Dr Paul Dongha

Paul Dongha's official speaking topics are listed below: 

How to Create and Operationalise an AI Governance and Compliance Program From Scratch

Adopting and deploying AI at scale, is not just an IT activity; it is a leadership imperative, which demands involvement from multi-disciplinary teams. It needs to be driven from the boardroom with clear accountability and a strong mandate.

• Designing Foundational Governance Frameworks: Master the blueprint for building an AI governance structure from the ground up, ensuring ethical alignment and accountability at every stage of the AI lifecycle.
• Operationalising Global Compliance: Transition from theory to practice by integrating regulatory requirements and industry standards directly into existing technical workflows and business processes.
• Risk Mitigation and Ethical Guardrails: Implement robust auditing practices and bias-detection mechanisms to protect your organisation from legal, reputational, and operational risks.
• Scalable Oversight for Rapid Innovation: Establish a dynamic governance model that evolves alongside emerging AI technologies, allowing for fast-paced innovation without compromising safety or security.

How to Mobilise, Create and Set Up Your Organisation’s AI Strategy in Line With Your Overall Business Strategy

• Bridging the Strategic Gap: Master the art of aligning AI roadmaps with core business objectives, ensuring that technology serves as a value multiplier rather than a siloed experiment.
• Architecting the AI-Ready Culture: Insights into mobilising cross-functional leadership and creating organisational "data-literacy" required to move from initial pilot programs to enterprise-wide adoption.
• Operationalising Innovation: A deep dive into the practicalities of setting up specialised "AI Centres of Excellence" (CoE) and selecting the right tech stack to future-proof your competitive advantage.

How to Mitigate Against the Most Prevalent Risks of AI

A step-by-step roadmap to ensure that even as technology rapidly shifts, you can mitigate against some of the biggest categories of risk:

• Accuracy and reliability – how can you measure accuracy in AI and what is ‘good enough’?
• Fairness and bias – how to identify and resolve issues that can alienate your customers and regulators
• Interpretability, explainability and transparency – how to explain decisions made by AI
• Accountability: how do you ensure the right people in your organisation take ownership for AI decisions?
• Privacy – how do you prevent data leaks from AI and protect your customers’ private data?
• Security – how can you prevent cyber-attacks and AI jail-breaking?
• Intellectual property and confidentiality – how can you ensure your AI does not break copyright and IP laws?

Preparing Your Workforce for AI - How Do You Ensure Your People Can Create an AI-First Organisation

• Cultivating an AI-First Mindset: Strategies for shifting organisational culture from technology-aversion to "augmented intelligence," ensuring employees view AI as a partner in productivity rather than a threat to job security.
• Strategic Upskilling and Reskilling Roadmaps: A blueprint for identifying future-critical skills and implementing continuous learning frameworks that evolve as rapidly as the AI landscape itself.
• Redesigning the Human-in-the-Loop Workflow: Expertise in restructuring roles and responsibilities to maximise human creativity and empathy while leveraging AI for high-speed data processing and automation.
• Leading Through the Transition: Leadership tactics for managing the "change fatigue" associated with rapid digital transformation, focusing on transparent communication and psychological safety.

Sustainable and Durable AI - How to Navigate the Tension Between Technological Performance and Environmental Sustainability

• Architecting Carbon-Efficient AI Systems: Strategies for optimising AI use (eg, utilising smaller, "lean" architectures and grid-aware computing—to reduce energy consumption without sacrificing accuracy.
• Navigating the Ethical-Environmental Trade-off: Expert frameworks for balancing the "hidden costs" of AI—including water scarcity for data centre cooling and the extraction of rare minerals—against the strategic business value of high-compute models.
• Decoupling Innovation from Emissions: A blueprint for operationalising "Green AI" through integrated ESG frameworks.
• Sustainable Governance as a Competitive Advantage: Guidance on building accountability through transparent auditing and ethical alignment, ensuring AI initiatives drive long-term value rather than reputational risk.
• Financial Operations – how to guide organisations in forecasting AI  compute costs and the trade-offs with accuracy and ethical alignment.

What is Agentic AI - Myths and Reality - How to Cut Through the Noise With Technical Realism and Business Acumen

• Demystifying Autonomous Agency: Distinguishing between scripted automation and true "agentic" behaviour, providing an easy-to-grasp blueprint to help leaders identify which systems can actually reason, plan, and execute multi-step tasks and which simply cannot.
• The Reality of the "Agentic Loop": A deep dive into the current limitations of LLM-based agents—from hallucination risks to infinite loops—and the practical engineering required to move from experimental demos to reliable production tools.
• Separating Hype from ROI: Strategic frameworks for evaluating "Agentic AI" vendors and internal projects, ensuring investments are focused on measurable gains rather than chasing the "all-capable digital employee" myth.
• Safety and Human Oversight in an Agentic World: Expert guidance on the "Human-in-the-Loop" architectures necessary to maintain control and accountability as AI agents gain increasing autonomy over digital workflows and sensitive data.

Artificial General Intelligence, Artificial Super Intelligence - What it Means, When it Might Be Achieved and What That Means for Society

• Navigating the Path to AGI: Decoupling the hype from technical reality by defining "Artificial General Intelligence"—from today's emerging reasoning models to systems that match human cognitive flexibility across every domain.
• The Superintelligence Divide: Strategic analysis of the transition from AGI to ASI (Artificial Superintelligence), exploring the "intelligence explosion" hypothesis and the potential for machines to solve problems beyond human comprehension.
• Managing Existential Risk and Alignment: The ever-vexing ‘alignment problem'— ensuring that as systems gain superhuman planning and self-improvement capabilities, they remain fundamentally compatible with human values and safety.
• Post-Scarcity Economics and the Future of Value: A look at the societal shifts triggered by AGI, including how organisations must redefine work, merit, and purpose in an era of radical abundance.
• Universal Basic Income: explore the viability of allowing society to receive an income in a world of abundance, divorced from their own productivity.

• 2025 – Co-Author of "Governing the Machine", named in the Amazon top 10 bestsellers in two categories & in the FT top 5 Business Books of the Month
• 2024 – Speaker at The AI Summit London
• 2022 – Group Head of Data & AI Ethics, Lloyds Banking Group
• 2021 – AI Ethics Principal Researcher, Fujitsu Research of Europe
• 2021 – Climate Risk IT Program Manager, HSBC
• 2016 – IT Director & Global Program Manager, Credit Suisse
• 2014 – IT Director & IT Risks Systems Programme Manager, Credit Suisse
• 2011 – IT Director, Commerzbank
• 2008 – IT Project Manager of the Finance Transformation Programme, HSBC
• 2007 – Head of Derivatives Product Control, HSBC
• 2004 – Programme Manager Risk, Banking, Treasury & Trade Finance, Standard Bank
• 2003 – Head of Credit Risk Systems, ABN AMRO Bank
• 1994 – Teaching Associate, University of Manchester
• 1990 – Research Associate, University of Manchester