What This Template Covers
The AI use case assessment template covers five structured evaluation areas that together produce a feasibility score and a prioritisation recommendation. The business value scoring section evaluates each use case against four dimensions: the size of the problem being solved (how many users, how frequently, with what cost), the quality of the solution AI could provide compared to the current approach, the strategic fit with organisational priorities, and the competitive differentiation value. Each dimension is scored on a scale and weighted according to organisational context. The data readiness checklist assesses whether the data needed to build the AI system exists, whether it is accessible, whether it is of sufficient quality, and whether its use is permissible under GDPR and relevant data governance policies. Data readiness is the most common blocker for AI use cases that look feasible on paper. The technical feasibility section evaluates the current state of AI technology against the specific requirements of the use case. Not every business problem maps to a well-solved AI task. This section helps identify whether you are working with mature AI capabilities or pushing the frontier in ways that increase risk and cost. The regulatory risk section covers the EU AI Act risk classification, GDPR implications, and any sector-specific regulatory considerations (FCA for financial services, MHRA for medical devices, NHS Digital for healthcare data applications). The build-versus-buy decision framework helps decide whether to build a custom AI system, procure an existing AI product, use an API-based AI service, or use a no-code AI tool. Each option has different cost, speed, control, and risk profiles.
How to Use This Template Step by Step
Step one: define the use case clearly. Write a two to three sentence description of the specific AI application: what task the AI would perform, for which user, with which inputs and what expected outputs. Vague use case definitions produce unreliable assessments. "Use AI to improve customer service" is not a use case. "Use an LLM to classify incoming support tickets by category and priority, and suggest a response from a knowledge base, for review by a human agent before sending" is a use case. Step two: complete the business value scoring. Score the use case from one to five on each of the four dimensions: problem size, solution quality improvement, strategic fit, and competitive differentiation. Multiply by the weight you assign to each dimension based on your organisation's priorities. The raw score provides a comparable baseline if you are assessing multiple use cases. Step three: run the data readiness checklist. For each data input the use case requires, answer: does this data currently exist in your organisation? Is it accessible to the team that would build the system? Is it of sufficient volume and quality to train or evaluate an AI system? Is using it for this purpose permitted under your data governance policies, your privacy notices, and GDPR? If the answer to any of these is no, note the remediation required and the estimated time. Step four: assess technical feasibility. Identify the specific AI task or tasks required (text classification, entity extraction, text generation, image recognition, time-series prediction). For each, assess: is there a well-established approach that works for this task? Are there off-the-shelf models or APIs that can be used? What are the accuracy requirements and can they be met? What is the expected inference cost at the volume you need? Step five: evaluate regulatory risk. Apply the EU AI Act risk classification to your use case. Prohibited use cases include AI systems for social scoring and real-time biometric surveillance in public spaces. High-risk use cases include AI systems used in employment decisions, credit assessments, healthcare diagnostics, and education. High-risk systems have additional conformity assessment and documentation obligations under the EU AI Act. Note the relevant GDPR lawful basis for any personal data processing. Step six: make the build-versus-buy decision. Map the use case requirements against each option. Custom build gives maximum control and differentiation but highest cost and longest time to value. API-based services (OpenAI, Anthropic, Google Gemini) give fastest time to prototype but ongoing cost and dependency on the provider. Existing AI SaaS products give fastest time to value for well-defined use cases but limited customisation. No-code AI tools work for simple use cases but hit capability ceilings quickly.
Section-by-Section Walkthrough
The business value scoring section works best as a table. Each row is a use case (if you are assessing multiple). Columns are the four scoring dimensions plus a weighted total. Include a brief justification for each score. A use case with a high score but low data readiness should not be prioritised over a use case with a medium score and strong data readiness. The score is an input to prioritisation, not the only factor. The data readiness checklist should be completed for each data input type separately. Common data types for AI use cases include: historical transaction records, customer interaction logs, documents and unstructured text, product usage data, sensor data, and third-party data feeds. For each, the checklist covers: location (where is it stored?), volume (how much exists?), quality (what is the error rate, completeness, and labelling status?), accessibility (what is needed to access it?), and permission (is using it for this purpose covered by existing privacy notices and data governance policies?). The technical feasibility section should include a task decomposition. Complex AI use cases are often made of multiple simpler AI tasks chained together. Decomposing the use case reveals which parts are mature AI problems and which are novel or underdeveloped. For example, a "contract review" use case might decompose into: document parsing, clause extraction (named entity recognition), clause classification, risk scoring, and output generation. Each sub-task has different maturity and cost characteristics. The regulatory risk section should explicitly name the EU AI Act article that applies. Annex III of the EU AI Act lists the high-risk AI system categories. Products targeting UK markets should also note the ICO's guidance on AI and data protection, which sets out UK-specific expectations for AI systems processing personal data. The build-versus-buy comparison should include a five-year total cost of ownership estimate for each option, not just the upfront development cost. Ongoing API costs, maintenance, model retraining, and the internal team time needed to manage each option all factor into the real cost.
Common Mistakes This Template Prevents
The most common mistake in AI use case assessment is starting with the technology and working backwards to a use case. "We should do something with AI" leads to poorly defined use cases with unclear success criteria. This template starts with the problem and works forward to the AI approach, which produces more coherent assessments. The second mistake is ignoring data readiness until after the use case has been approved and budgeted. Finding out that the required data is not accessible, not of sufficient quality, or not permissible to use for this purpose after committing budget is expensive. The data readiness checklist surfaces these issues at the assessment stage. The third mistake is underestimating regulatory risk for AI systems in regulated industries. UK financial services firms, healthcare providers, and education organisations face specific regulatory obligations that affect AI system design. The EU AI Act adds another layer for organisations operating in or selling to EU markets. Assessing regulatory risk at the use case stage, not the deployment stage, prevents costly redesign. The fourth mistake is defaulting to custom build when an existing solution would serve the use case adequately. Custom AI systems have ongoing maintenance and retraining costs that are not always obvious upfront. The build-versus-buy framework makes these trade-offs explicit before commitment.
Customisation Tips for Different Project Types
For corporate innovation teams assessing a portfolio of use cases, add a portfolio view to the template. Plot each use case on a two-by-two matrix with business value on one axis and feasibility on the other. Use cases in the high-value, high-feasibility quadrant are the clear starting points. This visualisation is effective for stakeholder presentations. For startups assessing a single use case, the template can be simplified. The business value scoring and data readiness sections are most important. The regulatory risk section should be completed in full if you are in a regulated sector. Skip the portfolio view and use the assessment as a structured go or no-go document for a specific build decision. For use cases in the NHS or UK health sector, add a section covering NHS Digital data standards, the Data Security and Protection Toolkit, and MHRA registration requirements if the AI system meets the definition of a medical device under UK MDR 2002 or EU MDR 2017. For fintech use cases, add a section covering FCA expectations for AI systems used in credit decisions, fraud detection, or customer advice. The FCA's guidance on the use of artificial intelligence in financial services sets out specific expectations for explainability, fairness testing, and governance.