BusinessBeginner25 min readUpdated December 2024

Managing AI Projects

Run Successful AI Initiatives

Run successful AI projects. Scoping, milestones, stakeholder management, and measuring success.

1. AI Projects Are Different

Traditional software has predictable behavior. AI systems are probabilistic—output varies.

Plan for iteration: the first version is rarely the final version. Build learning into your process.

Success criteria matter more than feature lists. Define what 'working' means early.

Start smaller than you think. A narrow, well-defined scope leads to faster value delivery.

Identify the minimum viable AI: what's the simplest version that proves the concept?

Plan for expansion, but don't build for it yet.

You need: domain expertise (the problem), technical expertise (the AI), and project expertise (the process).

Data scientists alone can't deliver AI projects. You need engineers who can productionize.

Consider partners for specialized capabilities vs. building everything in-house.

Set realistic expectations. AI isn't magic—it requires good data, iteration, and ongoing maintenance.

Communicate in business terms: costs, benefits, risks. Avoid technical jargon with executives.

Celebrate incremental wins. AI projects often show value before they're 'complete'.

Define metrics before starting. What will you measure? How will you measure it?

Balance business metrics (ROI, cost savings) with operational metrics (accuracy, speed, reliability).

Plan for ongoing measurement. AI systems need monitoring and maintenance.

Get project management support from AI specialists who understand both the technology and the business.