Axulu

Tag: For the CPO

  • Build, Buy, or Wait: How to Decide Your Product’s AI Strategy

    Written for the CPO.

    Every product leader is being pushed to “add AI.” The more valuable question is build, buy, or wait — and on what basis. For a CPO, that decision, made deliberately, is what keeps the roadmap serving users rather than chasing headlines.

    There’s enormous pressure on product leaders right now to put AI into the product. Boards ask for it, competitors announce it, the market rewards the story. But the pressure to ship an AI feature is not the same as a good reason to ship one — and a CPO’s job is to tell the difference, then decide deliberately between building, buying, and waiting.

    Because the wrong AI feature, shipped for the wrong reason, doesn’t just waste roadmap. It can add risk, erode trust, and distract from the problems that actually matter to users.

    Start with the real question

    The seductive question is “how do we add AI?” The right question is “does an AI capability solve a genuine user problem better than the alternative — and are we ready to build and support it responsibly?” That reframing matters, because it puts the user problem, not the technology, at the centre of the decision. AI is a means; a solved user problem is the end. Any AI roadmap that starts from “we need an AI feature” rather than “here’s a problem AI is uniquely good at” tends to produce features that demo well and matter little.

    Build, buy, or wait

    With the user problem established, the strategic choice resolves into three honest options:

    Build when the AI capability is genuinely core to your differentiation, and you have — or can realistically acquire — the data, the talent and the product and technical foundations to own it and support it over time. Building is the right call when the capability is the product’s edge, and a serious one because you own the reliability, the data handling and the ongoing cost.

    Buy (or integrate) when the capability is valuable but not differentiating. Someone else’s model or component, integrated well and governed properly, is usually faster, cheaper and safer than reinventing it. Most AI in most products should be bought or integrated, not built from scratch — the differentiation is in the product experience around it, not the model.

    Wait when the user problem isn’t real or pressing yet, when your foundations aren’t ready, or when the honest driver is trend-chasing rather than user value. Waiting is a legitimate, often wise strategic choice — and the discipline to say it is what separates a product strategy from a reaction to the news cycle.

    The warning that applies to all three

    Whichever you choose, one principle holds: AI amplifies your product foundations rather than fixing them. Bolt AI onto weak data, unclear ownership, or a shaky roadmap and you accelerate the weakness. And pilots flatter — a compelling internal demo of an AI feature tells you little about how it behaves in production with real users, real data and real scale. The build/buy/wait decision has to account for whether your foundations can actually support the choice, not just whether the demo impressed.

    The leadership question

    The CPO’s question isn’t “what AI feature should we ship?” It’s: what genuine user problem would AI solve better than the alternative — and should we build it, buy it, or wait, given our foundations and readiness?

    Try this prompt

    Structure the decision for a candidate capability:

    “Act as a pragmatic Chief Product Officer. We’re considering an AI capability: [describe the user problem and the proposed feature]. Help me decide build, buy, or wait. For build: what data, talent and foundations would we need. For buy: what to integrate and govern. For wait: what would make this premature. Then challenge whether this solves a real user problem or just chases the trend, and whether our foundations could support it.”

    What to do next

    Make the build/buy/wait call explicitly for each AI capability on your roadmap, anchored to a real user problem and an honest read of your foundations. Default to buy/integrate for the non-differentiating, reserve build for genuine edge, and have the discipline to wait where the case isn’t there. That deliberate sorting is what turns “add AI” pressure into a coherent, defensible product strategy.

    In closing

    For a CPO, the AI question isn’t whether to add it — it’s whether a given capability serves a real user problem, and whether to build, buy, or wait. Decided deliberately, that keeps your roadmap serving users. Decided by market pressure, it produces features that impress in a demo and disappoint in production.

    If your product leadership would value help making the build/buy/wait decision well — anchored to user value, foundations and responsible delivery — that’s exactly the conversation Savant and Axulu are set up to have, with the technical, data and security depth these decisions increasingly require, including fractional product-technology leadership where it helps.

  • Shipping AI Features Without Shipping Risk

    Written for the CPO.

    Adding an AI feature is easy; shipping one you can stand behind is the real work. For a product leader, that means designing for the failure modes — data, hallucination, liability, testing — before the feature reaches users.

    Every product leader is under pressure to put AI in the product. The demand is real and the demos are seductive: bolt on a capable model, show something impressive, capture the market’s enthusiasm. The trouble is that the distance between a compelling demo and a feature you can responsibly ship to real users is large — and it’s exactly the distance a CPO is accountable for closing.

    Because when AI is in the product, its mistakes aren’t internal drafts a colleague catches. They’re your brand, speaking to your customers, sometimes with liability attached.

    Why product AI is a different risk class

    Internal AI use is forgiving: a human reviews the output before it matters. AI in the product removes that buffer — the output goes straight to the user. That changes the risk class entirely:

    Confident wrongness reaches the customer. AI can be fluently, persuasively wrong. In an internal draft that’s a nuisance; in your product it’s your brand telling a customer something untrue — and depending on the domain, that can carry real liability.

    Customer data exposure. An AI feature often needs user data to work. How that data is handled, where it’s processed, and whether it trains anything are product decisions with privacy and trust consequences.

    Autonomy widens the blast radius. If the feature can act — not just answer — then a misfire does something, not just says something. The principle holds: the system is only as safe as its constraints.

    Designing for the failure modes

    Shipping AI responsibly means treating the failure modes as first-class design work, not an afterthought:

    Map where it can be confidently wrong, and the consequence. Design the guardrails, disclaimers, confidence signalling and fallbacks around those specific failure modes.

    Keep a human in the loop where stakes are high. For consequential outputs, design the feature so a person confirms before it acts — or so the user is clearly the decision-maker.

    Handle data deliberately. Decide what user data the feature uses, how it’s protected, and what you’ll tell users — before launch, not after an incident.

    Contain anything agentic. If the feature takes actions, scope tightly what it’s allowed to do and gate the irreversible.

    Test against production reality. This is the one most often skipped. Pilots lie — a controlled demo with clean inputs looks wonderful; production, with messy real data, full volume and edge cases, tells the truth. Test where the truth is.

    An honest caveat: no design guarantees a specific safety, privacy or legal outcome, and this isn’t legal advice. The aim is a defensible, controlled feature — one whose failure modes you’ve anticipated and can stand behind.

    The leadership question

    Before an AI feature ships: what’s the worst thing it could tell or do to a user, how likely is it, and what in the design prevents or contains it? If the honest answer is “we’re relying on the model being right,” it isn’t ready.

    Try this prompt

    Pressure-test a feature concept:

    “Act as a cautious head of product and a sceptical security reviewer in turn. Here’s an AI feature we’re considering shipping: [describe it, including what data it uses and whether it can take actions]. Identify where it could be confidently wrong and the consequence, what customer-data risks it introduces, where a human must stay in the loop, what must be contained, and what we must test in production rather than a demo. Be pessimistic.”

    What to do next

    Treat the failure-mode design as part of the feature, not a compliance step at the end. Decide the human-in-the-loop points, the data handling and the containment up front, and insist on testing against real production conditions before a wide release. Ship the version you can stand behind, then expand.

    In closing

    For a CPO, the AI opportunity in the product is real — and so is the responsibility. The leaders who ship AI features that last are the ones who designed for the failure modes before launch, not the ones who shipped the demo and hoped.

    If your product leadership would value a session on shipping AI features responsibly — data, hallucination, human-in-the-loop, containment and real-world testing — that’s exactly the conversation Savant and Axulu are built for, with the technical and security depth product decisions increasingly need.

  • From Roadmap to Real: What AI Can Do for Your Product and Your Product Team

    Written for the CPO.

    For a product leader, AI is two opportunities in one word: AI in the product your users touch, and AI for how your team discovers, decides and ships. The second is the faster, lower-risk win — and most CPOs are under-using it.

    Every CPO is fielding the same pressure right now: what’s our AI story? Usually the question means AI features — something users interact with. That matters, and it’s where the strategic and competitive stakes are highest. But it’s only half the opportunity, and fixating on it means overlooking the half that pays back fastest with the least risk: AI for the product team — the way you run discovery, decisions and delivery.

    AI for the product team

    This is where the immediate, low-risk value sits, because it’s internal and human-reviewed:

    Feedback and research synthesis at scale. Hundreds of support tickets, survey responses, interview transcripts and reviews condensed into the themes that actually matter — the kind of synthesis a team rarely has time to do properly. It surfaces signal you’re currently missing.

    Discovery acceleration. Turn discovery calls into structured insight, cluster problems, and draft the first version of a PRD or spec from a rough brief so your PMs start from a draft rather than a blank page.

    Prototyping in hours. Stand up a working prototype to pressure-test a concept before committing engineering time. A prototype becomes a cheap question rather than an expensive bet — which changes how boldly you can explore.

    Analytics you can talk to. Interrogate product data in plain language — “where are users dropping out of this flow, and what changed?” — instead of queuing behind a data request.

    Competitive intelligence, fast. Pull together a current view of the landscape in a morning rather than a week.

    The net effect is your roadmap moving faster from idea to evidence — and evidence, not opinion, is the scarce commodity in product decisions.

    AI in the product

    The larger prize is AI your users touch — and it deserves its own serious treatment, because shipping AI features carries real responsibilities around reliability, data and liability that internal use doesn’t. The important point here is one of sequencing: the disciplines you build using AI internally — knowing where it’s confidently wrong, where a human must own the output, how to test it against reality rather than a demo — are exactly the disciplines you’ll need to ship AI features well. Getting fluent with AI for the team is how a product organisation earns the judgement to put AI in the product responsibly. (That deployment question deserves its own conversation; treat this as the on-ramp.)

    The discipline, even internally

    Even the low-risk internal wins reward judgement. AI’s synthesis is fast and confident and occasionally confidently wrong — it will invent a theme that isn’t there or over-weight a vocal minority. A product mind still owns the interpretation. And a prototype is a question, not a decision: it tests whether an idea has legs, not whether it’s the right thing to build. Used that way, AI sharpens product judgement; mistaken for a replacement, it launders assumptions into false confidence.

    The leadership question

    For a CPO: where is my team spending scarce time on synthesis, drafting and prototyping that AI could accelerate — freeing them to do the judgement work only they can — and are we building the internal fluency we’ll need before we put AI in front of users?

    Try this prompt

    Put it to work on real (non-confidential) feedback:

    “Act as a senior product researcher. Here is a batch of user feedback: [paste anonymised feedback]. Cluster it into the themes that matter, rank them by apparent frequency and severity, flag where you’re inferring rather than certain, and suggest the three product questions this raises. Then tell me what a vocal minority might be distorting.”

    It shows, on your own data, how much faster idea-to-evidence can be — with the caveats built in.

    What to do next

    Start with the internal wins — feedback synthesis and prototyping are the usual highest-return entry points — with a PM owning the interpretation. Build the team’s fluency and judgement there. That capability is both an immediate roadmap accelerator and the foundation for deciding, later and responsibly, where AI belongs in the product.

    In closing

    For a product leader, the fastest AI value isn’t a feature — it’s a product organisation that gets from idea to evidence faster, and in doing so earns the judgement to ship AI to users well.

    If your product leadership would value a session on both halves — AI for the team now, and what it takes to put AI in the product responsibly — that’s exactly the conversation Savant and Axulu are set up to have, with technical and security depth on tap where the in-product questions get real.