Dr. Andrew Ng
Most organisations have run AI pilots. Few have moved beyond them. The gap is not technological – it is organisational. Building the internal structures, teams, and decision-making capacity to deploy AI at scale is the challenge most leadership teams have not yet solved. Without a systematic approach, AI investments accumulate without compounding.
Scaling AI from isolated pilots to enterprise-wide capability is the challenge Andrew Ng – who co-founded Google Brain, built Coursera to 150 million learners, and authored the AI Transformation Playbook – has spent his career helping organisations solve.
Full Profile
Why organisations work with Andrew Ng
- His AI Transformation Playbook – a named, five-step framework derived directly from building AI teams at Google and Baidu – gives organisations a structured implementation path, not principles. It is the difference between a direction and a plan.
- He has operated on both sides of the AI adoption challenge: as the person responsible for making two of the world’s largest technology companies into AI-first organisations, and as the founder of platforms that have trained more than 8 million people in AI skills. The failure modes at both ends are not theoretical to him.
- His “AI is the new electricity” thesis reframes AI as general-purpose infrastructure rather than a feature or a product – a distinction that changes how boards allocate capital and how leadership teams structure accountability.
- Named to Time’s inaugural Time100 AI list (2023) – recognising active, current influence in the field – and to Amazon’s board of directors. The former signals peer standing; the latter signals boardroom credibility.
- Through AI Aspire, developed in partnership with Bain & Company, he advises enterprises moving from AI experimentation to scalable transformation – giving organisations access to a perspective tested across venture building, hyperscale operations, and mass education.
Biography highlights
- Co-founded Google Brain – the research team credited with reshaping Google’s approach to artificial intelligence
- VP and Chief Scientist at Baidu, leading a 1,300-person AI team responsible for the company’s global AI strategy
- Co-founder and Chairman of Coursera (NYSE: COUR), now serving 150 million+ registered learners globally
- Founder of DeepLearning.AI – over 8 million people have completed AI courses through his programmes
- Named to Time100 Most Influential People (2012), Fast Company’s Most Creative People (2014), and Time100 AI (2023)
- Member of Amazon’s board of directors (appointed April 2024)
- Author of the AI Transformation Playbook and Machine Learning Yearning, both in wide circulation
- Holds a BSc from Carnegie Mellon, MSc from MIT, and PhD from UC Berkeley; co-authored over 200 research papers
Biography
Most AI pilot projects never become AI companies. Andrew Ng has spent years examining why – first as the founding lead of Google Brain and then as VP and Chief Scientist at Baidu, where he built a 1,300-person AI team. The gap between isolated AI experimentation and enterprise-wide capability is the problem he has studied from the inside.
His AI Transformation Playbook – drawn directly from that experience – sets out a five-step framework for taking AI adoption from initial momentum to company-wide infrastructure. It circulates widely because it treats AI transformation as an operational challenge, not a technology question. His thesis that AI is infrastructure the way electricity was infrastructure gives boards a strategic frame that holds across industries and business models.
Ng co-founded Coursera, which now serves over 150 million registered learners, and founded DeepLearning.AI, through which more than 8 million people have trained in AI skills. Building AI literacy at that scale – and measuring what works – gives him a concrete, tested perspective on what workforce transformation in practice actually requires. Few speakers can point to comparable operational evidence.
He holds degrees from Carnegie Mellon, MIT, and UC Berkeley, has co-authored over 200 research papers, and sits on Amazon’s board of directors. Named to Time’s inaugural Time100 AI list in 2023 – recognising current influence rather than historical contribution – he continues to co-found AI companies through AI Fund and advise enterprises on AI strategy through AI Aspire, a firm he built in partnership with Bain & Company.
Key speaking topics
- Enterprise AI adoption and transformation
- AI strategy for boards and C-suite leaders
- Agentic AI and the next phase of AI systems
- AI workforce development and capability building
- Responsible AI development and regulation
- Machine learning fundamentals and applied AI
- The economics of AI as general-purpose infrastructure
Ideal for
- C-suite executives and boards setting AI strategy and capital allocation
- Chief Technology Officers and Chief Digital Officers leading enterprise AI programmes
- CHROs and people leaders responsible for AI upskilling and workforce transformation
- Transformation leads and innovation teams moving from AI pilots to scaled deployment
Audience outcomes
- A structured framework for diagnosing where enterprise AI adoption is stalling and what to do next
- Clarity on how to build internal AI capability – teams, workflows, and governance – rather than relying on isolated project-by-project approaches
- A board-level frame for AI as infrastructure, with implications for investment, talent, and competitive positioning
- Practical understanding of what large-scale AI workforce development requires, drawn from programmes that have trained millions
- A more grounded view of responsible AI development – distinguishing credible risk from speculative concern – from a practitioner who has argued these positions in public and in policy
Talks
Examines how machines learn from unstructured data to advance fields including computer vision and natural language processing, and what this means for organisations building on AI foundations.
Key takeaways:
- How deep learning extracts meaningful patterns from unstructured data, and where this capability is practically applied
- The distinction between supervised and unsupervised learning, and when each approach is appropriate for enterprise use cases
- What the current state of machine learning research means for organisations investing in AI infrastructure
Explores how artificial intelligence is reshaping learning – enabling personalised, adaptive experiences at scale – and what this means for organisations responsible for workforce capability development.
Key takeaways:
- How AI creates personalised learning pathways that traditional training models cannot replicate at scale
- What expanding access to quality education means for global talent pipelines and competitive advantage
- The implications of lifelong learning as a structural requirement, not a cultural aspiration
Reviews emerging developments in AI and robotics across manufacturing, healthcare, and transportation, with a focus on practical deployment and the conditions for responsible, human-centred implementation.
Key takeaways:
- How robotics and AI are transforming operations in key sectors, and where the near-term opportunity is concentrated
- What effective human–machine collaboration looks like in practice, beyond the hype
- The ethical and governance considerations organisations must embed before deployment, not after