AI Projects Are Not Ordinary IT Projects
Implementing artificial intelligence differs fundamentally from a traditional IT project. In a conventional project, requirements are (or should be) clear from the start — we build a form, an API, a report. In an AI project, we only know what problem we want to solve at the outset, but we do not know whether the AI model will be good enough, how much training data is needed, or what inference costs will be in production.
This requires a different approach to management — iterative, experiment-driven, and focused on rapid hypothesis validation. You cannot plan a year-long AI project in a waterfall methodology and expect success.
Choosing the Right Methodology
There is no single ideal methodology for AI projects. We match the approach to the specifics of the project, team, and organization:
- Scrum with 2-week sprints — for teams that need a regular cadence and visible progress. Each sprint ends with a demo of a working increment.
- Kanban — for teams working on multiple smaller tasks in parallel. Visualizing work-in-progress helps identify bottlenecks.
- Dual Track Agile — discovery (research, prototyping) and delivery (implementation, testing) run in parallel. Ideal for AI projects where the experimentation phase is critical.
MVP — Minimum Viable Product
The first milestone of every AI project should be an MVP — minimum viable product. Not a fully-featured product, but the simplest version that validates the key hypothesis. Can the AI model solve the problem? Is the data of sufficient quality? Do users accept the solution?
AI accelerates MVP development. AI agents generate code, tests, and documentation. The engineer reviews and refines — the prototype is built in weeks, not months. This means the "continue or pivot" decision is made quickly, before a large budget is invested.
Automated Testing in AI Projects
AI projects require an expanded testing strategy. In addition to standard tests (unit, integration, E2E), the following are needed:
- Model quality tests — accuracy, precision, recall, F1 metrics on the test set
- Model regression tests — ensuring a new model version is not worse than the previous one
- Edge case tests — how the model reacts to unpredictable input data
- Performance tests — inference latency, throughput, resource consumption
- Security tests — resilience against prompt injection, data leakage, adversarial attacks
An implemented and automated development cycle with all types of testing ensures that every change in code or model is automatically verified before it reaches production.
Scaling — From MVP to Production
Scaling an AI project means transitioning from "works on a laptop" to "runs in production, 24/7, under load." It requires containerization, orchestration, auto-scaling, monitoring, and alerting. The infrastructure must handle increased load without quality degradation. Inference costs must be kept under control — intelligent AI model routing automatically matches the model to task complexity, minimizing costs without sacrificing quality.
The project manager's role in the scaling phase is risk management, team coordination, and stakeholder communication. AI supports the process — forecasting delays, optimizing resource allocation, and automating progress reporting.