Deflection Is The Ugly Word Quietly Wrecking Your Customer Service

Deflection Is The Ugly Word Quietly Wrecking Your Customer Service

Done well, AI in customer service needs more people, not fewer. That runs against the entire case being made for it. Matt Price has built a company on exactly this claim, and he brings the evidence. Matt Price, Founder and Chief Executive of Crescendo, argues that the industry has spent twenty years optimising for the wrong thing. He names deflection, the practice of stopping customers reaching a human, as the metric quietly wrecking service and returns. The vendor paid only for AI resolutions has every reason to keep you inside a bot, and Crescendo instead charges only when the whole journey ends in a satisfied customer. He makes the case that moving care from the back door of a business to the front produces around three times the engagement, and that over half of those extra inquiries come from people who want to buy. He is blunt about the point at which these programmes stall, and it is not the technology. ● Deflection as a twenty-year habit that punishes the customer and misleads the board ● The incentive trap: vendors paid per AI resolution keep you inside the bot ● Three to five points of labour retired for every ten points of automation, on a curve that is not linear ● Lovepop's Trustpilot rating moving from 3.6 to 4.6 in a couple of weeks ● “Where transformation or innovation goes to die is in middle management” Anyone who has been stuck in a loop begging a bot for a human will recognise the problem. This episode sets out the economics behind it, and the change that fixing the incentives produces. Chapters 00:00 AI's Impact on Customer Engagement 03:42 The Evolution of AI in Customer Service 06:11 Deflection vs. Engagement in Customer Service 08:40 Aligning Incentives for Successful AI Implementation 11:21 The Labour Dynamics of AI in Customer Service 13:11 The Bolt-On Bot Phenomenon 14:44 Continuous Improvement in AI Deployments 16:59 Real-World Examples of AI Success 21:44 Transformation Practices for AI Adoption 25:15 Key Strategies for AI Implementation in Organisations

AI With Humans in the Loop Gets Leadership Exactly Backwards

Every technologist repeats the same phrase: AI with humans in the loop. André Lacroix inverts it. The future, he argues, is purpose-based, people-centric leadership with AI in the loop, and the difference is not semantic. André Lacroix, Chief Executive of Intertek Group plc, makes the case that emotional intelligence, not technology, will decide which organisations win the AI decade. His reasoning starts from an uncomfortable number. Gallup's latest survey shows 80% of the global workforce is not engaged or not actively engaged, which André frames as three billion people arriving at work without purpose. He argues AI's real value is buying leaders back the time to fix that, by automating the analytical work that fills their diaries. He predicts every algorithm and agentic solution will eventually become a commodity, which leaves emotional intelligence as the only durable advantage. And he refuses, flatly, the idea of a CEO avatar answering employees on a leader's behalf. In this conversation: • The Gallup figure behind André's claim that the corporate world is "over managed and underled" • Why he processes three million data points in his role, and calls the goal "data science in real time at scale" • His four foundations for responsible AI: governance, transparency, security, and functional performance • His warning that uploading data into a third-party large language model means the data leaves your company • His argument that instinct is "the result of years of data processing in our brain," and should be trusted, not overridden André also shares breaking news: a sequel to his book Leadership with Soul, built around the idea he calls EQ supremacy in a world of AI. Press play for a leadership argument that puts AI firmly in second place. Chapters 00:00 Trusting Instincts and Embracing Change 03:03 Leadership in the Age of AI 10:24 The Role of AI in Decision Making 17:20 Emotional Intelligence vs. AI 22:38 The Future of Work and AI 26:04 Skills for Reinvention in an AI World 27:38 Trust and Transparency in AI Implementation

AI Is Underachieving Benefits and Organisations Keep Spending Anyway

Large organisations are achieving limited benefits from AI projects. Yet, their appetite to invest more is undiminished. Rob Lunn, Product Manager at Fnality, a regulated wholesale payment system that settles transactions on-chain, argues that this paradox is not just a general business problem. In financial services, where unit cost discipline and regulatory compliance define survival, it is a structural risk that leadership teams have not yet priced in. Rob makes the case that AI and blockchain are not competing technologies; they serve fundamentally different parts of the payment chain, and confusing the two is one of the more expensive mistakes a payments organisation can make. The conversation maps the payment process from instruction receipt through to settlement and reconciliation, and locates precisely where each technology delivers value. Blockchain, through atomic settlement, removes counterparty risk at the point where absolute certainty is required: payment occurs if and only if the corresponding asset transfer happens. AI delivers its greatest value at the start and end of that chain, reading unstructured invoice / payment instruction data, extracting meaning from vendor records, and supporting liquidity forecasting and reconciliation. Where existing automation already achieves very high straight-through processing rates, agentic AI faces two barriers Rob identifies as equally significant: the legal framework for autonomous transaction signing, and enterprise and consumer trust. Highlights from the conversation: • The paradox Rob names: large organisations report a low success rate with AI projects today, yet investment appetite, based on industry surveys and events including London Tech Week, is off the scale. • Atomic settlement explained: blockchain links payment and asset transfer so that one cannot occur without the other, removing the settlement risk that became systemic during the Lehman Brothers collapse. • Settlement risk is one of the factors determining how much capital banks must hold under global regulations, making its reduction a direct financial benefit, not just an operational one. • Rob's leadership framework: be highly selective in AI use cases; focus on a small number of well-defined projects; assess total cost of ownership against customer outcome, regulatory obligation, and long-term capability building. • The talent pipeline argument: eliminating junior roles before organisations build the technical and domain knowledge those roles develop leaves the future management layer without the skills to run AI-dependent operations safely. For leaders in financial services asking where AI and blockchain actually earn their place, and what the cost of getting that wrong looks like, this conversation provides an unusually precise answer. Chapters 00:00 Fnality and Blockchain in Payments 03:23 Blockchain's Role in Mitigating Settlement Risk 08:31 Synergies Between AI and Blockchain Technologies 11:07 Limitations of AI in Payment Processes 14:52 Regulatory Challenges in Financial Technologies 17:23 Impact of AI and Blockchain on Leadership in Payments 21:12 Long-term Challenges in the Payments Industry 23:21 Preparing for AI and Blockchain Integration

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