AI System Advisory & Development • AA-05

AI Capability Development

Give decision-makers and builders the skills the strategy assumes they have.

Scope

What the engagement covers.

AI programmes stall on capability, not technology. This service builds practical, role-specific skill across executives, delivery teams and control functions.

Included capabilities

AI awareness and ethics campaigns across the organization
Executive and staff training on AI governance, risk and obligations
Practitioner training for delivery, data and security teams
Red teaming and scenario planning exercises
Curriculum development and certification-oriented programmes

Outputs and deliverables

Capability assessment and skills gap analysis
Role-based curriculum and training materials
Exercise packs with facilitator guidance
Certification framework and refresher schedule

Workflow

How it is delivered, step by step.

Each step has an owner, an entry condition and an artefact that has to exist before the next step begins.

01AssessCurrent capability and the decisions each role has to make.

02DesignRole-based curriculum with practical, organization-specific content.

03DeliverBriefings, workshops, exercises and hands-on sessions.

04ExerciseScenario and red-team exercises against real systems.

05CertifyAssessment, internal accreditation and refresher cadence.

Use cases

Where this is typically applied.

Use case 01

Organizations rolling out AI tools to a broad workforce

Use case 02

Control functions needing to supervise AI they did not build

Use case 03

Building an internal AI Office from existing staff

Delivery model

The operating pattern for AI System Advisory & Development.

The same delivery discipline applies across every capability in this line, so combined engagements stay coherent.

Discover

Inventory use cases, models, data, vendors, users and regulatory context.

Classify

Assess materiality by impact, autonomy, data sensitivity and deployment context.

Design

Governance, roles, policy, controls, lifecycle gates and evidence requirements.

Test

Risk assessment, architecture review, red teaming and scenario exercises.

Implement

Controls in delivery workflows, platforms, models and human oversight.

Sustain

Reporting, incidents, change, re-assessment, training and improvement.

Integration

Model registry, feature store and MLOps pipelines for lifecycle gates.
GRC platform for AI control mapping, evidence and issue management.
Data catalogue and lineage tooling for dataset provenance.
Security stack for logging, monitoring and incident handling of AI systems.

Engagement approach

Baseline engagements are a readiness and risk assessment. Build engagements operationalize the framework. Assurance engagements test models, LLM applications, agents and the governance controls around them, then validate remediation.

Other capabilities in AI System Advisory & Development.

View the full AI System Advisory & Development line →

AA-01