The Digital Transformation Playbook

Hosted by Kieran Gilmurray

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317

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Oct 2026

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About the show

Kieran Gilmurray is an Internationally acclaimed expert in leadership, AI, strategy and transformation. He helps boards, executive teams and senior leaders make sense of complex technological change and turn it into practical business value. Most experts make technology feel more complex. Kieran makes complex ideas simple, useful and actionable. He has worked with leadership teams across the globe to help them understand AI, use data to make better decisions and apply technology in ways that improve performance. The outcome is clearer thinking, stronger leadership confidence, better adoption and more measurable business benefit from technology. Kieran and his team bring the practicality many thought leaders lack, the human clarity large consultancies often miss, and the strategic depth that goes beyond standard AI training. If your organisation is trying to digitally transform and make AI useful, safe and commercially relevant, then connect. 📅 Book a call: https://calendly.com/kierangilmurray/catch-up 🌎 Website: www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🌐 Substack: https://kierangilmurray.substack.com 📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK or Audible https://www.audible.com/search?keywords=kieran+gilmurray Kieran

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October 26, 20269 min

Chapter 14 Evidence and Patterns of Success: What Intelligent Organizations Consistently Get Right

AI tools create lasting value when they improve how organizations decide, act, and learn. Strategic Intelligence connects these capabilities to the decisions that shape business performance. This episode explores three recurring success patterns and the operating disciplines that make them effective. TLDR / At a Glance • Prediction for repeated decisions at scale • Industrial twins for testing choices before commitment • Risk radars linking early detection to timely intervention • Proprietary signals embedded in operational workflows • Feedback loops and clear decision ownership • Focused investment in high-value decision domains Lasting advantage comes from matching the right pattern to consequential business decisions and building learning into execution. Support the show If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect. 🌎 Website: www.KieranGilmurray.com 📅 Book a call: https://calendly.com/kierangilmurray/catch-up 📘 Kieran Gilmurray | LinkedIn 🌐 Substack: https://kierangilmurray.substack.com 📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK AI Transparency Notice: This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.

October 23, 202612 min

Your Board Has An AI Problem, And It Is Not Technical

Talking about AI can feel productive. Governing AI is what protects enterprise value, reduces risk, and turns spend into results. We tackle the uncomfortable truth many boards face: AI still arrives as a management briefing, not as a board-owned system of oversight, even as it reshapes strategy, capital allocation, risk appetite, and public disclosure. TL;DR / At A Glance why AI now hits strategy, risk, disclosure, and enterprise value at once board readiness data showing knowledge gaps and weak oversight structures the difference between agenda time and genuine ownership the three-part oversight test: ownership, visibility, assurance EU AI Act timeline, AI literacy duties, and board-level penalty exposure why disciplined AI governance supports value creation at scale investor expectations for transparent, documented AI oversight warning signs: no inventory, unclear risk appetite, no named owner, weak literacy, unmanaged third-party AI practical actions: standing agenda, accountable executive, enterprise AI inventory, evidence of controls and outcomes We walk through why AI is no longer a “technology topic” that sits neatly in one committee. It cuts across responsible AI, data protection, bias, intellectual property, safety, and investor reporting, which means the full board must own the connective decisions. To make this practical, we share a simple “oversight test” we use to spot the gap between attention and accountability: can you clearly answer ownership (who is accountable), visibility (where AI is used), and assurance (how controls are independently evidenced)? The regulatory clock makes this urgent. The EU AI Act brings phased obligations, with major requirements applying from 2 August 2026 and meaningful duties already in force, backed by penalties that belong in the boardroom. We also unpack the market pressure: investors increasingly expect clear disclosure of how boards oversee AI governance and ethics, and weak answers are becoming a proxy red flag. You will leave with concrete steps: put AI on the agenda as a standing item, name an accountable executive and board owner, commission an enterprise AI inventory (including third-party tools), and require both value metrics and assurance evidence. If this helps, subscribe, share with a director or exec team, and leave a review. What is the one oversight question your organisation cannot yet answer? Support the show If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect. 🌎 Website: www.KieranGilmurray.com 📅 Book a call: https://calendly.com/kierangilmurray/catch-up 📘 Kieran Gilmurray | LinkedIn 🌐 Substack: https://kierangilmurray.substack.com 📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK AI Transparency Notice: This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.

October 21, 202614 min

AI Saved the Time. Leadership Must Decide Where It Goes.

AI is saving employees significant time, yet many organisations struggle to turn those gains into measurable business value. Without clear leadership decisions, released capacity is absorbed by routine work, rework and inefficient processes. This episode explores how leaders can use the Capacity Budget framework to allocate AI-generated time deliberately and make its impact measurable. TLDR / At a Glance The gap between AI time savings and business value Why unallocated capacity disappears into existing workloads The difference between individual productivity and organisational performance The Capacity Budget framework for allocating released time Measuring usable capacity, outcomes and accountability Balancing efficiency, growth, workforce development and wellbeing AI creates opportunities for greater productivity, but leadership determines how that capacity translates into lasting organisational value. Support the show If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect. 🌎 Website: www.KieranGilmurray.com 📅 Book a call: https://calendly.com/kierangilmurray/catch-up 📘 Kieran Gilmurray | LinkedIn 🌐 Substack: https://kierangilmurray.substack.com 📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK AI Transparency Notice: This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.

October 20, 202612 min

Chapter 13 Metrics That Matter: Measuring Success in the Intelligent Enterprise

AI performance metrics can show whether a model works, but they rarely explain whether it creates measurable business value. For executives, the challenge is connecting technical performance to operational decisions and financial outcomes. This episode explores how organizations can build credible measurement frameworks that demonstrate AI's contribution to business performance. TLDR / At a Glance Limitations of traditional AI performance metrics The KPI pyramid: technical, operational, and strategic measures Decision Intelligence and measurable workflow improvements Financial outcomes and credible value attribution Risk, trust, and negative KPI monitoring Leading indicators, lagging outcomes, and measurement discipline Effective AI measurement connects system reliability, decision quality, operational adoption, and economic results to establish a defensible case for sustained investment. Support the show If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect. 🌎 Website: www.KieranGilmurray.com 📅 Book a call: https://calendly.com/kierangilmurray/catch-up 📘 Kieran Gilmurray | LinkedIn 🌐 Substack: https://kierangilmurray.substack.com 📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK AI Transparency Notice: This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.

October 16, 202614 min

Chapter 12 Future Scenarios and the AGI Horizon: Preparing for Discontinuity Without Predicting It

AI capabilities can advance faster than organizations can supervise them. Preparing for the AGI horizon requires leaders to anticipate strain on decision processes, human oversight, and operational control. This episode explores how Decision Aperture and the Strategic Intelligence Loop support preparedness for rapid capability shifts. TLDR / At a Glance Augmentation, automation, and acceleration scenarios Automation bias and human judgment Workforce transitions and exception handling Monitoring, escalation, and intervention thresholds Accountability and external dependency risks Stress testing and scenario rehearsals If you’re waiting for a definitive “AGI arrival date” before you act, you’re already behind. We take a different route: treating advanced AI as a preparedness challenge, not a prediction contest. The core idea is decision aperture, designing decision processes, escalation paths, and control systems that stay coherent when capabilities move in jumps and adoption spreads faster than governance can absorb. We walk through three future scenarios for the intelligent enterprise: augmentation, automation, and acceleration. Augmentation sounds safe until automation bias erodes human judgement at speed. Automation promises productivity, but edge cases, responsibility, and workforce transition become the real constraints. Acceleration is the scenario leaders avoid, because it does not require AGI at all. It only requires that autonomy, coupling, and system complexity outpace your ability to observe what is happening, intervene quickly, and explain outcomes after the fact. From there we get practical: why capability jumps break monitoring and approval processes first, how organisational structure slows escalation when authority and expertise sit in different places, and why external dependencies on foundation models, cloud services, or agent platforms create risks you cannot fully control while still owning the outcomes. We also connect the dots to modern AI governance, including the NIST AI Risk Management Framework and the direction of travel in regulation such as the EU AI Act, where auditability, documentation, and meaningful human oversight become non-negotiable. We end with a concrete operating habit: stress testing and scenario rehearsal for agentic behaviour and governance response before you scale. Subscribe, share with a leader who needs this, and leave a review. What would break first in your organisation if AI capability jumped next month? Support the show If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect. 🌎 Website: www.KieranGilmurray.com 📅 Book a call: https://calendly.com/kierangilmurray/catch-up 📘 Kieran Gilmurray | LinkedIn 🌐 Substack: https://kierangilmurray.substack.com 📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK AI Transparency Notice: This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.

October 16, 202612 min

The AI reckoning: how boards must evolve to govern what matters most

Artificial intelligence is no longer a technical curiosity but a board-level imperative shaping long-term competitiveness. As adoption accelerates, many boards remain unprepared to govern its strategic and operational impact. Boards that align on AI posture, build fluency, and tailor governance can shift from reactive oversight to deliberate value creation. This episode explores how boards must evolve to oversee AI as a driver of enterprise transformation. TLDR / At a Glance AI adoption outpacing board governance Gap in board-level AI fluency and oversight AI reshaping industry structure and value pools Importance of defining a clear AI posture Four archetypes guiding governance models Six practical actions for effective board oversight AI has quietly moved from experimentation into the parts of the business that decide outcomes: pricing, hiring, customer decisions, operational optimisation, and even safety systems. That shift changes the board’s job overnight, because AI risk is not just a technical control problem. It is a governance, reputation, compliance, and strategy problem, made harder by opaque models, unpredictable behaviour, model drift, and third-party AI embedded deep in core processes. We walk through a practical framework for setting an explicit AI risk posture that balances innovation with protection. You’ll hear the clear distinctions between AI risk posture, risk appetite, risk tolerance, and risk capacity, plus why appetite should never exceed capacity. From there, we translate governance into action: board versus management responsibilities, three lines of defence, and how a single accountable executive can reduce fragmentation. We also get specific on defining red line AI uses, designing escalation thresholds, and tailoring posture by use case across customer-impact AI, safety-impact AI, and regulated decision systems. To make this real, we share a 30-60-90 day implementation playbook and a reporting cadence directors can rely on, including the metrics that matter: AI incidents, performance drift, bias and fairness indicators, compliance exceptions, audit findings, third-party exposure, governance coverage, and training completion. If you’re building responsible AI governance, tightening AI oversight, or preparing for regulators and auditors, this is a board-ready roadmap. Subscribe, share with a fellow leader, and leave a review with the one AI risk question your board should ask next. Support the show If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect. 🌎 Website: www.KieranGilmurray.com 📅 Book a call: https://calendly.com/kierangilmurray/catch-up 📘 Kieran Gilmurray | LinkedIn 🌐 Substack: https://kierangilmurray.substack.com 📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK AI Transparency Notice: This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.

October 12, 202614 min

Chapter 9 Infrastructure and the Agentic Future: The Hidden Engines of the Intelligent Enterprise

AI only creates lasting value when organizations can execute intelligence reliably under real operating conditions. Weak data, fragmented platforms, and disconnected workflows can turn promising models into fragile, optional tools. This episode explores the infrastructure required to make intelligence repeatable, scalable, and operational. The key takeaway is that intelligent enterprises depend as much on infrastructure, integration, and operating design as they do on algorithms. TLDR / At a Glance • The three-layer intelligence stack • Governed data as a reusable product • Shared platforms for model lifecycle management • Workflow integration as an adoption driver • Decision environments that reduce friction • Embedded feedback loops for continuous learning AI rarely fails at the idea stage. It fails after the promising pilot, when the organisation tries to execute the same “smart” decision again and again under messy real-world conditions. We dig into why intelligence only becomes real when it is operational and repeatable, and why infrastructure is the hidden engine of the intelligent enterprise. If your models look great in controlled tests but fall apart in production, the problem is often not the algorithm, it is the system around it. We break the challenge down into a practical three-layer intelligence stack. First comes the data foundation: data that works for reporting can be unusable for decision systems, especially when you need early, probabilistic signals. We look at how inconsistent definitions, brittle pipelines, and manual reconciliation create “model problems” that are actually upstream quality and ownership problems, and why treating key datasets as products with clear accountability (often associated with data mesh thinking) changes what is possible. Next is the platform layer, where MLOps stops being a buzzword and becomes a requirement for scale. A shared platform for development, deployment, monitoring, and version control helps teams spend time on decision logic rather than scaffolding, and it prevents the slow creep of shadow systems. Finally, we get to the application layer: the moment where behaviour either changes or stays the same. A slightly inferior model embedded inside the workflow, with next steps and audit trails, can outperform a better model sitting on a dashboard because integration removes friction and makes action routine. If you want AI that people trust, use, and improve over time, start here. Subscribe, share this with a colleague building AI in the enterprise, and leave a review telling us which layer is causing you the most pain. Support the show If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect. 🌎 Website: www.KieranGilmurray.com 📅 Book a call: https://calendly.com/kierangilmurray/catch-up 📘 Kieran Gilmurray | LinkedIn 🌐 Substack: https://kierangilmurray.substack.com 📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK AI Transparency Notice: This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.

October 12, 202613 min

Chapter 11 Competitive Advantage: Intelligence As Differentiation

AI access is becoming standardized, shifting competitive advantage toward how effectively organizations turn intelligence into better decisions and execution. Sustainable differentiation comes from proprietary operational data, embedded workflows, rapid learning, disciplined scaling, and strategic focus. The key advantage belongs to organizations that consistently connect signals, decisions, actions, and learning across the enterprise. This episode explores how the Strategic Intelligence Loop converts organizational experience into compounding advantage. TLDR / At a Glance • Strategic Intelligence Loop • Proprietary operational data moats • Intelligence embedded in workflows • Faster organizational learning cycles • Operational maturity and strategic coherence • Monetization, resilience, and trust Powerful AI is now cheap and widely available, so the old playbook of “pick the best model and win” is falling apart. We dig into what actually creates competitive advantage in the AI era: not access to algorithms, but the ability to improve decisions repeatedly and reliably. The centrepiece is the strategic intelligence loop a continuous cycle where signals drive decisions, decisions drive action, and action generates learning that strengthens the next round. That is what makes performance compound. We also get practical about where your edge really comes from: organisation-specific operational data. Conversions, fraud outcomes, renewals, delivery delays, service records, call centre notes and exception tickets quietly build an operating history that competitors cannot rent. When that signal is organised and connected, AI predictions stop being generic and start matching the messy reality of your customers and processes. Then we tackle the make-or-break move: workflow integration. Insights on dashboards are easy to ignore, but recommendations inside routing, underwriting, CRM, and collections systems change behaviour because they show up at the moment of decision. We talk through exceptions, decision rights, escalation, incentives, monitoring, and the cultural conditions that let teams report failures early so the system learns faster. We also touch on governance and trustworthy AI, including why approaches like the NIST AI Risk Management Framework treat risk management as a core design feature rather than a bolt-on. If you want to scale AI beyond pilots, build operational maturity, and keep your AI portfolio aligned to strategy, this is a roadmap. Subscribe, share the episode with a colleague, and leave a review if it helps. What part of your organisation’s intelligence loop is weakest right now? Support the show If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect. 🌎 Website: www.KieranGilmurray.com 📅 Book a call: https://calendly.com/kierangilmurray/catch-up 📘 Kieran Gilmurray | LinkedIn 🌐 Substack: https://kierangilmurray.substack.com 📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK AI Transparency Notice: This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.

October 8, 202617 min

Copilot Is Learning to Do the Work. Is Your Business Ready?

We track how Microsoft Copilot evolves from a drafting assistant into a system that can carry out multi-step work, build simple apps from plain language, and eventually run persistent agents while we are offline. We focus on what that shift forces leaders to decide on governance, approvals, ownership, and value, rather than counting licences and hoping for impact. TL;DR/At a Glance The shift from Copilot as helper to Copilot as doer and why it lands on the leadership agenda Home and Today as the new starting point for work and what that demands from processes and data quality Co-work as task execution across email, calendar, documents, and systems with designed approval points Usage based charging and the move from licence counting to cost per result thinking Copilot Code for building apps in plain language plus the ownership and support risks it creates Copilot is no longer just a helpful sidebar that drafts emails and summarises meetings. Microsoft’s latest updates point to something bigger: AI that can carry out multi-step tasks, build lightweight apps from plain English, and soon keep working towards a goal while we are offline. That leap changes the conversation from training and adoption to operating decisions leaders cannot dodge: what work do we hand over, where do we keep human judgement, and who is accountable when an agent gets it wrong? We walk through the major capabilities in plain terms, starting with Home becoming the single place to begin work, then moving into Co-work where Copilot takes actions across Microsoft 365 with explicit sign-off points. From there we explore Copilot Code, why “anyone can build an app” is both a gift and a governance risk, and what managed runtime means for IT oversight. We also unpack Autopilot, the most ambitious idea of all: persistent AI agents with their own identity, memory, and workspace, plus what independent evidence suggests about where agents are reliable and where they still overclaim. Behind the headline features, we dig into the parts that make AI delegation real in an organisation: data access (including Power BI and business system connectors), identity and audit through Entra, and the shift to usage-based billing that forces a new metric: value per result. The episode closes with a five-question readiness check leadership teams can answer in a single meeting to decide what to try first and what needs fixing before more autonomous tools arrive. You can also read this article on my website and LinkedIn pages, where I share regular insights on AI, strategy, and emerging technologies.. Support the show If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect. 🌎 Website: www.KieranGilmurray.com 📅 Book a call: https://calendly.com/kierangilmurray/catch-up 📘 Kieran Gilmurray | LinkedIn 🌐 Substack: https://kierangilmurray.substack.com 📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK AI Transparency Notice: This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.

October 7, 202617 min

Chapter 10 Talent and Skills for the AI Era: Building the AI Workforce of an Intelligent Organization

This episode features an abridged adaptation of Chapter 10, Talent and Skills for the AI Era: Building the AI Workforce of an Intelligent Organization . Discover why AI transformation is not simply a technology or hiring challenge. It requires leaders to redesign skills, roles, workflows and career pathways so intelligence can become part of everyday work. The episode explores AI’s rapidly advancing capabilities, its impact on knowledge work and the responsibility leaders have to prepare their organizations for a changing workforce. Explore The Executive’s Guide to Strategic Intelligence and access the free preview: https://kierangilmurray.com/strategic-intelligence/ Support the show If you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect. 🌎 Website: www.KieranGilmurray.com 📅 Book a call: https://calendly.com/kierangilmurray/catch-up 📘 Kieran Gilmurray | LinkedIn 🌐 Substack: https://kierangilmurray.substack.com 📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK AI Transparency Notice: This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.