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AI projects are notoriously hard to estimate upfront — unlike traditional software features, outcomes depend heavily on data quality and model performance that isn't fully knowable until you're deep into the work. Here's how to estimate responsibly anyway.

Separate Discovery From Delivery

Rather than quoting a single fixed price for an entire AI initiative, structure the engagement as a short, fixed-scope discovery phase — assessing data readiness and technical feasibility — followed by a much more confident estimate for the build phase.

Data Readiness Is the Biggest Unknown

The single largest source of AI project timeline uncertainty is data quality and accessibility — not model complexity. A discovery phase focused specifically on data assessment removes much of this uncertainty before committing to a full timeline.

Use Ranges, Not False Precision

Presenting a wide but honest range, tied to specific milestones and decision points, serves stakeholders better than a falsely precise single number that inevitably shifts once real data exploration begins.

Build in Explicit Checkpoints

Structuring the project with clear evaluation checkpoints — "if model accuracy reaches X by this point, proceed; if not, reassess approach" — gives stakeholders genuine visibility and control rather than an opaque black-box timeline.

Plan for Iteration From the Start

AI model performance rarely hits target accuracy on the first attempt. Budgeting explicit iteration cycles into your estimate, rather than treating the first attempt as final, produces far more realistic project plans.

Cantonet Technologies scopes AI projects with this kind of staged, honest estimation approach — giving you real visibility instead of a number that falls apart three weeks in.