Altamira product discovery checklist for AI and software projects

Most software projects that fail were already in trouble before anyone wrote a line of code. The requirements were vague, the data did not exist, or the product solved a problem nobody had. Product discovery services exist to catch those issues while they are still cheap to fix. This checklist covers what to validate before development starts, what discovery should hand you at the end, and how Altamira connects it to delivery planning. Explore in more detail: software product discovery services.

Why AI and software projects need discovery first

According to RAND Corporation research, more than 80% of AI projects fail, roughly twice the failure rate of IT projects that do not involve AI. MIT's Project NANDA reported in 2025 that 95% of enterprise generative AI pilots showed no measurable return. Gartner expects at least 30% of generative AI projects to be abandoned after proof of concept. Traditional software fares somewhat better, but not by much: the Standish Group's CHAOS research puts the share of fully successful projects at around 31%, and McKinsey found that large IT projects run 45% over budget on average while delivering 56% less value than projected.

What stands out in the post-mortems is how rarely the technology itself is the cause. Projects collapse because the problem was never defined, the data was never checked, or the scope grew without anyone deciding it should. All of these are discovery failures, and all of them are visible weeks before development begins if someone goes looking.

This is where a structured software discovery process earns its keep. Its entire job is to answer one question: should we build this, and if so, what exactly? For AI initiatives the stakes are doubled, because you are validating the business case and the feasibility of the model at the same time. AI product discovery has to confirm that the data you need actually exists, in usable condition, before anyone commits to a delivery date.

What to check before sprint 1

A good discovery checklist is short but unforgiving. If any of the three areas below fails validation, the project plan needs to change before development starts, not after.

User problem

Start with the least technical question on the list. Who has this problem, how often, and what do they do about it today? If the honest answer is "we assume people want this," you have a research task, not a development task. Interview real users, review support tickets, and look at how people work around the gap right now, since an existing workaround is strong evidence of demand. This stage should end with a written problem statement that names the user, the pain, and the current cost of living with it.

Data availability

For AI projects, this is the checkpoint that kills or saves the business case. Gartner has predicted that 60% of AI projects lacking AI-ready data will be abandoned through 2026. Before Sprint 1, you need answers to specific questions. Does the data required for the use case exist? Who owns it? Is it labeled, complete, and legally usable? How much cleanup will it need, and who pays for that? Teams regularly find that the "six months of customer data" they planned to train on is fragmented across three systems with inconsistent fields. Better to find that out during discovery than in week four of development.

Integration requirements

Software rarely lives alone. Map every system the product must talk to: CRMs, ERPs, payment providers, internal APIs, legacy databases. For each one, confirm that documented interfaces exist, that access can be granted, and that data formats are compatible. Integration surprises are among the most common causes of timeline slippage, and they are almost entirely preventable. If a critical system has no API and no documentation, that fact should reshape the estimate before it becomes an emergency.

What discovery should produce

Discovery is only worth the money if it ends in concrete artifacts. Vague alignment is not a deliverable. Here is what project discovery services should hand you at the close of the phase.

Prioritized feature list

Not a wishlist. A ranked backlog where every item is tied to the validated user problem and scored against effort. The point of prioritization is to make the first release small and defensible. When the Standish Group studied project outcomes over three decades, the consistent finding was that smaller scopes succeed far more often than large ones. A prioritized list gives you permission to cut.

Technical assumptions

Every estimate rests on assumptions, and unwritten assumptions are how budgets die. Discovery should document them explicitly: which architecture the plan assumes, which third-party services will be used, what model performance is considered acceptable for the AI components, and which risks were consciously accepted. When something changes mid-project, this document tells everyone whether the change breaks the plan or not.

Development roadmap

The roadmap converts everything above into sequence and time. It should show phases, dependencies, team composition, and realistic milestones, with the riskiest assumptions tested earliest. A roadmap built on validated inputs is an actual commitment device, especially when it is paired with delivery practices that shorten build cycles. One built on guesses is decoration.

How Altamira turns discovery into delivery planning

Altamira treats discovery as the first stage of delivery rather than a separate consulting exercise. The same questions asked during software product discovery, what problem are we solving, what data do we have, what must this system connect to, feed directly into architecture decisions and estimation. Business analysts validate the problem and scope, solution architects translate findings into a technical design, and the delivery team builds the estimate from that design instead of from a sales conversation.

The practical benefit is continuity. The people who challenged your assumptions in discovery help plan the build, so nothing gets lost in a handover document. Findings become a costed roadmap with named risks, and that roadmap becomes the delivery plan for Sprint 1. For AI projects, this includes validating the data and model approach before any commitment to production timelines.

Conclusion

The gap between projects that deliver and projects that drift is usually decided before development starts. Run the checklist honestly: a validated user problem, confirmed data, mapped integrations, and a roadmap built on documented assumptions. If you would rather not run that process alone, Altamira's discovery team can take an idea through validation and hand back a plan you can actually budget against. Book a discovery consultation and find out what your project looks like on paper before it costs you in production.