Productboard is a Business Reporter clientSpeed without strategy is just a faster way to build the wrong thing – the product teams winning are not the ones shipping most but those with the strongest inputs.Your engineering team is moving faster than ever. The roadmap looks full, sprint velocity is up and AI coding tools are turning ideas into working software overnight. And yet, in your last business review, the features your team celebrated internally were not the ones customers actually use. The output has multiplied; the outcomes have not. Despite US businesses pouring an estimated $35 to $40 billion into generative AI, independent research has found that 95 per cent of pilots deliver no measurable P&L impact, and a global management consultancy found that 75 per cent of CTOs are not hitting ROI expectations on software development. The organisations that will win aren’t the ones with the fastest pipelines – they’re the ones with the strongest product inputs: discovery rigour, customer understanding, and cross-functional alignment that ensures AI is pointed in the right direction before it starts building. The illusion of progress The productivity lift is real. According to a 2025 software engineering report, 63 per cent of engineering organisations ship code more frequently since adopting AI tools. But the same report finds that 45 per cent of deployments involving AI-generated code lead to problems, and 72 per cent have suffered a production incident. But even tighter reviews, better testing and more governance cannot answer the more important question: should this have been built at all? What AI-accelerated development changes is not whether features get used, it’s how many unused ones ship. More features, faster, with the same underlying rate of customer indifference. The rise of product slopProduct slop is what this build-up looks like internally. It’s not necessarily buggy software – product slop is technically sound, carefully developed and yet entirely missing the point. Think of a filtering interface built because the PM assumed frustration, when the data showed customers had stopped using that section altogether. Each one ships cleanly, appears in the changelog and largely gathers dust. A prototype only creates value when teams build to learn, not to ship – and AI has made that distinction easier than ever to ignore. When plausibility keeps being mistaken for proof, the result is discovery debt: unvalidated assumptions hardened into roadmap commitments before anyone tested whether they were true. Daniel Hejl, co-founder and Chief AI Officer at Productboard, has watched this directly. “AI assumes you’re working on an average product for an average company,” he observes. “The outputs feel generic – like something a business professor would write rather than someone who deeply understands your customers.” The quality ceiling of an AI-assisted process is set by what goes in, and the answer is stronger product inputs. How product discovery evolves in the AI era Product discovery fails at three stages under velocity pressure:Gathering: defaulting to internal experts rather than the full signal set – usage data, sales patterns, competitor intelligence and real customer conversations Analysis: converging on solutions before separating symptoms from root causes Framing: building the business case to justify a decision already made, rather than to earn genuine confidence in one still open Fixing this starts with visibility. Product leadership practitioners recommend mapping your discovery process cross-functionally – every step, every handoff, every wait-state between insight and launch. Most organisations find around 50 steps, and that map alone is often enough to create momentum with senior leadership. The follow-on question is which steps genuinely require human judgment, and which can be automated. Some organisations have compressed a discovery process that previously took five or six months down to a week by being rigorous about that distinction. What AI changes is the speed of the automatable work – synthesising customer feedback, aggregating usage data, monitoring competitor moves – freeing up the work that can’t be automated: talking to customers, separating real problems from symptoms, building the commercially grounded case that earns confidence. A 2025 Productboard survey found that 94 per cent of enterprise PMs use AI daily, but the gap between those who find it transformative and those who find it merely useful comes down almost entirely to input quality, not tool sophistication. The spec is the new conversation That shared understanding has a name. It’s the spec – and in an AI-native development environment, getting it right is the difference between a team that ships value and one that ships fast. The consensus that held for two decades – write less, talk more, iterate fast – has collapsed. When your delivery mechanism is an AI coding agent, it has no memory of past decisions, no ability to infer intent, and no judgment to flag when a spec is dangerously underspecified. As Hejl puts it: “The knowledge has to be in the spec. Pricing plan, analytics events, onboarding impact. These are not details you resolve in a hallway conversation when your coding agent is building at three in the morning.” AI is also changing how specs get written – the same synthesis capabilities that accelerate discovery can now generate first drafts grounded in real customer evidence, surface commonly missed sections, and flag gaps before they reach engineering. Productboard Spark is built on this premise: an agentic platform that maintains context across decisions and sessions, synthesises customer feedback at scale and generates specifications that stay connected to the discovery that preceded them. In the AI era, the spec is not a bureaucratic formality but the moment where product thinking becomes executable. The bottleneck has moved: have you? Every organisation now has access to the same AI coding tools. The differentiator is who knows most clearly what to build. What AI has changed is the cost of getting it wrong – bad prioritisation used to produce one wasted sprint, now it produces a wasted quarter, shipped at speed. The product leaders who get this right are building something that compounds: discovery that is rigorous, customer understanding that runs continuously and specifications that actually reflect what customers need. Features get used, retention improves, and the roadmap earns trust because it keeps proving itself externally. The question is not whether your team is using AI. It is whether you have built the discipline upstream that makes the machine worth running.Explore how modern product teams are enhancing their product strategy with Productboard Spark.