A convincing AI demonstration can make implementation look straightforward. Real work is less tidy: documents are incomplete, permissions differ and exceptions arrive at the busiest moment. A feasibility study tests whether an idea can work under those conditions before the organisation commits to delivery.
A sector moving from experimentation to execution
The Professional and Business Services AI Champion’s adoption plan, published on GOV.UK in June 2026, identifies a gap between individual use of AI and broader organisational change. It describes how firms can gain local productivity improvements without redesigning the workflows needed to spread those benefits across a business.
For consultancy, accountancy and other knowledge-based services, the practical implication is to assess the whole process surrounding the model. A tool that produces a good draft still needs reliable inputs, professional review and a route into the systems people use. Source: AI Adoption Plan: Professional and Business Services, 8 June 2026.
1. Is the problem clear enough to test?
Describe the input, expected output and person responsible for accepting it. “Help with administration” is too broad. “Prepare a draft summary of an approved client questionnaire for a consultant to check” gives a pilot a defined boundary. Compare the proposed approach with improving the existing process or using conventional automation.
2. Is the information usable and permitted?
Identify where source information lives, who owns it and whether it is accurate enough for the task. Check access permissions, contractual restrictions and data protection requirements. A shared folder full of outdated documents is not an approved knowledge base merely because an AI system can search it.
Use representative material for evaluation, with personal or confidential information removed or appropriately controlled. Include awkward cases: conflicting versions, missing pages and questions the system should decline to answer.
3. Can the solution fit the workflow?
Map integrations with email, document storage, customer records and case-management tools. Establish who can approve access and whether suppliers support the connection. Define what happens when an integration is unavailable, an output is incomplete or a reviewer disagrees with the result.
4. What evidence supports a go or no-go decision?
Agree a small evaluation set and acceptance criteria before testing. Measure accuracy, checking effort, processing time and exception handling. Include ongoing operating and support requirements in the assessment, without assuming that a successful demonstration proves a return on investment.
The outcome should be a decision: proceed with a scoped pilot, change the approach, improve the data first, or stop. Finding that a use case is unsuitable can be a valuable result.
Smart Flow AI’s AI Feasibility Study examines technical fit, data readiness, integrations and delivery options. Request a proposal to assess a specific opportunity.