The feasibility study has always been a fundamental tool for evaluating a project's sustainability before investing significant resources. With AI's advent, this process is evolving radically: not only is AI the subject of many feasibility studies, but it's also becoming the tool we use to conduct them.
From intuition to data
Traditionally, feasibility studies relied on market estimates, stakeholder interviews, and projective financial analysis. Today, AI tools enable real-time market dataset analysis, economic scenario simulation with advanced Monte Carlo models, and technical feasibility assessment through automated proof-of-concepts, significantly reducing decision uncertainty.
Technical feasibility of AI projects
When the feasibility study's subject is an AI project, the analysis must consider specific aspects: training data availability and quality, infrastructure requirements (GPU, storage, bandwidth), required team competencies, realistic development timelines for MVP and final product, and expected performance metrics.
Evaluation framework
At Adalot, we use a structured framework evaluating five dimensions: technical feasibility, operational feasibility, economic feasibility, legal feasibility (GDPR, AI Act, compliance), and temporal feasibility. This multidimensional approach reduces project failure risk.
The value of feasibility studies
Investing in a feasibility study before starting an AI project isn't a cost — it's insurance. Data shows that projects starting with a structured assessment have 60% higher success probability than those proceeding directly to implementation.