70% of AI projects never reach production. The main cause isn't technology but lack of rigorous assessment before investment. A well-conducted feasibility study is the difference between a value-generating project and one that consumes budget without results.
Phase 1: problem definition
The first and most critical step is verifying that the problem is truly an AI problem. Not all business problems require machine learning. At Adalot, 30% of our technology assessments conclude with recommending non-AI solutions, saving clients considerable resources.
Phase 2: data assessment
AI project feasibility critically depends on data availability and quality. The assessment must evaluate: available data volume, quality (completeness, accuracy, consistency), accessibility (silos, formats, permissions), and additional data acquisition costs if needed.
Phase 3: proof of concept
Before full development investment, a limited proof of concept validates key hypotheses with contained investment, answering: does the model achieve minimum required performance? Are inference times compatible? Is available data sufficient?
Phase 4: economic analysis
Economic analysis must consider development costs plus cloud infrastructure, operational maintenance costs, monitoring and retraining, and human resources. The 3-year TCO is the most realistic metric for evaluating economic sustainability.
Phase 5: roadmap and governance
The feasibility study concludes with a detailed roadmap including milestones, KPIs, identified risks, and mitigation plans. Adalot offers comprehensive feasibility studies covering all these phases with a pragmatic, results-oriented approach.