Too many AI pilots end up as well-intentioned slides or dusty prototypes. The technology gets praised, the metrics glow in a lab environment, and then the organisation asks the familiar question: why hasn’t this created value at scale?
Why pilots stall — and what product leaders must notice first
Pilots succeed at proving a capability; production delivers ongoing value. The trap is treating a pilot as an engineering milestone rather than a product hypothesis. Three frequent failure modes:
- Mismatched success metrics — a pilot is judged by technical accuracy, not sustained user behaviour or commercial outcomes.
- Missing operational foundations — models need data pipelines, monitoring, latency SLAs and retraining regimes that pilots rarely build.
- Organisational ownership gaps — pilots often live in data science or R&D silos, with no product team accountable for user outcomes.
These are not bugs; they’re feature requests for a product mindset.
Three product-first moves to convert experiments into services
1. Start with the user job, not the model
Ask: what job is this AI doing for a specific user segment, and how will they behave differently because of it? A pilot demonstrating 90% accuracy is meaningless if the user experience remains unchanged. Map the user journey, define the measurable behaviour change you expect, and design lightweight experiments that validate that change in real contexts.
2. Build an operational Minimum Viable Product
Turn prototypes into an Operational MVP — a thin product that includes data plumbing, monitoring and basic governance. This avoids the “works-on-my-machine” syndrome. Practical checklist items:
- Production data pipelines and lineage
- Latency and uptime targets aligned to the user flow
- Simple logging and business-level observability (not only model metrics)
- Rollback and safety controls
3. Make accountability cross-functional and continuous
Embed AI within an empowered product team that includes a product manager, designer and engineers working alongside data scientists and ML engineers. Ownership must be end-to-end: from user research and KPIs through to deployment and ongoing optimisation. This organises learning and prevents the pullback to “research-only” status.
Lessons from the field: EdTech and consumer examples
Khan Academy’s Khanmigo is a helpful case. Initially a pilot using GPT-4 style models, Khanmigo moved towards teacher- and student-facing features while partnering with infrastructure providers to scale access for educators. See Khan Academy’s rollout and Microsoft partnership for context.
Duolingo offers another lesson: the company rapidly published thousands of AI-generated content units to accelerate learning, aligning model work to measurable curriculum outputs. Their investor materials explain the shift from experimentation to productised learning experiences.
On the enterprise side, Microsoft’s Copilot story shows how embedding AI across workflows (and instrumenting real usage) converts excitement into persistent adoption — but only when coupled with clear SLAs and productivity metrics.
Links for further reading: Khan Academy’s Khanmigo overview, Duolingo strategy notes, and Microsoft’s Copilot usage report provide practical examples of productising AI.
Concrete tactics for product leaders
- Define business-level KPIs first — e.g. retention lift, time saved, conversion improvement. Avoid model-only goals.
- Stage investments — fund the operational MVP and the first 6 months of run costs; pilots that show promise should receive runway for reliability and monitoring, not just more research hours.
- Build a safety and ethics checklist — privacy, bias testing, explainability and consent must be part of the product definition, not an afterthought.
- Instrument for learning — capture experiments as product features: A/B frameworks, rollout flags and metrics to show causal impact.
- Prepare the business model — decide early whether the feature is free, premium, or platform-level; pricing and contract impacts change design choices.
What organisational design looks like when it works
High-performing organisations treat AI products like any digital product: small cross-functional teams with autonomy, clear outcome ownership and a path from prototype budget to product budget. This is the same model that scaled ecommerce and mobile in previous cycles — durable because it aligns incentives, not hype.
Change is rarely purely technical. It demands senior sponsorship to shift budgets from isolated pilots into product teams with long-term P&L responsibility.
Moving forward: making pilots pay their way
If you are responsible for an AI pilot, run an audit this week: does the work have a clear user job, an operational MVP, cross-functional ownership and a path to monetisation or cost saving? If the answer is “no” to any of those, you’re running a research experiment, not building a product.
Treat the next 90 days as a product sprint: pick one pilot, reframe its success metrics around user behaviour and business impact, and allocate the engineering and operational capacity it needs to survive beyond the lab. The alternative is familiar — a backlog of promising demos and an organisation perpetually waiting for the next shiny proof-of-concept. That’s not transformation; it’s theatre.
Ready to move one pilot into a product this quarter? Start by assigning a product manager, creating an operational MVP plan and publishing the KPIs that will decide whether it earns a place in your product portfolio.
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