AI Opportunity Review
Select frequent, measurable work with available inputs.
06 / AI CONSULTING
AI consulting that turns business questions into a testable pilot
Assess workflows, data, existing systems and risk to prioritise useful AI opportunities and define success before development spend.
WHO IT IS FOR
For leaders and teams who see potential in AI but need to choose the right first use case, decide whether to buy or build, and understand data readiness.
OUR APPROACH
Define the outcome, scope and acceptance criteria before choosing technology, so the investment solves a real problem and remains maintainable.
PROBLEM
Problems to solve before adding another tool.
- Many ideas lack a clear priority
- Data and existing systems may not be ready
- Model cost, security and ownership are unclear
- Tool experiments have no measurement plan
OUTCOME
What the project should make better.
- Use cases ranked by value, feasibility and risk
- A view of data, permissions and integration readiness
- A pilot scope with baseline and go/no-go criteria
- An informed buy, build or adapt decision
USE CASES
Useful scopes shaped around the work.
Workflow & Data Readiness
Review actual steps, sources, quality and access.
Pilot Design
Set test cases, cost assumptions, human approvals and success measures.
Build / Buy Decision
Compare existing tools, custom development and ongoing operation.
PILOT FRAMEWORK
From a business problem to a testable AI pilot
Review work, data and risk before choosing a tool, then define the evidence needed to expand or stop a pilot.
Inputs
- Business goal
- Current workflow
- Data & constraints
Outputs
- Prioritised use cases
- Pilot test set
- Success criteria
DELIVERABLES
Deliverables that can be accepted and operated.
Stakeholder and workflow review
Prioritised use-case shortlist
Data, integration and risk checklist
Pilot scope and success criteria
Next-step roadmap and unresolved assumptions
SCOPE & INVESTMENT
Project scope and pricing inputs
Scope follows the number of teams and workflows, data readiness and depth of technical review. Consulting is scoped separately from implementation and model usage.
- Teams and stakeholders
- Workflows and systems
- Access to representative data
- Prototype or technical-review depth
ACCEPTANCE & OPERATION
Acceptance and operating costs
Project acceptance criteria
- Deliver prioritised use cases with data and constraints
- Define pilot baseline, pass/stop criteria and decision owners
- Separate build, platform and operating costs
Ongoing costs to itemise in the proposal
- Usage-based model/API and connected-tool fees
- Evaluation and knowledge-source maintenance
- Monitoring, permissions and workflow changes
Test coverage and operating responsibilities are agreed for the project before work begins.
From scope to acceptance and handover
01 /
Understand
Discuss goals, audiences and constraints to define a useful scope.
02 /
Design
Make structure and interactions clear before development begins.
03 /
Build & test
Develop the system and test critical journeys across real devices.
04 /
Launch & support
Launch, share documentation and use evidence to guide improvements.
Agree test cases, reviewers, account ownership and support before work. Test critical journeys with approved data before launch.
FAQ
Questions before the work begins.
Must our data be ready before AI consulting?
No. Knowing which tasks take time, how the team works and where information lives is enough to assess whether the first step should be data preparation or an AI pilot.
Do we have to commission development afterwards?
No. The output should support a decision to use an existing tool, buy software or build a custom solution.
How do we judge pilot value?
Agree a baseline such as time per case, error rate or response time, then compare quality, cost and operational risk before scaling.
What do we receive to support the next investment decision?
The agreed scope can include prioritised use cases, required data and systems, a baseline, pilot pass/stop criteria and separate build and operating costs. Define documents and decision owners in the proposal. The recommendation may be to use an existing tool, prepare data first or defer AI development.
Have a problem worth scoping clearly?
Share the context, outcome and systems involved. We will help identify the questions to answer before investing.