AI Ethics & Responsible Technology

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

Why organisations work with Reshma Saujani She has founded and scaled two of the most recognised gender-equity institutions in the United States, Girls Who Code and Moms First, and can speak to what actually moved the numbers in each. She brings a working policy and product track record, including federal Marshall Plan for Moms legislation and the PaidLeave.ai tool used by tens of thousands of parents in its New York pilot. Her argument on perfectionism, drawn from “Brave, Not Perfect” and her TED stage, gives senior leaders a concrete diagnosis of why high-performing women self-select out of risk and visibility. “Pay Up” reframes the future of women and work around the motherhood penalty rather than individual ambition, useful for CHROs designing caregiver policy that holds under scrutiny. A New York Times bestselling author with a Yale JD and Harvard MPP, recognised by Fortune, Forbes and Fast Company, which travels with senior boardroom and executive-program audiences. Biography highlights Founder and CEO of Moms First, the national advocacy movement for paid leave, affordable childcare, and pay equity for caregivers. Founder and former CEO of Girls Who Code, which has reached hundreds of thousands of girls through in-person programming and a New York Times bestselling book series. New York Times bestselling author of “Pay Up”, “Brave, Not Perfect”, “Women Who Don’t Wait in Line”, and the Girls Who Code series. TED2016 speaker, “Teach girls bravery, not perfection”, one of the most viewed TED talks on women in technology. McGraw Prize in Education; Fortune World’s Greatest Leaders; Forbes Most Powerful Women Changing the World; WSJ Magazine Innovator of the Year. Yale Law School (JD), Harvard Kennedy School (MPP). First Indian American woman to run for US Congress. Biography The female pipeline in technology and senior leadership does not leak in the places organisations assume. It leaks at the points where the workplace stops accommodating risk-taking and caregiving. Reshma Saujani has spent fifteen years building the institutions that address both of those failures directly. Girls Who Code, which she founded in 2012 after running for Congress and seeing the gender gap in computing classrooms first-hand, became one of the most recognised pipeline interventions in American technology, with hundreds of thousands of girls reached through in-person programmes and a New York Times bestselling book series. Her 2016 TED talk “Teach girls bravery, not perfection” reframed the problem as cultural, not technical, and is one of the most cited talks on women in tech. Moms First, which Saujani launched in 2021 as the Marshall Plan for Moms, moved her work upstream into the structural economics of caregiving. The organisation has driven federal legislation, the creation of a New York City task force, and PaidLeave.ai, a working AI tool that helps parents claim benefits they are owed and processed tens of thousands of users in its first New York pilot. Her arguments land in boardrooms because they come with proof of execution. “Brave, Not Perfect” and “Pay Up”, both New York Times bestsellers, give senior leaders a usable diagnosis of why their female talent stalls and what to change. A Yale JD and Harvard MPP, recognised by Fortune, Forbes, Fast Company, and WSJ Magazine, she is read by the policy world and the executive audience at the same time. Key speaking topics Women in technology and leadership The motherhood penalty and caregiver economics Paid leave, childcare, and workplace policy design Perfectionism, risk, and the career consequences for women Building gender-equity institutions at scale AI applied to social policy and benefits access Ideal for CHROs and chief people officers redesigning caregiver, parental leave, and return-to-work policy Boards and executive committees benchmarking gender representation against credible structural interventions Heads of DEI, women’s leadership networks, and high-potential women’s development programmes Technology leaders responsible for early-career pipeline and retention of women in engineering Audience outcomes A clear structural account of why women stall in senior pipelines, separating cultural drivers from policy drivers Concrete examples of paid leave, childcare, and pay-equity interventions that have moved measurable outcomes A working diagnosis of perfectionism as a career-limiting pattern, with language leaders can use with high-potential women Evidence of how AI can be applied to benefits access and social policy, drawn from PaidLeave.ai A sharper view of the business case for caregiver infrastructure, beyond the standard wellbeing framing

Why organisations work with Yonah Welker Direct line into the European policy machinery shaping AI rules, through working group contributions to the EU AI Code of Practice, DG CNECT’s digital and critical infrastructure work, and EU ICT and standardisation programmes. Published, citable expertise on how AI systems treat disability, neurodiversity, and other designated groups, with frameworks and a MOOC catalogued by OECD.AI, a specialism few governance advisers can match. Cross-institutional fluency across OECD, UN, UNESCO, and WHO governance processes, useful for organisations whose AI strategy now has to answer to several overlapping frameworks at once. Practical command of how governance applies to AI in physical and public deployment, including robotics and assistive systems, where compliance obligations under the EU AI Act and emerging harmonised standards are least settled. A practitioner-policy bridge rather than a pure academic, with hands-on evaluator and rapporteur roles across EU Horizon and EIT/EIC programmes, which is the lane most boards and compliance leads now need filled. Biography highlights AI expert listed in the OECD.AI community directory; visiting lecturer and evaluator affiliated with MIT collaborative work, and contributor to OECD.AI’s Wonk blog. Contributor to the EU AI Code of Practice (working Groups 2 and 3), DG CNECT digital and critical infrastructure programmes, and EU ICT standardisation work. Contributor of input to OECD (G7 Hiroshima AI Process), UN (ITU and IGF), UNESCO, NIST (Risk Management Framework), and White House PCAST processes. Lead author and coordinator on the forthcoming UNESCO/UN AI Science Report, and co-author of the MIT AI Safety Repository. Evaluator, rapporteur, and quality controller across EU Horizon, StandICT, and EIT Health, Deeptech, and Creativity programmes; member of the EDPB expert pool on embodied AI privacy. Former curator of the Global AI Summit and adviser to a national data and AI authority in the EMEA region; speaker at the AI Action Summit (Paris/Sorbonne), Sciences Po, Technical University of Munich, AI Safety Korea, and the Delphi Economic Forum; jury and peer reviewer for the MIT Global Competition, NeurIPS, and IEEE journals. Biography The EU AI Act, the OECD framework, UNESCO’s ethics recommendation, and the G7 Hiroshima Process now govern the same operating decisions, and they do not line up neatly. The people who work inside several of these processes at once, rather than reading them from outside, are few. Welker is one of them. He has contributed to the EU AI Code of Practice working groups and to DG CNECT’s critical infrastructure work. He sits on the EDPB expert pool examining embodied AI privacy, and is lead author and coordinator on the forthcoming UNESCO/UN AI Science Report. His input has also fed NIST and White House PCAST processes, and he has advised a national data and AI authority in the EMEA region alongside his EU Commission work. His distinctive angle is a lens few governance specialists share. His work centres on how AI systems treat designated groups: people with disabilities, neurodivergent users, and others where an algorithm can quietly grant or deny access. That question is now moving into robotics, assistive systems, and AI that acts in public space, where the frameworks are thinnest. The disability-centred AI MOOC catalogued in the OECD.AI tools repository is one concrete marker of it. For senior leaders, the value is operational. He can set out what an EU AI Act audit will actually examine, and what a defensible accessibility claim looks like in a deployed system. He can also show how the OECD, UN, and EU layers connect to a decision a general counsel or risk committee faces next quarter. Key speaking topics AI governance and the EU AI Act Algorithmic accountability and AI audit Disability-centred and accessibility AI policy Neurodiversity and inclusive AI design Sovereign AI and critical digital infrastructure AI safety and responsible deployment Public sector AI and citizen-facing systems Ideal for General counsel, chief compliance officers and CTOs preparing for EU AI Act enforcement Heads of AI, responsible AI leads and digital ethics committees Public sector and intergovernmental bodies designing AI policy Boards reviewing AI risk exposure across accessibility, bias and public-trust dimensions Audience outcomes A clearer map of how the EU AI Act, OECD framework and UN/UNESCO instruments interlock for their organisation Specific questions to put to vendors, internal teams and audit committees on algorithmic accountability A working definition of what disability-centred and accessibility AI obligations look like operationally Sharper instinct for where reputational and regulatory risk concentrates in current AI deployments A view of where European AI policy is heading next, drawn from inside the working groups shaping it

Why organisations work with Richard Foster-Fletcher Richard tests the latest AI systems at the limits of their reasoning and judgement. He works across models from leading AI labs and is regularly invited to evaluate new releases. His research examines the distance between frontier capability, the AI available inside companies, and what those organisations can then decide and do. Through MKAI, Richard publishes independent empirical studies on how advanced AI changes organisations, including an analysis of 150 SEC 10-K filings across 50 US public companies. His work is independent of technology vendors and consulting firms. His research identifies seven recurring Structural Dynamics of enterprise AI adoption. He has advised UK Parliament and sits on the United Nations Environment Programme Resilience Frontiers Advisory Group, and has spoken for Salesforce, Telefónica, Oracle, and ARM, and at LSE, Imperial College London, UCL, and the University of Oxford. Biography highlights Author of the forthcoming The AI Gap (Kogan Page, June 2027). Chair of MKAI, the independent AI research organisation he founded in 2019. Author of the seven-pattern framework on the Structural Dynamics of enterprise AI adoption. Published a working paper on SSRN, The First Annual Reports of the LLM Era, analysing 150 SEC 10-K filings across 50 US public companies. Publishes What Still Matters, a weekly essay series read by over 6,000 subscribers. Has advised UK Parliament and sits on the United Nations Environment Programme Resilience Frontiers Advisory Group. Has spoken for Salesforce, Telefónica, Oracle, and ARM, and at LSE, Imperial College London, UCL, and the University of Oxford. Named among Favikon’s UK Top 20 AI Researchers and Entrepreneurs in 2026, and a LinkedIn Top Voice in AI. Biography Advanced AI systems can now reason and make judgements that go well beyond the tools most companies have in daily use. That gap is the subject of Richard Foster-Fletcher’s research and of his forthcoming book, The AI Gap. One of the UK’s leading independent researchers in the field, his talks examine what the gap means for the decisions organisations make and the commitments those decisions rest on. The AI Gap, to be published by Kogan Page in June 2027, examines the distance between the capability a company has placed into use, the capability its commitments require, and the evidence connecting the two. Richard works directly with the latest systems from leading AI labs. He tests their reasoning and judgement, and compares how they perform from one release to the next. He also studies what changes when those systems enter governed organisational environments. He chairs MKAI, founded in 2019, which publishes independent empirical research on AI and organisations. Recent studies have examined corporate capability reporting, the effect of organisational instructions on AI judgement, responsibility for AI-assisted decisions, and the visibility of controls placed around enterprise systems. One analysed 150 SEC 10-K filings across 50 US public companies. His research identifies seven recurring Structural Dynamics of enterprise AI adoption. Richard has advised UK Parliament and sits on the United Nations Environment Programme Resilience Frontiers Advisory Group. He has given talks for Salesforce, Telefónica, Oracle, and ARM, and at LSE, Imperial College London, UCL, and the University of Oxford. He was named among Favikon’s UK Top 20 AI Researchers and Entrepreneurs in 2026 and is a LinkedIn Top Voice in AI. Key speaking topics Artificial intelligence and organisational capability Executive decision-making and judgement Organisational strategy under advanced AI AI governance and capability constraints Risk, responsibility, and evidence Frontier AI and enterprise availability Workforce capability and the future of work Ideal for Main-stage audiences seeking an evidence-based account of what leading AI systems can now do. Boards and executive committees examining commitments that depend upon AI capability. P&L holders responsible for results whose delivery assumes that AI can perform specific work. Governance, risk, and audit audiences examining the evidence behind organisational AI decisions. Audience outcomes An evidence-based account of where leading AI systems have reached. A basis for separating material advances from repackaged capability and unsupported claims. A clear distinction between the result being promised, the system being used, and the evidence connecting them. A view of the distance between frontier capability and the AI available inside companies. What remains untested when a financial or strategic commitment depends on AI.

Why organisations work with Tia White Operator perspective from inside a hyperscaler. She runs go-to-market, product and engineering for an AWS AI portfolio tracking over USD 25M in profit, including some of the fastest-growing services at AWS. A track record of taking generative AI and personalisation from theory into customer-facing product. She led the introduction of Personalized Search, Next Best Action and generative AI capabilities into Amazon Personalize. Cross-sector enterprise fluency. Before AWS she held senior technology roles at Wells Fargo, Capital One and JPMorgan Chase, leading regulated digital transformation programmes in financial services. A specific point of view on what separates AI investments that compound from AI investments that stall, grounded in current AWS customer work rather than vendor abstraction. A credible voice on building the technical leadership bench behind AI, including women and Black technologists, that boards are now being asked to account for. Biography highlights General Manager, AI and Machine Learning, Amazon Web Services. First Black woman hired as a GM in Machine Learning, AI and Analytics at AWS. Director of Engineering at a Fortune 100 company before age 30. Prior senior technology roles at Wells Fargo, Capital One and JPMorgan Chase. Led the launch of new Amazon Personalize capabilities including Personalized Search, Next Best Action and generative AI features; speaker at AWS re:Invent 2022 on personalisation. Featured in Forbes; keynote and emcee credits include SXSW and the Grace Hopper Celebration. Board and advisory roles include Rewriting the Code. Biography Generative AI has moved faster than most enterprise operating models can absorb. Inside AWS, Tia White runs the AI and Machine Learning business that customers turn to when a pilot has to become a product. She owns go-to-market, product and engineering for an AI portfolio tracking over USD 25M in profit, including some of the fastest-growing services at AWS. Her work at AWS includes leading the next generation of Amazon Personalize, with features such as Personalized Search, Next Best Action and generative AI capabilities now used by enterprises to lift engagement and revenue. She has spoken at AWS re:Invent on personalisation as an operating discipline rather than a marketing feature, and is regularly cited by AWS as a voice on what Gen Y and Gen Z customers expect from personalised experiences. Before AWS she held senior technology roles at Wells Fargo, Capital One and JPMorgan Chase, including the leadership of public cloud migrations in financial services. She reached Director of Engineering at a Fortune 100 company before the age of 30. That financial services background gives her a working answer to the questions regulated industries are now asking about generative AI, data, privacy and risk. She was the first Black woman hired as a GM in Machine Learning, AI and Analytics at AWS, and uses that platform to address the harder question behind most AI roadmaps, which is whether the organisation has the talent and the leadership bench to deliver them. She has been featured in Forbes, has keynoted and emceed at SXSW and the Grace Hopper Celebration, and sits on boards including Rewriting the Code. Key speaking topics Generative AI in the enterprise Personalisation as a revenue and engagement discipline AI agents and agentic workflows Machine learning in regulated industries Cloud migration and digital transformation Responsible AI and bias mitigation Building diverse technical leadership in AI and engineering Ideal for CTOs, CIOs and Chief Data and AI Officers moving from AI pilots into production CMOs and Chief Customer Officers investing in personalisation, recommendation and customer engagement Boards and executive committees in financial services, retail and regulated industries setting AI strategy CHROs and DEI leads responsible for the technical talent pipeline behind AI Audience outcomes A clearer read on which generative AI use cases pay back and which do not, drawn from live AWS customer patterns. A working view of personalisation as an operating capability that touches data, product and engineering, not a marketing campaign. Sharper questions for the board on AI risk, privacy and the operating model around generative AI. A concrete sense of what the technical leadership bench behind a serious AI roadmap looks like, and where most organisations are short.

Why organisations work with Lorraine Hariton Six years leading Catalyst, the global nonprofit whose research and benchmarks Fortune 500 boards already use to evaluate their own progress on women’s advancement. Operating background that buyers in male-dominated executive teams take seriously: 15 years at IBM, CEO of two Silicon Valley start-ups, Obama-appointed State Department official. Specific authority on the measurement question. Catalyst’s frameworks on emotional tax, empathic leadership, and inclusion assessment were developed and scaled during her tenure. Credible on the intersection of bias and emerging technology, including how gender and racial bias enter AI systems through data and design choices. Biography highlights President and CEO of Catalyst from 2018 to 2024. Special Representative for Commercial and Business Affairs at the US State Department under President Obama. CEO of two venture-backed Silicon Valley companies, Beatnik and Apptera. Senior Vice President of Global Partnerships, New York Academy of Sciences; co-founder of the Global STEM Alliance and the 1000 Girls, 1000 Futures programme. Inaugural Women’s Leadership Champion Award, University of Virginia Darden School of Business, 2022. Crain’s New York Business Women of Influence, 2023. BS Stanford University, MBA Harvard Business School. Biography Catalyst is the research organisation whose data Fortune 500 boards quote back to themselves when they discuss the women-in-leadership gap. For six years that organisation was run by Lorraine Hariton, an operator with credentials those same boards understand: fifteen years at IBM, two CEO roles at venture-backed Silicon Valley companies, an Obama-administration appointment representing US commercial interests at the State Department. That mix matters because the audience for serious DEI work has shifted. Senior teams now want practitioners who can speak the language of operating performance and capital discipline, not advocacy. At Catalyst, Hariton oversaw the development of frameworks used inside member companies on emotional tax, empathic leadership, and inclusion measurement, the kind of artefacts that survive contact with a sceptical executive committee. Her work addresses the credibility gap between public DEI commitments and observable workplace outcomes. She speaks directly to the failure mode senior leaders fear most, performative policy that produces no change and erodes employee trust. She also works on how gender and racial bias enter AI systems through training data, an issue most executive teams are still under-prepared for. The University of Virginia Darden School of Business presented her with its inaugural Women’s Leadership Champion Award in 2022. Crain’s named her to its Women of Influence list the following year. Her holistic view of where women’s advancement sits within an organisation, alongside compensation strategy, capital allocation, and technology governance, is what makes her useful in a boardroom rather than at a conference panel. Key speaking topics Gender equity and women’s advancement in senior leadership Inclusion measurement and DEI accountability Bias in artificial intelligence and emerging technology Workplace culture transformation Future of work and flexible workplace design Empathic and inclusive leadership practice Ideal for CHROs, Chief Diversity Officers, and CEO offices commissioning a measurable advancement strategy for women in senior leadership Boards and nominating committees evaluating their own pipeline credibility Senior leadership teams in male-dominated industries (financial services, technology, energy, industrials) working on inclusion outcomes that hold up to scrutiny Companies building governance around bias and fairness in AI deployment Audience outcomes A clearer picture of what credible DEI measurement looks like, drawn from Catalyst’s published frameworks A sharper grasp of where bias enters AI systems and what executive teams should be asking about it Direct exposure to a practitioner who has worked with the leadership teams of many of the world’s largest employers on this agenda A more confident vocabulary for senior leaders, particularly men in executive roles, to engage with women’s advancement as a business strategy issue rather than a compliance one

Why organisations work with Andrew Trask He has built the working infrastructure for privacy-preserving AI, not just written about it. PySyft, the open-source framework he created through OpenMined, is used by researchers and engineers to train models on data they never directly see. He sits inside one of the most consequential AI research labs in the world, DeepMind, while also leading an 18,000-strong community of privacy-preserving AI engineers. Few speakers operate credibly in both rooms. His agenda is structured transparency: making it possible for institutions to collaborate on AI without surrendering the underlying data. That is the exact tension a regulated industry leader is now trying to solve. He teaches the material rather than narrates it. Grokking Deep Learning and his Udacity courses have introduced tens of thousands of practitioners to neural networks and to private AI, which is why technical audiences find him credible rather than performative. Biography highlights Senior Research Scientist, Google DeepMind, working on privacy, federated learning, differential privacy, and secure computation. Founder of OpenMined, an open-source community of more than 18,000 researchers and engineers building privacy-preserving AI tools. Creator of PySyft, the open-source framework for training machine learning models on data the model owner cannot see. PhD candidate, University of Oxford, on the Oxford-DeepMind Graduate Scholarship; affiliated with the Future of Humanity Institute and the Centre for Governance of AI. Author of Grokking Deep Learning, Manning Publications, and instructor on Udacity’s Secure and Private AI and Deep Learning Nanodegree courses. Term Member, Council on Foreign Relations. Biography The hardest problem in applied AI is not modelling. It is access. The data that would make a healthcare model genuinely useful sits inside hospital systems that cannot legally share it. The data that would make a financial crime model work is split across banks that compete with each other. The default response has been to do less. Trask has spent the last decade building the technical answer to that problem. Through OpenMined, the open-source community he founded, he created PySyft, a framework that lets engineers train models on data they never directly see. The underlying methods, federated learning, differential privacy, secure multi-party computation, homomorphic encryption, are what serious AI governance now relies on. He calls the broader agenda structured transparency, and it is becoming the operating vocabulary for AI deployment in regulated settings. The institutional positioning matters. He is a Senior Research Scientist at Google DeepMind and a PhD candidate at Oxford, funded by the Oxford-DeepMind Graduate Scholarship and affiliated with the Future of Humanity Institute and the Centre for Governance of AI. He is also a Term Member of the Council on Foreign Relations. That combination puts him inside the frontier AI conversation and inside the AI governance conversation at the same time, which is unusual. The teaching record is the test of whether his ideas travel. Grokking Deep Learning, published by Manning, taught a generation of engineers how neural networks actually work. His Udacity courses on secure and private AI did the same for the privacy-preserving stack. The reason senior technical audiences treat him as a primary source rather than a commentator is that they have already worked through his material. Key speaking topics Privacy-preserving artificial intelligence Federated learning and secure computation AI governance and structured transparency Differential privacy in practice AI safety and responsible deployment The future of data infrastructure for AI Foundations of deep learning for non-specialists Ideal for Chief AI officers, CTOs, and chief data officers in regulated industries (healthcare, financial services, government) deciding how to deploy AI on sensitive data. Boards and audit committees pressure-tested on AI governance, data risk, and the gap between AI ambition and AI infrastructure. Heads of research, innovation, and product engineering teams building the next generation of AI products. Policy and public-sector leaders responsible for AI standards, data sharing, and international AI governance. Audience outcomes A working mental model of the privacy-preserving AI stack, named technique by named technique, instead of the usual abstractions. A clearer view of which AI use cases are unlocked by federated learning and secure computation, and which remain blocked by data access. A grounded read on where AI governance is heading, from someone inside both DeepMind and the Oxford AI governance ecosystem. A direct sense of how to move from AI pilots to deployments that survive regulatory and reputational scrutiny.

Why organisations work with Glen Weyl He is the working bridge between three of the people who matter most in this debate: Vitalik Buterin on Ethereum and Web3, Jaron Lanier on AI and data, and Audrey Tang on civic technology. Senior teams get one room with the network, not three separate conversations. He co-invented Quadratic Voting and Quadratic Funding, mechanisms now used inside crypto ecosystems, civic budgets, and corporate innovation funds. Boards looking at participatory capital allocation are looking at his work, often without knowing it. He led Microsoft’s Office of the CTO work on the OpenAI relationship and now runs Microsoft Research’s Plural Technology Collaboratory. He has seen the inside of how the largest AI partnership in the world is governed, and can talk about it candidly. His book Radical Markets, written with University of Chicago law professor Eric Posner, was named an Economist Book of the Year in 2018 and changed how serious economists discuss property, voting, and antitrust in a digital economy. He works in the same language as policymakers in Taiwan, the EU, and the US Treasury, which makes him unusually useful to organisations whose AI exposure is also a regulatory exposure. Biography highlights Founder and Research Lead, Plural Technology Collaboratory, Microsoft Research. Former Political Economist and Social Technologist, Microsoft Office of the CTO, including the leadership team for the OpenAI relationship. Co-author, Radical Markets, Princeton University Press, 2018; an Economist Book of the Year. Co-author with Audrey Tang of Plurality: The Future of Collaborative Technology and Democracy, 2024. Founder, RadicalxChange Foundation; co-founder and chair, Plurality Institute; co-founder, Harvard GETTING-Plurality Research Network. Named by CoinDesk, WIRED, and Bloomberg Businessweek on their respective lists of most influential people in technology, 2018. Biography Most institutions designed for the industrial economy do not have a good answer to generative AI. Voting systems, ownership rules, antitrust law, and corporate governance were all written for a world where the most powerful entities in a room were human. Glen Weyl’s career has been a sustained attempt to redesign those institutions for a world where they are not. His 2018 book with Eric Posner, Radical Markets, was an Economist Book of the Year and forced a new conversation about property, voting, and antitrust inside serious policy circles. Out of that work came Quadratic Voting and, with Vitalik Buterin, Quadratic Funding. Both are now in operational use in civic budgets and Web3 ecosystems, including projects funded by Ethereum. At Microsoft, he sat inside the Office of the CTO during the period when the company’s relationship with OpenAI became one of the most consequential commercial arrangements in technology. He now runs the Plural Technology Collaboratory, Microsoft Research’s special project on how democratic societies function under generative AI, working with Taiwan’s Audrey Tang, with Harvard, and with civil society groups including the Collective Intelligence Project. His 2024 book with Tang, Plurality, was written openly on GitHub and translated by volunteers into more than a dozen languages, an unusual proof point that the governance ideas in it actually work in practice. For a board trying to decide what AI legitimacy means inside its own organisation, that record makes him one of the few people in the world worth bringing into the room. Key speaking topics Generative AI and democratic institutions Plurality and collaborative technology Quadratic Voting and Quadratic Funding Web3, decentralised identity, and digital ownership Antitrust and platform competition Data as labour and the economics of AI training Ideal for Boards and CEOs setting AI governance and partnership strategy Government, regulator, and central bank audiences on AI and digital policy CTOs and Chief AI Officers working on legitimacy and deployment frameworks Foundations, philanthropic capital allocators, and corporate venture funds rethinking how they fund collective goods Audience outcomes A clearer view of how AI partnerships at the scale of Microsoft and OpenAI are actually governed. A working vocabulary for Quadratic Voting, Quadratic Funding, and Plurality, with concrete examples of where each is operating. A sharper sense of where current antitrust, data, and AI regulation is heading in the US, EU, and Asia. Specific design ideas for participatory decision-making inside large organisations, not generic engagement theory.

Why organisations work with Mo Gawdat His central AI thesis – that these systems reflect the values humans encode into them, and that organisations still have a window to act – was published in 2021 and validated by the subsequent speed of AI deployment. Boards are not getting speculation; they are getting a forecast with a track record. Scary Smart gives senior leadership a specific accountability framework, not a risk briefing: the argument is that the question is not whether AI will be powerful, but what the organisation is actively doing to shape its values now. The Solve for Happy happiness model is a named, reproducible framework developed through the same engineering logic Gawdat applied throughout his technology career – not a wellbeing programme, but a systematic methodology for building psychological stability under pressure. He addresses the external disruption (AI’s structural impact on work and value) and the internal response (the human psychology of navigating it) as a single argument – which is why his sessions land differently in a strategy forum than a conventional AI speaker or a conventional wellbeing speaker would. Seven years inside Google [X] at the point when self-driving cars, Project Loon, and Google Life Sciences were being built and commercialised gives him a credibility baseline that no amount of research or commentary can replicate. Biography highlights Former Chief Business Officer, Google [X] (2013-2018), overseeing commercial strategy for Google’s moonshot innovation division 30-year career in technology spanning IBM, NCR, Microsoft, and Google, including senior roles across emerging markets globally Author of Solve for Happy (Macmillan, 2017), Scary Smart (Macmillan, 2021), That Little Voice in Your Head (Macmillan, 2022), and Unstressable (2024, co-authored with psychologist Alice Law) Scary Smart named a Sunday Times Business Book of the Year (2021) Chief AI Officer at Flight Story, the digital marketing company co-founded by Steven Bartlett Founder of One Billion Happy, a global initiative to share structured approaches to happiness and wellbeing Host of Slo Mo, one of the leading mental health podcasts globally Biography When Scary Smart was published in 2021, its central argument read as speculative: AI would become superintelligent, its real danger lay not in the technology itself but in the values humans encode into it, and organisations still had a narrow window to shape the outcome. Within a year, the release of ChatGPT had reframed the argument as a warning that had arrived early. Mo Gawdat had been inside Google [X] when these systems were being built. Gawdat joined Google in 2007 and served as Chief Business Officer of Google [X] from 2013, overseeing commercial strategy for the moonshot division behind self-driving cars, Project Loon, and Google Life Sciences. His seven years there gave him direct exposure to both the capability and the accountability gaps at the frontier of AI development. He left in 2018 and now serves as Chief AI Officer at Flight Story, the digital marketing company co-founded by Steven Bartlett. The parallel body of work on happiness was not a departure from his technology career. It emerged from the same engineering mindset applied to personal crisis. Following the death of his son Ali in 2014, Gawdat developed the framework at the centre of Solve for Happy: a systematic, reproducible model for well-being grounded in cognitive science. The book became an international bestseller. His thesis is that the two bodies of work are not separate. An organisation that understands the human psychology of its people is better positioned to navigate the disruption AI will accelerate. Unstressable, co-authored with psychologist Alice Law and published in 2024, extends that logic into a practical stress-management framework built around four human systems. He hosts Slo Mo, one of the leading mental health podcasts globally, and his earlier books – That Little Voice in Your Head (2022) and Scary Smart, a Sunday Times Business Book of the Year – continue to reach executive audiences grappling with AI governance and personal resilience across a 30-year technology career. Key speaking topics AI ethics and governance Responsible AI adoption The human impact of AI disruption Happiness science and the engineering of well-being Stress management frameworks Technology leadership and strategic foresight Human values in AI development Ideal for Boards and executive leadership teams making AI governance and adoption decisions Chief People Officers and Chief Technology Officers managing workforce responses to AI disruption Strategy, transformation, and future of work forums where AI risk and human resilience intersect Technology, financial services, and marketing organisations with significant and growing AI exposure Audience outcomes A coherent framework for evaluating AI’s ethical and human risks, grounded in direct experience of building these systems rather than external commentary Clarity on what values organisations need to embed in AI systems now, and why the window to do so meaningfully is narrowing Practical tools from the Solve for Happy model for building psychological stability and resilience under conditions of sustained uncertainty A more calibrated understanding of the timeline and direction of AI development, distinct from both catastrophism and uncritical optimism Practical techniques from Unstressable for identifying and reducing sources of stress at individual and team level

Why organisations work with Ramy Nassar He brings a published, structured method for AI product design from idea to launch, set out in the AI Product Design Handbook and used by enterprise teams who need a repeatable process rather than another inspiration deck. He has run innovation inside Mattel and led design and strategy at Architech for Apple, Air Canada, CIBC, Facebook and Rogers, so the operational reality of shipping new products inside large organisations is in the room. His work separates “AI Now, Next and Never,” giving boards a defensible position on what to deploy, what to wait on, and where to draw governance lines. He teaches design thinking and innovation at McMaster University and Toronto Metropolitan University, and executive programmes at Schulich ExecEd, which keeps the frameworks tested against working leaders. Client engagements span Apple, TD Bank, TELUS, Verizon, New Balance and the Government of Canada, covering both regulated and consumer environments. Biography highlights Author, AI Product Design Handbook, and the forthcoming The World After AI (early 2027) Former Head of Innovation, Mattel, where he launched the company’s first innovation lab Former Managing Director, Design and Strategy, Architech Founder, 1000 Days Out, advisory practice on AI and emerging technology Faculty: McMaster University, Toronto Metropolitan University, the Norwegian University of Science and Technology, Schulich ExecEd at York University Featured by CPA Canada at The ONE national conference; speaker at FITC, IxDA, FWD50 and World Usability Congress Biography Most AI projects inside large organisations have the same shape. A pilot ships, attention moves on, and the operating model does not change. Boards then have to decide whether the next round of spend should go into more experiments or into the harder work of getting AI into the product and into the workflow. That is the decision Ramy Nassar is built to inform. The AI Product Design Handbook sets out a repeatable process for taking AI products from idea to launch, with explicit attention to user trust, transparency and governance. It is used by product and design leaders who need a method, not a manifesto. The argument is grounded in his time as Head of Innovation at Mattel, where he launched the company’s first innovation lab and led emerging-technology work for Barbie and Hot Wheels, and earlier as Managing Director of Design and Strategy at Architech, where he ran client work for Apple, Air Canada, CIBC, Facebook and Rogers. Through 1000 Days Out he advises senior teams at Apple, TD Bank, TELUS, Verizon, New Balance and the Government of Canada on AI adoption, strategic foresight, and product strategy. His forthcoming book, The World After AI, extends that foresight work, with the argument that the decisive variable in any AI future is the set of choices leaders make, not the technology itself. His framing of “AI Now, Next and Never” is built for boards that need to commit capital and set governance lines at the same time. Faculty roles at McMaster University, Toronto Metropolitan University, the Norwegian University of Science and Technology, and Schulich ExecEd at York University keep the frameworks tested against working executives. CPA Canada featured him at The ONE national conference on turning insights into measurable impact, and his international speaking includes FITC, IxDA, FWD50, and the World Usability Congress. Key speaking topics Artificial intelligence adoption and product design Responsible AI and governance Strategic foresight and scenario planning Innovation strategy Emerging technology and exponential change Digital product and customer experience design Ideal for Boards and C-suite teams setting AI investment and governance policy Chief Product Officers, Chief Design Officers and Chief Innovation Officers moving from pilots to deployed AI Heads of Strategy and corporate development running scenario planning under technology uncertainty Public-sector leaders shaping responsible AI policy and procurement Audience outcomes A defensible “now, next, never” view of where to deploy AI, where to wait, and where to refuse A structured method for taking AI features from concept to launch with governance built in Sharper foresight tools for testing strategy against multiple long-range scenarios Concrete language for communicating AI risk, ethics and product trade-offs to non-technical executives and customers

Why organisations work with Maria Ressa She has tested platform governance arguments against actual prosecution, exile threats and a coordinated state-backed disinformation campaign, which gives her a clarity on digital risk that academic experts rarely match. As co-founder of the Real Facebook Oversight Board and co-leader of Columbia’s Technology and Democracy Initiative, she sits at the centre of the policy conversation shaping how governments will regulate platforms and AI. Her Nobel citation specifically named the defence of free expression as a precondition for democracy, giving leadership and ESG audiences a credible voice on information integrity as a governance issue, not a communications one. She runs a working newsroom that has documented platform manipulation in real time, so her arguments come with primary evidence rather than secondary commentary. How to Stand Up to a Dictator gives her a published thesis on the architecture of digital authoritarianism that boards and policy teams can engage with in advance of a session. Biography highlights 2021 Nobel Peace Prize laureate, jointly with Dmitry Muratov, for work safeguarding freedom of expression. Co-founder and CEO of Rappler, the Manila-based investigative news organisation she launched in 2012. Professor of Professional Practice, Columbia University School of International and Public Affairs; Distinguished Fellow and co-leader of the Technology and Democracy Initiative at SIPA’s Institute of Global Politics. Author of How to Stand Up to a Dictator (Harper, 2022), a New York Times bestseller, and Seeds of Terror (2003). 2021 UNESCO Guillermo Cano World Press Freedom Prize; 2018 Golden Pen of Freedom from the World Association of News Publishers. Time Person of the Year 2018; Time 100 Most Influential People; BBC 100 Women. Nearly two decades as CNN bureau chief in Manila and Jakarta; former SVP for News and Current Affairs at ABS-CBN. Biography The 2021 Nobel Peace Prize citation named freedom of expression as a precondition for democracy. It was the first time since 1935 that the prize had been given to working journalists, and the choice was not symbolic. Ressa shared it with Dmitry Muratov for a body of work that treats disinformation, platform power and the erosion of shared reality as the defining governance problem of the decade. Her vantage point is unusual. She co-founded Rappler in Manila in 2012, then ran it through years of state-backed harassment, criminal charges and an online campaign that the company itself documented in forensic detail. That experience produced a working theory of how engagement-driven platforms, weaponised by political actors, dismantle public trust and institutional legitimacy from below. The argument is laid out in How to Stand Up to a Dictator, the Harper book that became a New York Times bestseller and Princeton’s Class of 2027 Pre-read. She now sits at Columbia’s School of International and Public Affairs as a Professor of Professional Practice and co-leads the Technology and Democracy Initiative at the Institute of Global Politics. The role pairs naturally with her work on the Real Facebook Oversight Board, an independent body of journalists, academics and activists holding Meta to account on content governance. Earlier in her career she opened CNN’s Manila bureau, ran its Jakarta bureau through the post-9/11 years, and led news at ABS-CBN; her 2003 book Seeds of Terror remains a reference on Jemaah Islamiyah. For corporate audiences, the value sits in the bridge she builds between platform mechanics and executive decision-making. AI accelerates everything she has been documenting for a decade, and the regulatory response, in Brussels, Washington and Manila, is now arriving in the operating environment of any business with a public-facing reputation. Key speaking topics Disinformation and the integrity of the information ecosystem Platform governance and Big Tech accountability AI ethics and the future of public trust Press freedom as institutional infrastructure Democratic resilience under digital authoritarianism Leadership and moral courage in contested environments Ideal for Boards and C-suite teams reassessing reputational and regulatory risk in an AI-mediated information environment Chief Communications Officers, Chief Risk Officers and General Counsel responsible for platform exposure and disinformation response Public-sector and policy audiences working on technology regulation, democratic resilience and ESG frameworks Leadership programmes seeking a serious voice on values, courage and institutional integrity Audience outcomes A working model of how engagement-driven platforms shape public perception of organisations, leaders and policy. Direct evidence, drawn from Rappler’s own reporting, of how coordinated disinformation campaigns are designed and executed. A clear read on where AI regulation and platform accountability are heading across major jurisdictions. A sharper sense of what leadership looks like when institutional norms are under pressure, told through a Nobel-recognised case. Language and frameworks for treating information integrity as a governance issue at board level, not a communications problem.

Why organisations work with Joe Toscano He left Google over the data practices he saw inside it, then wrote the book on what those practices look like from within. That insider account is hard to replicate at the keynote level. His work translates AI and privacy debates into specific operating decisions: what data to keep, what to delete, what to disclose, and where consent breaks under regulatory scrutiny. As founder of DataGrade and BEACON, he speaks from the position of someone running a privacy-first business, not commenting on one from outside. His public visibility through The Social Dilemma, Fox News, and Forbes means he can hold a non-technical audience on a technical subject without dropping the substance. Biography highlights Featured expert in Netflix’s The Social Dilemma. Author of Automating Humanity (powerHouse Books / Simon & Schuster). Founder and CEO of DataGrade. Founder of the Better Ethics and Consumer Outcomes Network (BEACON). Former Experience Design consultant for Google. TEDx speaker; Forbes contributor; recurring Fox News technology commentator. Biography The companies setting the rules for how consumer data trains AI systems are also the companies whose products most depend on that data. That conflict sits at the centre of Joe Toscano’s work. He came out of Silicon Valley with a designer’s view of how those products are built, then turned that knowledge outward. Toscano was an Experience Design consultant for Google before stepping away in 2017 over what he saw of the industry’s data practices. Automating Humanity, his book with powerHouse Books, set out the argument in detail: how attention is engineered, how disinformation pays, and how automation is being trained on data the public never knowingly handed over. He went on to found DataGrade, a privacy-first data company, and BEACON, a network focused on technology literacy and ethical innovation. The Netflix documentary The Social Dilemma brought that work to a mass audience, and he has since become a regular technology commentator on Fox News and a contributor to Forbes. What makes him useful to boards is the combination of inside knowledge and operating responsibility. He is not theorising about AI governance from a lectern. He is building a company that has to make those decisions every day, and translating that into language a non-technical executive team can act on. Key speaking topics AI ethics and governance Data privacy and consumer trust The business case for privacy-first design Technology literacy for non-technical leaders The future of work under AI and automation Disinformation and platform accountability Regulation and the post-GDPR landscape Ideal for Boards and executive committees setting AI and data governance standards Chief Privacy Officers, CISOs, and Chief Data Officers CMOs and customer experience leaders weighing personalisation against privacy HR and transformation leads planning workforce change driven by AI Audience outcomes A clearer line between defensible and indefensible uses of customer data in AI systems Specific questions a board should be asking about model training data and consent A working view of how privacy can be a commercial advantage, not just a compliance cost Sharper judgement about what to believe and discount in vendor pitches on AI capability