Our AI Delivery Methodology

Start with a named user, a specific workflow and the decision or action that should improve. Observe how the work is done now, including workarounds, exceptions and downstream consequences. Separate the cost of the problem from enthusiasm for a particular technology.

Agree a baseline that can be measured again: handling time, rework, retrieval quality, processing delay or another relevant measure. Define what success would mean and what would make a solution unacceptable. The output is a discovery brief, a prioritised use case and a decision on whether to proceed.

Map the data required, its owner, quality, format and permitted use. Identify sensitive fields, access boundaries and retention requirements. Check whether test data can represent the real task without exposing information unnecessarily. Missing access or weak source data is a delivery constraint to resolve, not an assumption to hide.

Document who can be affected by an incorrect output, what actions the system may take and when a person must review or intervene. For higher-impact decisions, keep the appropriate human decision-maker responsible. Build a risk register proportionate to the use case, with an owner and treatment for each material issue.

Consider a rules-based workflow, conventional software, retrieval, a predictive model or an agent against the same business need. Evaluate integration effort, operating cost, maintainability and the level of uncertainty. A more autonomous system is not automatically a better system.

Define system boundaries, user journeys, access roles, integrations and failure behaviour. Create a representative evaluation set before tuning the solution. Agree what it means to abstain, escalate, retry or fall back to the current process. The design record should make these choices understandable to a business owner as well as an engineer.

Implement the selected slice of the workflow and test it against the baseline. Include realistic inputs, incomplete information, edge cases and expected misuse. For language-based systems, assess whether answers are relevant and supported; for workflows, test permissions, retries and duplicate events; for predictive models, examine data leakage and error across relevant conditions.

Review quality alongside latency, cost and usability. Keep a record of limitations and unresolved findings. A persuasive demonstration is useful, but acceptance should depend on evidence from representative tasks. The evaluation report informs whether to improve the prototype, narrow its scope or move to a controlled pilot.

Introduce the solution to a defined group with a named sponsor and operating owner. Explain what it can and cannot do, what feedback is useful and how users obtain help. Limit access and actions to the pilot scope. Keep the previous process available where a fallback is needed.

Review performance and adoption together. A system may meet a technical target yet fail because it creates additional work for the team. Agree review points and stop conditions before the pilot starts. Expansion should follow evidence of useful performance, acceptable risk and a team that can support the change.

Prepare deployment notes, data flows, access procedures, user guidance and an operating runbook. Identify who manages incidents, changes, costs and updates. Define support boundaries and response arrangements in the agreement rather than assuming unlimited support is included.

Monitor the measures that matter to the workflow and revisit evaluations when source data, models, prompts or integrations change. Retain a change history and test significant updates before wider release. The operating model should allow the organisation to improve the service deliberately, with decisions and responsibilities that remain clear after the original build.

We use governance questions throughout the engagement: who owns the risk, what context changes the risk, how it is measured and how it is managed. NIST’s AI Risk Management Framework provides a useful reference through its Govern, Map, Measure and Manage functions. Our use of this reference does not imply NIST certification or automatic legal compliance.

The practical result should be understandable controls: least-privilege access, suitable data handling, evaluation before release, clear human review points and a way to investigate problems. Jurisdiction-specific legal or regulatory questions need appropriate specialist review in the project context.