Highlights
By the end of the day, participants will be able to:
- Identify high-value AI opportunities across real assets without over-investing in low-ROI initiatives
- Understand how AI improves asset performance, reliability, and lifecycle management
- Assess AI solutions through a data integrity, governance, and risk lens
- Design controls for data quality, model reliability, and accountability
- Define their role in responsible AI oversight at executive level
- Build a credible AI roadmap aligned to asset strategy and regulatory expectations
Course Details
Session 1: AI, Real Assets & Executive Accountability
Purpose - Frame AI as a leadership and governance issue, not a technology project.
Key Topics
- Why AI is now unavoidable in asset-intensive sectors
- Where AI sits alongside:
- Asset management strategy
- Capital allocation
- Risk ownership
- Executive accountability for AI outcomes
- Differentiating:
- Operational optimisation
- Strategic advantage
- Compliance-driven adoption
Outputs
Individual executive action plan Key questions to take back to:- Asset teams
- Data teams
- Risk and audit
Session 6: Executive Action Planning & Board Readiness
Purpose - Ensure leaders leave board-ready.
Key Topics
What boards will ask about AI in real assets Framing AI decisions in:- Risk language
- Value language
- Assurance language
Group Exercise
90-Day / 12-Month / 3-Year AI RoadmapFocused on assets, data integrity, and oversight — not tools.
Session 5: AI Roadmapping for Asset-Intensive Organisations
Purpose - Translate insight into a realistic, staged roadmap.
Key Topics
Sequencing AI initiatives:- Quick wins vs foundational capability
- Data
- People
- Governance
- Change management
- Performance metrics
- Risk indicators
- Assurance evidence
Discussion
What decisions should never be fully automated in asset environments?
Session 4: Governance, Risk & Responsible AI Oversight
Purpose - Equip leaders to govern AI safely and credibly.
Key Topics
Executive-level AI governance models Oversight vs management vs implementation Key risks:- Model opacity
- Drift and decay
- Over-automation
- Safety and compliance exposure
- Existing risk frameworks
- Asset assurance
- Regulatory expectations
Executive Exercise
Data Trust Stress Test:Participants assess one critical asset dataset against integrity and AI-readiness criteria.
Session 3: Data Integrity, Quality & Trust in AI Systems
Purpose - Address the real blocker: data trust.
Key Topics
Why AI amplifies data integrity problems Common data failures in asset-heavy environments:- Fragmented systems
- Poor master data
- Sensor noise
- Human workarounds
- Anomaly detection in data streams
- Validation and reconciliation
- Automated quality checks
- Data ownership
- Lineage
- Auditability
Case Examples
Utilities, transport, property portfolios, energy assets Where AI delivered value — and where it didn’t
Session 2: Asset Performance & Value Creation with AI
Purpose - Focus on commercially defensible use cases tied to asset value.
Key Topics
AI applications across the asset lifecycle:- Design and commissioning
- Operations and maintenance
- Renewal and disposal
- Performance drift
- Data errors
- Emerging failures
- OPEX reduction
- CAPEX deferral
- Service-level performance
Discussion
“Where are we currently exposed — through not using AI?”
Who should attend
VPs, Directors, Heads of Asset Management, Data Integrity, Engineering, Digital, Risk, Compliance, and Transformation in asset-intensive organisations.
Feedback
4.8 out of 5 average
"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"
Brian Leek, Data Analyst, May 2022
