Opportunity assessment and architecture
Assess candidate use cases, data readiness and workflow requirements. Architecture work can define the AI component's role, its interfaces and the surrounding application needs.
AI Consultancy
& Development
Give AI a useful role in the solution.
An AI opportunity starts with a useful task. If the fit between AI, your data and the intended workflow is unclear, the first decision is what to assess. An engagement may address suitability, application design or integration, with evaluation defined around the intended use.
Move from interest to a defined use case
AI can be difficult to assess when the intended outcome, available data or operating constraints are unclear. The first useful step is to define what the application needs to do, who will use it and what would make its behavior acceptable.
This gives an assessment a practical focus and helps identify where AI may be appropriate, where a simpler approach may be sufficient and what needs further investigation.
Assessment, applications and integration
AI-enabled application development
Depending on the agreed requirements, development may include custom AI-enabled applications, machine learning, applied data science, intelligent process automation or decision-support functions. The method and scope follow the problem and available data.
Integration and evaluation
Plan how the AI capability connects to bespoke software or an existing workflow. Define relevant evaluation criteria, human review points and limitations before interpreting performance.
Give the next decision an evidence base
Use-case definition and data assessment establish what a prototype would need to test. Where appropriate to scope, architecture and prototype work can provide a basis for deciding whether to integrate, revise or take the idea further.
Depending on scope, outputs may include an opportunity assessment, data-readiness notes, architecture, prototype or application components, evaluation findings and integration documentation. Production operation, retraining and continuing support require a defined scope of their own.
Evaluation needs to reflect the intended context. Results should be considered alongside the test conditions, data limitations and the decisions a person will continue to make.
Discuss where AI may fit
Describe the task, the information available and the decision an AI capability would need to support. A high-level summary is enough to discuss the assessment or development work that may be appropriate.