Module 4 — AI Project Management & Implementation

Managing an AI project differs from traditional software development. The AI project lifecycle includes:

Problem definition: The most critical step. A vague goal ("use AI to improve our business") guarantees failure. A clear one ("reduce customer churn by 15% by predicting at-risk customers 30 days in advance") provides a measurable target.

Data assessment: Does the necessary data exist? Is it accessible, clean, and sufficient? This stage often reveals that the data infrastructure must be improved before AI can be applied.

Proof of concept (PoC): Build a quick, rough model to test feasibility. Can AI actually solve this problem with available data? A PoC should take weeks, not months.

Development: Build the production-quality model with proper engineering practices: versioned data, reproducible experiments, automated testing, and monitoring.

Deployment: Integrate the model into business operations. This is where most AI projects stall — the gap between a working model in a notebook and a model serving predictions in production is significant.

Monitoring & maintenance: AI models degrade over time as the world changes (called "model drift"). Continuous monitoring, retraining, and improvement are essential.

The build vs. buy decision for AI depends on several factors:

Build when: the problem is unique to your business, competitive advantage depends on custom AI, you have technical talent, and off-the-shelf solutions don't fit.

Buy when: the problem is common (e.g., spam filtering, document OCR), time-to-market matters, you lack in-house AI expertise, and proven solutions exist.

Hybrid approaches are increasingly common: buy a platform, customise with your data.

A well-functioning AI team includes: Data engineers (build and maintain data pipelines), Data scientists (explore data, build models, run experiments), ML engineers (deploy and scale models in production), Product managers (define requirements, prioritise features, measure business impact), and Domain experts (provide subject matter expertise and validate results).

Change management for AI adoption is critical because AI changes how people work. Common resistance comes from fear of job displacement, lack of understanding, distrust of AI decisions, and disruption to established workflows. Address resistance through education (demystify AI), participation (involve end-users in design), demonstration (show quick wins), and honest communication (be transparent about AI's role and limitations).