Overview

This 5-day in-person Enterprise AI Taskforce Enablement Programme equips a cross-functional taskforce to translate enterprise strategy into governed, secure, and scalable AI execution. The programme moves from strategic alignment and use-case prioritisation (Day 1), into governance and operating model design (Day 2), architecture and secure-by-design implementation (Day 3), data governance and operational monitoring (Day 4), and finally execution planning and institutionalisation (Day 5). Participants will complete structured workshops and simulations to produce a practical set of enterprise-ready artefacts, including AI ambition and readiness outputs, governance and policy drafts, risk and release gate models, architecture and security baselines, monitoring frameworks, and 90-day/12-month roadmaps—enabling consistent decision-making, accountability, and controlled scaling of AI across the organisation.

Course Objectives

On completing this course, the participants will:

Assess enterprise AI maturity and readiness gaps (people, process, data, technology, governance).
Define AI ambition and align initiatives with corporate strategy and risk appetite.
Identify, scope, and prioritise AI use cases using impact, feasibility, and risk criteria.
Design AI governance and operating model across the full AI lifecycle (RACI, decision rights, escalation, release gates).
Establish implementation foundations: architecture patterns, security baseline (OWASP LLM controls), data governance, and monitoring metrics.

Target Audience

The Enterprise AI Taskforce course target audience includes but is not limited to:

IT / Technology Teams
Risk & Compliance Functions
Enterprise / Solution Architects
Business Unit Representatives
Executive Sponsors / Decision Makers

Day 1 – Enterprise Strategy & AI Alignment

Theme: Strategic Positioning & Organisational Readiness

Learning Objectives

Participants will be able to:

Assess organisational AI maturity
Define enterprise AI ambition level
Align AI initiatives with corporate direction
Identify structural readiness gaps

Detailed Topics

AI as Enterprise Infrastructure
AI beyond experimentation
AI’s impact on operational models
AI maturity model (People, Process, Data, Technology, Governance)
AI Ambition Levels
Assistive AI
Augmented AI
Semi-autonomous systems
Autonomous decision systems
Risk Appetite & Regulatory Context
Risk-based AI classification
Regulatory overview
Ethical vs operational risk boundaries

Activities

AI Maturity Self-Assessment Workshop
Risk Tolerance Mapping Exercise
Ambition Statement Drafting

Deliverables

AI Ambition Statement
Readiness Gap Assessment
Risk Alignment Summary

Learning Objectives

Participants will be able to:

Identify operational AI opportunities
Define scope and boundaries clearly
Prioritise use cases based on feasibility and risk

Detailed Topics

Use-Case Discovery Framework
Process bottlenecks
Repetitive tasks
Knowledge-intensive workflows
Risk mitigation use cases
Operational Framing
Human-in-the-loop design
Workflow integration
Data requirements
Risk exposure considerations
Prioritisation Logic
Impact
Feasibility
Risk profile
Strategic alignment

Activities

Cross-functional Use-Case Workshop
Feasibility & Risk Scoring
Scope Definition Exercise

Deliverables

Ranked AI Use-Case Portfolio
Use-Case Definition Templates

Day 2 – Governance & Operating Model Design

Theme: Institutional Structure & Accountability

Learning Objectives

Participants will be able to:

Design AI governance structure
Define decision rights
Establish lifecycle accountability

Detailed Topics

Governance Structure
AI Taskforce vs Steering Committee
AI Center of Excellence (CoE)
Reporting structure
AI Lifecycle Governance
Intake → Design → Build → Deploy → Monitor → Retire
Approval gates
Escalation paths
RACI & Accountability
Role clarity
Ownership post-deployment
Cross-functional alignment

Activities

Governance Structure Mapping
RACI Development Workshop
Escalation Flow Simulation

Deliverables

AI Governance Structure
RACI Matrix
Escalation Framework

Learning Objectives

Participants will be able to:

Categorise AI risk levels
Build AI risk registers
Draft AI governance policy structure
Define release gate model

Detailed Topics

Responsible AI Operationalisation
Risk Categorisation (Low/Medium/High Impact AI)
Vendor & Third-Party AI Risk
Audit & Compliance Readiness

Activities

AI Risk Register Development
Policy Drafting Exercise
Release Gate Definition Workshop

Deliverables

AI Risk Register
AI Policy Framework Draft
Release Gate Model

Day 3 – Enterprise Architecture & Secure Implementation

Theme: Secure-by-Design & Technical Foundations

Learning Objectives

Participants will be able to:

Design enterprise AI architecture blueprint
Select appropriate GenAI deployment pattern
Map integration points

Detailed Topics

AI Architecture Layers
Interface
Orchestration
Model
Retrieval
Governance
Monitoring
Deployment Patterns
Prompt-only
RAG
Tool-augmented
Agentic workflows
Enterprise Integration
CRM
ERP
Data Warehouse
Logging & observability

Activities

Architecture Blueprint Workshop
Deployment Pattern Comparison
Integration Risk Mapping

Deliverables

Architecture Blueprint
Deployment Decision Matrix
Integration Plan

Learning Objectives

Participants will be able to:

Identify LLM risks
Apply OWASP LLM controls
Design AI security baseline

Detailed Topics

OWASP LLM Top 10
Prompt Injection Mitigation
Data Leakage Controls
Insecure Output Handling
Kill-Switch Governance

Activities

Prompt Injection Tabletop Exercise
Security Baseline Development
Incident Escalation Simulation

Deliverables

AI Security Baseline
Incident Escalation Flow
Security Review Checklist

Day 4 – Data Governance, Responsible Deployment & Monitoring

Theme: Control & Operational Resilience

Learning Objectives

Participants will be able to:

Classify enterprise data for AI usage
Define access & retention policies
Design AI Data Pack template

Detailed Topics

Data Classification Models
Permitted vs Restricted AI Data
Provenance & Lineage
Knowledge Asset Strategy

Deliverables

AI Data Governance Framework
Data Classification Matrix
AI Data Pack Template

Learning Objectives

Participants will be able to:

Translate principles into release gates
Define human oversight mechanisms
Assess organisational impact

Deliverables

AI Release Gate Checklist
Change Impact Plan
AI Communication Framework

Learning Objectives

Participants will be able to:

Define AI performance metrics
Design monitoring dashboards
Establish SLA & regression controls

Deliverables

Evaluation Framework
Monitoring KPI Dashboard
Escalation Criteria Matrix

Day 5 – Execution & Institutionalisation

Theme: Operationalisation & Scaling Governance

Learning Objectives

Participants will be able to:

Sequence AI pilots logically
Assign executive sponsors
Align governance cadence

Deliverables

Approved Pilot Sequence
Governance Coordination Plan

Learning Objectives

Participants will be able to:

Develop milestone-based execution plan
Define governance cadence
Track operational risks

Deliverables

90-Day AI Action Plan
Governance Calendar
Risk Mitigation Tracker

Learning Objectives

Participants will be able to:

Plan AI capability institutionalisation
Define operating rhythm
Present strategy to executive leadership

Deliverables

12-Month AI Transformation Roadmap
Executive Presentation Deck
AI Capability Development Plan

Final Programme Outputs：

Participants leave with:

AI Ambition Framework
AI Governance & Operating Model
AI Risk Register
AI Policy Framework
Architecture Blueprint
AI Security Baseline
Data Governance Framework
Release Gate Model
Evaluation & Monitoring Framework
90-Day Execution Plan
12-Month Institutionalization Roadmap

Programme scope, delivery arrangements and suitability are confirmed during enquiry.