Your team keeps underestimating AI project timelines. How can you ensure accurate time estimates are met?

Accurate time estimation in AI projects often gets complicated due to the unpredictable nature of research and development. A model may take weeks to train, and the performance won’t be known until after it’s evaluated. Here’s how you can improve timelines in this unpredictable field:

1. Consider data complexity. Ensure a thorough data audit early on to account for cleaning, labeling, and training time.

2. Monitor resource allocation: Use historical data from similar projects to model more realistic timelines, factoring in available team expertise.

3. Iterate and monitor. Break the project into smaller sections with frequent milestones to continuously recalibrate based on real progress. Conduct pre and postmortems on similar projects so you can continuously learn from previous projects.

4. Build in risk buffers. Add contingency buffers for unforeseen challenges, such as model retraining or technical setbacks.

This structured approach builds trust and keeps expectations aligned with reality.