Intelligence Is the Third Utility. Taiwan Proves It.

Intelligence Is the Third Utility. Taiwan Proves It.

Taiwan makes 95% of the world's AI servers. The rest of the world runs on what Taiwan builds. Sega Cheng, Co-Founder and Chairman of iKala, argues that Taiwan's hardware leadership is the clearest illustration of how foundational AI infrastructure has become: one country manufactures, assembles, and supplies the chips and servers that power AI for virtually the entire world. From that vantage point, he makes the case that intelligence is becoming the third utility of human civilisation, after water and power. Just as no enterprise or state would accept indefinite dependency on another for electricity, he argues that home-grown AI capability, at the level of data, models, and infrastructure, is no longer a strategic preference. It is a necessity. That argument is the starting point for a conversation that moves well beyond Taiwan. The tension Sega identifies is sharp: the country building the infrastructure for global AI has yet to fully use it itself, with more than 70% of Taiwanese businesses still to genuinely integrate AI into their operations. Organisations everywhere are making the same structural mistake: layering AI on top of legacy systems and legacy mindsets, then wondering why the returns do not materialise. His answer is not transformation at scale. It is something more disciplined: start where the results are visible. In iKala's experience, that means marketing, where a two-point improvement in targeting or a 20% lift in social media performance gives an organisation concrete evidence of AI's value before committing to deeper and more disruptive change. The conversation also covers an argument Sega makes with considerable force: what he calls 'consider software soft.' The cost of producing software is approaching zero as AI coding tools advance, which shifts value away from code itself and towards sector-specific application. The organisations that will benefit are those with deep domain knowledge in fields such as agriculture, medicine, and manufacturing, ready to apply rapidly produced software to problems they already understand better than anyone else. Highlights from this conversation: • Taiwan assembles over 95% of global AI servers, making it the backbone of the world's AI infrastructure • 80% of AI adoption effort goes into data collection and cleansing; only 20% touches algorithms or machine learning • China's AI adoption is state-led and follows a different pattern from the 30/70 split visible in the rest of Asia • Edge AI is moving from concept to deployment in manufacturing, warehousing, and defence • iKala's Kolr platform tracks data on over 300 million influencers, providing a concrete case study in AI-layered transformation Chapters 00:00 AI as the Third Utility 02:32 iKala and AI Adoption 05:10 AI Adoption in Taiwan 08:21 Industry-Specific AI Adoption 09:32 Regional AI Adoption Trends 12:14 Challenges in AI Integration 16:08 Measuring AI ROI 21:16 The Future of Software in AI 24:15 Understanding Edge AI 29:39 Taiwan's Semiconductor Advantage 33:28 Sovereignty in AI and Chip Manufacturing

Bank of America's AI Results Are In. The Human Override Stays

AI tools at Bank of America are already delivering around 20% productivity improvements in coding. That figure is live, not projected and from the executive responsible for technology and operations in the bank's French entity. He argues it will increase as the technology matures. Yet his most insistent point is this: at every consequential decision point, a human must remain in the loop. The productivity gain and the human override are not in tension; they are the same strategy. Andy Price, Head of Technology and Operations at Bank of America's French legal entity, makes the case that the difference between organisations that merely adopt AI and those that lead with it comes down to culture, governance, and disciplined application of standard business case logic, not the sophistication of the tools themselves. The conversation covers Bank of America's seven-year AI journey, from the launch of Erica in 2018 through to its current portfolio of internal and client-facing tools. Andy explains the bank's prioritisation framework: high-volume, repetitive, manual processes come first, assessed against the same return-on-investment criteria as any other technology investment. He argues that AI deployed without stringent governance will go off track, and that human oversight is non-negotiable, not as a regulatory concession, but as an operational principle. In an environment where AI is accelerating into research, drafting, code generation, and client interaction, that principle has direct implications for how far AI autonomy can extend into consequential execution. Highlights from the conversation: • Bank of America's AI-assisted coding tools are producing productivity gains of around 20% already, with Andy Price expecting that figure to rise as the technology develops. • Erica for Employees has measurably reduced technical support calls and response times across the organisation, with automatic escalation to a human when the AI cannot resolve an issue. • AskGPS searches thousands of internal documents in real time, giving employees faster, more accurate answers to client queries without leaving the workflow. • Andy's boardroom advice centres on business case discipline: do not automate a process that saves 30 minutes a year and costs significant investment to build. Focus on the repeatable, the high-volume, the genuinely time-consuming. • On the limits of AI autonomy: "There must be a human being who has to look at it, review it, press the button." For any organisation asking where AI is actually delivering, and how to build the governance structures that make that delivery sustainable, this conversation provides concrete answers from one of the world's largest financial institutions. Chapters 00:00 Embedding AI into Culture and Operations 02:10 The Importance of AI Investment in Banking 05:06 AI Outcomes for Clients and Employees 07:43 Leading vs. Simply Adopting AI 09:00 Prioritising AI Investments 10:05 Deployment Strategies for AI Technology 11:10 Reshaping Employee Experience with AI 13:13 Productivity Improvements through AI 14:39 Talent Development and Upskilling 16:09 Responsible and Ethical AI Practices 18:21 The Future of AI in Banking 20:38 Advice for Companies Starting Their AI Journey

The Future With AI

Hallucination in AI is not always a failure to be fixed. For some organisations it is a design choice. That observation, made on stage at Brunel University of London, is one of nine tensions this conversation surfaces, examines, and poses as challenges to be resolved as we live and work with AI. This is the Season 3 opening episode of The AI Adoption Podcast, recorded live in front of an audience at Brunel's Research Festival 2026. Five panellists joined me to discuss The Future With AI: Zahra Bahrololoumi CBE, President and CEO of Salesforce UK and Ireland; Lord Tim Clement-Jones, Liberal Democrat spokesperson for Science, Innovation and Technology and co-chair of the APPG on AI; Janusz Marecki, CEO and co-founder of FractalBrain; Maggie Sarfo, founder of Meres Consulting; and Alex Dalman, Managing Partner at Faith. The theme is the future WITH AI. Not the future of AI. The emphasis matters. The panel assumes AI is already ubiquitous and asks what life looks like inside that reality, and where the tensions lie that policymakers, business leaders, and academics need to face up to. Five tensions to listen for: • Salesforce's agentic AI now resolves between 82 and 84 per cent of complex customer queries on its help platform without human intervention, saving $100 million a year with no engineers displaced. Alex Dalman observes that most organisations have no real understanding of the effort required to get there. • Janusz Marecki argues that current large language models are unreliable by design: the underlying architecture samples from a probability distribution rather than reasoning from verified knowledge, and the training data itself contains what he describes as inaccurate, harmful, and toxic content. Zahra Bahrololoumi argues that people are smarter than we give them credit for, and that enterprise AI built on curated, governed data is a categorically different proposition. • 61 per cent of people in business are already using AI without having received any formal training. The government has committed to training 10 million people by 2030. The gap between those two assertions is where a great deal of the risk currently lives. • Lord Tim Clement-Jones describes graduates encountering only automated assessments throughout the early stages of job applications, with no human contact and no feedback until they clear several initial hurdles. Maggie Sarfo argues that AI should be making us more human by releasing capacity for emotional intelligence. Both observations are accurate about different parts of the same reality. • AI, as Alex Dalman notes, is unlike every previous technology revolution in that it has no tangible form most people associate with it directly. The mobile phone was something you could hold. AI's experiential invisibility is both the source of public distrust and the condition that allows it to embed everywhere without friction. There are four more tensions in this conversation. Listen to find them. Chapters 00:00 The Future With AI: Opportunities and Risks 08:43 SMEs and AI Adoption 12:49 Creativity in the Era of AI 17:07 Societal Readiness for AI: Trust and Responsibility 30:59 Business and Societal Readiness: A Unified Approach 32:17 Assessing AI Maturity 36:17 The Rise of the Agentic Organisation 44:02 Accountability in AI Deployment 55:11 The Impact of AI on Jobs and Skills

Death by a Thousand AI Licences and other lessons from Season 2

Two seasons in, many lessons to learn. Season 3 information at the end of this post. Every organisation investing in AI is focused on the Tech Stack: infrastructure, platforms, applications. That stack matters. But there is a second stack that barely features in strategy discussions or boardroom agendas, and Professor Ashley Braganza believes it is where the real value from AI adoption is either created or squandered. The Organisation Stack has four layers: Strategy, Governance and Risk, Leadership and Transformation, and Culture and Behaviours. Each layer raises a distinct set of questions that most organisations have not yet answered seriously. In this end-of-season reflection, Ashley draws together the central lessons from Season 2. The conversations across twenty five episodes consistently pointed to the same gap: organisations are investing heavily in the technology stack and insufficiently in the organisation stack. It’s no wonder organisations are facing difficulty to extract value and ROI from AI investment. Distributing AI licences to an entire workforce is not a strategy. Layering AI on top of processes that are not fit for purpose does not fix those processes. And allowing employees to optimise their individual tasks in isolation generates what Ashley calls inefficiencies by a thousand AI licences, a pattern that sub-optimises end-to-end organisational performance even as individual productivity appears to rise. The episode closes with a direct challenge on the agentic organisation. Coordinating AI agents across departmental and silo boundaries is the next critical frontier. Organisations that can design, deploy, and operationalise agents in a genuinely coordinated way will gain a durable advantage. Those that cannot face the same coordination problem in AI that they have always faced in organisational transformation. Key themes: - The Organisation Stack: strategy, governance, leadership, transformation, culture - Death by a thousand AI licences - Agentic organisations and coordinated AI deployment - The limits of individual-level AI adoption - AI adoption maturity and what genuine strategic adoption looks like Chapters 00:00 Season 2 Reflection and Insights 03:06 The Importance of the AI Stack 06:03 Understanding the Organisational Stack 08:53 The Challenge of Agentic Organisations Season 3 launches 4 June, opening with an in-person and live-streamed panel featuring AI adoption alums from 12:00 to 13:00 UK time. Please join a live audience at Brunel’s Eastern Gateway Auditorium by registering here https://www.eventbrite.co.uk/e/brunel-research-showcase-tickets-1985263093354. The discussion will be live-streamed and the link is here https://vimeo.com/event/5944341. I hope you will join.

Your AI Use Cases and Proofs of Concept Are Not Evidence of Progress

Most banks now have impressive-looking AI. Very few have AI that genuinely transforms their customers experience. The gap between a polished interface and a transformed organisation is wider than most leaders want to admit, and it comes down to foundations that were never built properly in the first place. Moush Verma, Senior AI Lead at Santander UK, makes the case that the biggest mistake organisations are currently making is scaling AI use cases before they have scaled the underlying capability. In this conversation, Moush argues that value in banking is shifting from transaction efficiency to contextual guidance: reaching customers at the precise moment they need support, before they have to ask for it. She sets out the structural reasons why most legacy institutions struggle to achieve this, pointing to fragmented data ownership, siloed operating models, and an industry-wide habit of measuring AI maturity by volume of proof of concepts use cases. Moush argues that banks should measure the value of AI initiatives in terms of actual delivery and value to customers and the business. The conversation also covers the philosophical dimension of AI adoption that too many organisations skip entirely: not just whether AI can be deployed, but what it is genuinely for. Moush is clear that the winners in two to three years will be organisations that embed AI coherently across customer journeys, risk, operations, and service, treating it not as a set of separate initiatives but as the invisible intelligence running through everything. Highlights from this episode: • Volume of proof of concepts is not maturity. • The payment failure example: a concrete case of how AI can shift a bank from reactive to proactive customer support. • Life moments and everyday moments require different AI responses. A good AI engine knows the difference. • A polished AI interface overlaid on weak data foundations will never produce genuine transformation. • The philosophical question every organisation must answer before scaling: is AI there to industrialise processes, or to create clearer, more trusted guidance for customers? If your organisation is accumulating AI pilots without seeing sustained transformation, this episode gives you the framework to understand why, and what to do about it. This episode marks the 50th episode of The AI Adoption Podcast and brings Season 2 to a close. Thank you to every listener who has been part of this journey. Season 3 coming soon! Chapters 00:00 Transforming Legacy Processes with AI 02:14 AI's Role in Value Creation in Banking 05:40 Understanding Timing in Customer Interactions 07:16 Designing AI-Based Customer Interactions 08:56 Organisational Change for AI Transformation 10:09 Building Strong Data Foundations 11:44 Creating a Unified Purpose in Organisations 15:04 Hype vs. Reality in AI Adoption 17:23 Measuring AI Implementation Success 19:02 Scaling AI Capabilities in Organisations 21:41 Transformational Outcomes in Banking 23:59 Capturing Customer Moments 25:23 Future of AI in Banking