06 / AI CONSULTING

AI consulting that turns business questions into a testable pilot

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.

AI Opportunity Review

Select frequent, measurable work with available inputs.

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

Scoped around the work—not the service label.

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

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.

EXPLORE NEXT

Useful context for choosing the right scope.

NEXT STEP

Have a problem worth scoping clearly?

Share the context, outcome and systems involved. We will help identify the questions to answer before investing.

Start the conversation