Product discovery services for enterprise AI solutions: deliverables, timeline, and budget

BySelena Gunawan

Many enterprise AI projects run into trouble before a model is trained. Budget gets approved while the data and workflows behind the idea stay unchecked. Gartner expects organizations to abandon 60% of AI projects unsupported by AI-ready data through 2026.

For leaders comparing business AI solutions, discovery is the cheapest way to get evidence first.

What product discovery means for AI business solutions

Product discovery is a short, fixed-scope phase that answers two questions: what should you build, and can your organization support it? For AI business solutions, data and process matter as much as features.

How AI discovery differs from classic software discovery

Classic discovery covers users, features, and interfaces. AI discovery also has to confirm that the data exists, that you can access it, and that a model performs well on your real cases. For that reason, feasibility checks on actual data happen early.

Who should take part on the client side

  • A business owner accountable for the outcome.
  • A process owner who knows the workflow and its exceptions.
  • A data or IT lead who can grant access.
  • A compliance contact in regulated industries.

When a role is missing, gaps surface during the build, when changes cost more.

Typical discovery timeline: week by week

Six weeks is common for one workflow at enterprise scale.

Weeks 1-2: workflows, data, and constraints

The team maps the workflow as it runs today, measures where time and money go, and audits the data. Legacy systems, access rules, and regulatory limits surface here too.

Weeks 3-4: architecture options and feasibility checks

Architects compare two or three options, such as an off-the-shelf model or a retrieval-based design. Feasibility tests run on your data. A working prototype often settles debates in days.

Weeks 5-6: scope, estimate, and roadmap

The chosen option becomes a prioritized backlog, an estimate, and a roadmap sized to the team you actually have.

Deliverables you should expect

Artifact

What it contains

Why you need it

Who accepts it

Process map

Steps, handoffs, time and cost per step

Shows where AI pays off

Process owner

Data audit

Sources, quality, access, gaps

Confirms usable data

Data or IT lead

Architecture

Approach, system connections, security

Prevents rework

Client CTO

Backlog

Prioritized, validated requirements

Clear build start

Business owner

Estimate

Effort, timeline, team, cost range

Budget approval

Sponsor

Risk register

Risks with mitigations and owners

Keeps risks visible

Sponsor, compliance

What drives the budget of a discovery phase

  • Workflows in scope. One workflow is a focused engagement, while three become a program.
  • Data condition. Scattered or restricted data adds audit time.
  • Legacy systems. Older platforms need extra architecture work.
  • Compliance. Finance and healthcare add review cycles.
  • Prototyping depth. A prototype costs more upfront and saves rework later.

Discovery should cost a small fraction of the build it de-risks.

How to tell a useful discovery from a paid sales exercise

  • Every deliverable is yours and usable by another vendor.
  • The data audit runs on your real data.
  • The findings can say "start smaller" or "don't build this."
  • Estimates state their assumptions.

If every recommendation points to the largest possible build, treat it as a warning sign.

Discovery with an AI product development company: the Altamira approach

Altamira brings 17 years of software engineering to product discovery services for enterprise AI solutions. An AI readiness assessment gives you a specific answer about your data and systems. Rapid Prototyping turns a brief into a working prototype in a day or two.

Requirements then enter Altamira's AI software development lifecycle (AI SDLC). An agent checks each one against the architecture before any code is written, and a named person accepts every finished piece. Known bottlenecks get a proven engine. New ones get custom AI solutions on the same pipeline.

Conclusion

A sound discovery phase leaves you with six signed-off artifacts, a realistic budget, and a decision you can defend. When you compare vendors, look at what they hand over and who accepts it.