AI Feasibility Studies

Our feasibility studies examine AI initiatives from three angles: technical viability, data readiness, and business value.

Technical viability looks at whether current AI capabilities can deliver what you need. Some problems are well-suited to AI; others remain genuinely difficult despite the hype. We tell you which category yours falls into.

Data readiness examines what you have to work with. AI systems learn from data, and the quality of that data determines what is achievable. We assess volume, cleanliness, accessibility, and relevance.

Business value tests whether the outcome is worth the effort. What needs to be true for this to deliver value, and what are the realistic paths to impact compared to alternatives?

We start with your proposed use case. You might have a detailed specification or just a rough idea. Either works.

Our team reviews your existing documentation, interviews key stakeholders, and examines relevant data sources. For technical assessment, we often build small prototypes or run experiments with sample data.

We also agree what “success” would mean before we test anything. That typically includes measurable outcomes, acceptable error rates, operating constraints, and the level of human oversight required. This prevents feasibility from becoming an academic exercise and keeps the work tied to real delivery decisions.

The findings come together in a feasibility report. This gives you a clear verdict: proceed, proceed with modifications, or do not proceed. When we recommend changes, we explain exactly what needs to happen and why.

Every feasibility study delivers:

Technical assessment. Capability requirements, known limitations, and what approach is most likely to work.

Data readiness review. Quality, coverage, and access—and the gaps that must be addressed.

Value case and success criteria. What “good” looks like, how you would measure it, and the assumptions being made.

Risk register and mitigations. What can go wrong, how likely it is, and how to reduce exposure.

A clear recommendation. Proceed, proceed with changes, or do not proceed—plus concrete next steps if you go ahead.

Consider this service when:

You have an AI concept but uncertainty about whether it will work. You want evidence before you commit to build.

You need confidence for a decision. You want a clear recommendation that can stand up to stakeholder scrutiny.

Previous attempts have failed. You want to understand what went wrong before trying again, and avoid repeating the same mistakes.

A vendor is proposing a solution. You want an independent view of feasibility, risk, and what needs to be true for success.

Most feasibility studies complete in two to four weeks. Complex projects with multiple data sources or integration requirements may take longer. We agree the scope and timeline before starting, so there are no surprises.

Where helpful, we include a short “decision brief” version of the findings for leadership teams: the recommendation, the critical risks, and what needs to be true for success.

Use these prompts to clarify scope and identify hidden risks before you commit.

“What are the key assumptions behind this AI idea, and which ones are most likely to be wrong?”

“What data would we need for reliable outcomes, and what validation would prove it works?”

“What are the main failure modes in production, and what guardrails or fallbacks reduce risk?”