AI TRAINING
Delivering AI Diagnostics at Client Engagements
Run end-to-end AI diagnostic engagements that produce credible, decision-ready reports clients act on.
What it covers
This practitioner programme equips consultants with a repeatable methodology for scoping, running, and presenting AI readiness diagnostics at client organisations. Participants work through a full engagement simulation: stakeholder interviews, data maturity scoring, use-case prioritisation, and steering-committee delivery. The format combines structured frameworks with live practice sessions so attendees leave with reusable templates, a personal scoring rubric, and the confidence to sell and deliver these engagements independently.
What you'll be able to do
- Design and price a structured AI diagnostic engagement scoped to a client's size and sector
- Facilitate a 90-minute stakeholder discovery workshop and extract scored readiness signals
- Apply a weighted scoring rubric to assess data maturity, organisational readiness, and use-case viability
- Write a structured diagnostic report with prioritised recommendations a C-suite audience can action immediately
- Deliver a 20-minute steering-committee presentation and handle pushback on AI investment decisions
Topics covered
- Scoping and pricing an AI diagnostic engagement
- Designing stakeholder interview guides and data-gathering questionnaires
- Scoring frameworks: data maturity, AI readiness, and use-case feasibility
- Facilitation techniques for cross-functional discovery workshops
- Use-case prioritisation matrices (impact vs. effort vs. risk)
- Structuring and writing a consultant-grade diagnostic report
- Steering-committee presentation design and objection handling
- Engagement risk management and ethical red flags
Delivery
Delivered over three to four weeks in a blended format: two live full-day virtual workshops bookending the programme, with asynchronous case work in between. Participants complete a real or simulated client diagnostic between sessions and receive written and peer feedback. Materials include a diagnostic playbook (PDF + editable), scoring spreadsheet templates, interview guide bank, and a slide deck master for final reports. Hands-on practice represents approximately 60% of total contact time.
What makes it work
- Using a pre-agreed scoring rubric shared with the client sponsor before the kickoff, reducing scope disagreements later
- Running at least one cross-functional workshop mid-engagement to surface conflicting priorities before the final report
- Anchoring every recommendation to a quantified business outcome the client has already validated
- Building a short 'next-steps decision tree' into the steering-committee deck to convert insight into commitment
Common mistakes
- Jumping straight to tool recommendations before completing a thorough data and process audit
- Interviewing only IT or data leads and missing operational and commercial stakeholders
- Producing a generic maturity score without tying findings to the client's specific business priorities
- Presenting the report as a document review rather than a facilitated decision-making session
When NOT to take this
This programme is not appropriate for in-house AI teams tasked with internal capability assessments, the engagement-management and client-relationship components are irrelevant, and a lighter internal audit framework would serve them better.
Example providers
This page describes a training topic. It gives no price or duration: those depend on the provider and your team. The free diagnostic tells you which trainings your team needs first.