Machine Learning Project Benefits Realisation Plan
This is a practical guide to building a benefits realisation plan for a machine learning project project — how the project’s benefits will be measured and actually realised, adapted to the realities of building and deploying a machine-learning model.
What a Benefits Realisation Plan is
A benefits realisation plan is how the project’s benefits will be measured and actually realised. For the full concept and how it works in general, see Benefits Realization. On a machine learning project project it plays the same role, tuned to this kind of work.
Why it matters for a Machine Learning Project project
Machine Learning Project projects live or die on building and deploying a machine-learning model. A well-built benefits realisation plan gives the team a shared, explicit reference for exactly that — reducing ambiguity, aligning stakeholders, and making problems visible early enough to act. Skipping it, or doing it generically, is how machine learning project projects drift into avoidable delay and cost.
What to include
- Target benefits
- Metrics / KPIs
- Baseline values
- Owner
- Realisation timeline
Machine Learning Project-specific considerations
Tailor the benefits realisation plan to the risks that most often derail machine learning project projects:
- Data labelling and quality
- Model drift
- Productionisation and monitoring
Example
On a real machine learning project project, the benefits realisation plan would be shaped by building and deploying a machine-learning model. In particular, it should explicitly account for the project’s biggest risks — data labelling and quality, model drift, productionisation and monitoring — rather than treating them as afterthoughts.