Most AI features never make it out of the lab. The ones that do often break, underdeliver, or quietly decay until no one remembers why they were shipped in the first place.

Building AI products isn’t just about adding a model. It’s about building differently across every phase of the product lifecycle. From fuzzy discovery to graceful sunsetting, AI demands new questions, new workflows, and new roles.

This guide will walk you through the complete lifecycle of an AI product…

Let's begin with Phase 1: Discovery.

Phase 1: Discovery - Finding the Right Problem

The problem is blurry. The solution is blurrier.

You're not building a button or dashboard, you're building something probabilistic, contextual, and often speculative. Traditional product discovery asks "What problem are we solving?" AI product discovery adds: "Is this even solvable?" and "Should we use AI at all?"

What makes AI discovery different

Conventional features give you a clear line from input to output. AI features operate in a realm of probabilities and edge cases. Your discovery process must accommodate this fundamental shift in thinking. You may also want to keep in mind how a problem that feels hard today might be trivial in 6 months.

Questions to answer

• Are we solving a problem that truly needs AI? Or are we just AI-washing a simple feature?

• What kind of intelligence are we adding (prediction? classification? generation?)

• How will we measure success when "correctness" isn't binary?

• Do we have the right data or the right signals to train on?

• Will users accept probabilistic answers in this context?

Who you need

• A product manager with strong AI literacy who can distinguish hype from value.

• A product designer who can help frame the problem, not the solution.

• A data scientist who can reality-check your assumptions.

Activities that work

• Problem framing specifically for probabilistic systems.

• Competitive research focused on non-obvious AI applications.

Watch out for

• Reverse-engineering a use case from a model you "want to try out".

• Jumping to solutions before understanding user problems.

• Assuming you have the right data (you probably don't).

• Discovery timelines that look like traditional features (AI discovery takes longer).

What success looks like

You've identified a real user problem where AI provides unique value. You have initial confidence in your data quality and availability. Providing a solution to this user need is coherent with your business and product strategies.

And most importantly, you've set realistic expectations with stakeholders about what comes next.

Phase 2: Experimentation - Testing What's Possible

The real challenge

Now it's time to play. Play, yes, but with purpose.
You are not building features yet. You're testing assumptions, probing data, sketching outputs. Most of this work won't ship and that's the point.

Unlike traditional MVPs, AI experimentation involves exploring the boundaries of what's technically feasible before committing to product directions. You're not just validating user interest; you're validating that the technology can reliably solve the problem.

What makes AI experimentation different

Experimentation with AI features isn't linear. You're simultaneously validating data quality, model performance, and user acceptance, often with rapidly changing tools and capabilities.

Questions to answer

• How do we evaluate success early when the final quality might be unknown?

• Can we simulate the user experience with minimal engineering effort?

• What does "good enough" look like for model performance?

• What are the critical failure modes we need to prepare for?

• How much improvement can we reasonably expect with more data/tuning?

Who you need

• A data scientist or ML engineer comfortable with rapid prototyping.

• A product manager comfortable with fuzziness and partial solutions.

• A product designer who can create experiences around imperfect inputs and uncertain outputs.

Activities that work

• Wizard-of-Oz tests: fake the AI to test interaction patterns.

• Quick, disposable model experiments that test specific hypotheses.

• Shadow deployments that run AI silently without showing results to users.

Watch out for

• Chasing perfect accuracy when "good enough" will validate your concept.

• Spending weeks tweaking a model without ever testing with users.

• Building too much infrastructure for experiments that might fail.

• Getting attached to specific technical approaches too early.

What success looks like

You've validated that AI can solve the core problem with acceptable performance. You understand the technical constraints you'll need to design around. You've identified the key quality metrics to track, technical and business. And you've seen enough user reactions to know there's genuine excitement about the value proposition.

Phase 3: Productization - Making AI Reliable

The real challenge

Now it gets real. The proptotype impressed the execs. The Slack demo was fire.
Now it needs to work for messy impatient users, at scale, under load and with unpredictable inputs.

This is where many teams stumble.
Because productizing AI isn’t just about shipping features, it’s about shipping trust.

What makes AI productization different

Traditional features have predictable behaviors and clear failure modes. AI features require different approaches to testing, monitoring, and user experience. They mislead, confuse or erode trust silently.

You're not just building functionality, you're building safety rails, recovery mechanisms and explainability.

Questions to answer

• How do we design for trust, not just functionality?

• How will we handle data privacy and security concerns?

• What happens when the model gets it wrong? (And it will)

• What's our fallback if the AI fails or behaves unexpectedly?

• How will we explain model decisions to users and customer support?

Who you need

• Backend/infra engineers with AI deployment experience.

• MLOps/platform engineers for production model infrastructure.

• Frontend devs and UX designers who understand uncertainty and feedback loops.

• Product manager preparing cross-functional teams for the shift from demo to production.

Activities that work

• Graceful degradation planning for when models fail

• Building human review workflows for edge cases

• Creating transparent user feedback mechanisms

• Comprehensive testing with diverse and adversarial inputs

• Designing confidence thresholds to filter low-quality outputs

Watch out for

• Launching an impressive feature that breaks in the real world.

• Overlooking the UX of model failures.

• Building without instrumentation to track quality.

• Assuming your test data represents production scenarios.

• Treating model deployment as a one-time event.

Success looks like

You've shipped an AI feature that performs reliably at scale, with appropriate guardrails for quality. Users understand what to expect, including limitations. You have monitoring in place to catch issues before users do. And your team is prepared to respond quickly when unexpected behaviors emerge.

Phase 4: Improvement - Evolving Your AI Over Time

The real challenge

Unlike classic features, AI outputs change over time. Models drift. Users adapt. Feedback flows in (or doesn't).

What makes AI products unique is that they don't stay static after launch. Their performance can degrade silently as the world changes around them. Your job is to build systems that detect, adapt, and improve continuously.

What makes AI improvement different

Traditional feature improvements are typically driven by new user needs or changing business requirements. AI improvements are also driven by model performance, data quality shifts, and sometimes unpredictable user interactions with the system.

Questions to answer

• What does long-term maintenance look like when the underlying technology is rapidly evolving?

• Are we collecting feedback in a usable way that can improve the model?

• Do we know when to retrain, and why?

• Which metrics actually correlate with user satisfaction?

• How do we distinguish between model failures and UX problems?

• What's our process for deploying model updates without disruption?

Who you need

• Product Managesr who can prioritize improvements and prevent regression.

• User researchers or designers who can uncover how AI is really being used.

• Engineers focused on model monitoring and evaluation loops.

• Data scientists who can iterate based on production insights.

Activities that work

• Creating data pipelines for continuous improvement.

• Building systematic feedback collection into the product.

• A/B testing model versions against each other.

• Regular reviews of edge cases and failures.

Watch out for

• Shipping and forgetting. AI features decay quietly while metrics stay green.

• Ignoring changing user behavior as they adapt to AI capabilities.

• Collecting feedback that never gets used to improve the model.

• Assuming your initial success metrics will remain relevant.

• Failing to budget for ongoing model maintenance

Success looks like

You've established a continuous improvement cycle where real-world usage makes your AI consistently better. You can detect quality issues before users complain. Your team has sustainable processes for maintaining the feature as both user needs and AI technology evolve. And you're gathering insights that inform your broader product strategy.

Phase 5: End-of-Life - Knowing When to Sunset Your AI

The real challenge

Eventually, you'll sunset the feature. Or redesign it. Because the model is obsolete. Or usage dropped. Or you found a better way.

This is perhaps the hardest phase for technical teams emotionally. AI features often represent significant investment in time, resources, and organizational learning. But clinging to underperforming AI can drain resources and erode user trust.

What makes AI end-of-life different

Traditional features often sunset because they're replaced by better UX or aren't used enough. AI features might additionally sunset because their underlying technology becomes obsolete, maintenance costs become unsustainable, or risk profiles change with evolving regulations.

Also: AI features often represent huge sunk costs and org-wide learning. That makes them harder to kill and more dangerous to keep alive when they’re no longer pulling their weight.

Questions to answer

• Is this still delivering value relative to its maintenance costs?

• Are we holding onto this because we built it, not because it works?

• Should this be replaced with something simpler or more deterministic?

• What have we learned that should inform our next AI initiatives?

• How do we transition users without disrupting their workflows?

• How has the AI landscape changed since we launched?

Who you need

• Product team able to make hard ROI assessments about ongoing costs.

• Strategic product or exec leader willing to make tough calls.

• Product designers who can create a graceful user transition.

• Data analysts who can objectively assess performance trends.

Activities that work

• Regular portfolio reviews that treat AI features like investments.

• Clear success criteria established in advance for when to sunset.

• Exit interviews with power users to understand what worked.

• Knowledge capture to preserve learnings for future initiatives.

• Thoughtful communication plans for feature deprecation.

Watch out for

• Keeping a dead AI feature alive because it took too long to build.

• Missing the opportunity to replace complex AI with simpler solutions.

• Losing institutional knowledge about what worked and what didn't.

• Failing to harvest valuable data and insights before shutting down.

• Ignoring mounting maintenance costs as technologies evolve.

Success looks like

You've made the tough decision to sunset or replace an AI feature based on objective criteria, not sunk cost fallacies. You've preserved the valuable learnings from the initiative. Your users have clear migration paths. And most importantly, your organization has matured in how it evaluates and develops AI products for the future.

Building AI products isn’t about shipping a model, it’s about managing uncertainty across the entire product lifecycle. Each phase requires different instincts, different questions, and different kinds of discipline.

Most teams fail because they treat AI like any other feature. The ones that succeed? They treat it like a system: living, shifting, and tightly integrated into how users think and behave.

This article focused on just one part of building an AI system: the discovery-to-delivery path from a product lens. Ethics, compliance, GTM, org design, and full-stack technical strategy all matter too... and we'll get to those!

Until then: build smarter, learn faster.

Let’s roll. And roll. And roll.