gated steps, from research and options to an impact model, a communication plan and a rollout review, pausing for the owner's approval between steps.

Scores candidate countries, regions or segments on demand evidence, competition, localisation and regulatory effort, channel access and fit, then recommends a sequence and an entry test.

Ranks candidate third-party integrations for a software product by deals and churn they affect, customer overlap, depth needed, build route and upkeep cost.

Reviews a range of products or SKUs on revenue, margin, growth, strategic role, complexity cost and cannibalisation, recommending keep, fix, reposition or cut with a transition plan.

Runs product due diligence on a company being acquired or invested in - users, usage, retention, roadmap, product-market fit, dependencies and red flags - with questions to ask.

Tears down a product's onboarding, value moments, pricing, retention and growth mechanics, separates observed facts from inference, and turns them into lessons for your own product.

Sets kill criteria for a product bet before results arrive, with leading indicators, continue, rethink and stop thresholds, review dates, decision rights and a wind-down outline.

Works back from target retail price through channel margins, sales tax, shipping, returns and warranty reserve to a target landed and BOM cost, showing the gap and levers to close it.

Red-teams a written product strategy - diagnosis, hidden assumptions, coherence of actions, competitor responses and failure modes - and returns the hardest questions and fixes.

Writes requirements for an AI feature covering the user problem, behaviour with good and bad output examples, a quality bar, evals, failure handling, data, safety, cost and launch criteria.

Writes a working-backwards press release and FAQ for a proposed product, with customer and internal FAQs that expose the hard questions. Use when pitching a new initiative.

Writes five to seven ranked product principles a team can use to settle trade-offs, each with its meaning, what it rules out and an example decision, tested against real debates.

Writes a one-page product strategy with a diagnosis of the core challenge, a guiding policy, coherent actions and an explicit list of what the team will not do.

Writes a memorable three-to-five-year product vision covering the customer's future, the change the product makes, guiding principles and what it means for next year's work.

Writes a Shape Up pitch with the problem, the appetite, a fat-marker solution described in words, rabbit holes with patches and explicit no-gos. For teams using fixed-time, variable-scope cycles.