Podcast
The Digital Transformation Playbook
Hosted by Kieran Gilmurray · EN
Kieran Gilmurray is a globally recognised authority on Artificial Intelligence, intelligent automation, data analytics, agentic AI, leadership development and digital transformation.
He has authored four influential books and hundreds of articles that have shaped industry perspectives on digital transformation, data analytics, intelligent automation, agentic AI, leadership and artificial intelligence.
𝗪𝗵𝗮𝘁 does Kieran do❓
When Kieran is not chairing international conferences, serving as a fractional CTO or Chief AI Officer, he is delivering AI, leadership, and strategy masterclasses to governments and industry leaders.
His team global businesses drive AI, agentic ai, digital transformation, leadership and innovation programs that deliver tangible business results.
🏆 𝐀𝐰𝐚𝐫𝐝𝐬:
🔹Top 25 Thought Leader Generative AI 2025
🔹Top 25 Thought Leader Companies on Generative AI 2025
🔹Top 50 Global Thought Leaders and Influencers on Agentic AI 2025
🔹Top 100 Thought Leader Agentic AI 2025
🔹Top 100 Thought Leader Legal AI 2025
🔹Team of the Year at the UK IT Industry Awards
🔹Top 50 Global Thought Leaders and Influencers on Generative AI 2024
🔹Top 50 Global Thought Leaders and Influencers on Manufacturing 2024
🔹Best LinkedIn Influencers Artificial Intelligence and Marketing 2024
🔹Seven-time LinkedIn Top Voice.
🔹Top 14 people to follow in data in 2023.
🔹World's Top 200 Business and Technology Innovators.
🔹Top 50 Intelligent Automation Influencers.
🔹Top 50 Brand Ambassadors.
🔹Global Intelligent Automation Award Winner.
🔹Top 20 Data Pros you NEED to follow.
𝗖𝗼𝗻𝘁𝗮𝗰𝘁 Kieran's team to get business results, not excuses.
☎️ https://calendly.com/kierangilmurray/30min
✉️ kieran@gilmurray.co.uk
🌍 www.KieranGilmurray.com
📘 Kieran Gilmurray | LinkedIn
257episodes
Episodes
Newest firstAll episodes
Why Faster AI Answers Can Make You Learn Less
2d ago00:19:49Tap to summarizeFrictionless AI feels like a miracle: one prompt, instant answers, spotless work. But when we use large language models for learning, that same “no effort” design can become a trap. Google Notebook LM agents break down the learning performance paradox, where AI can make you look brilliant in the moment while quietly preventing the mental work that builds memory, judgement, and real competence. If you have ever “understood” something with AI help and then blanked the next day, you will recognise what we mean. TL;DR / At a Glancethe learning performance paradox and why speed can mask absent learningcognitive offloading and metacognitive laziness in AI-assisted studyproductive struggle, desirable difficulty, retrieval practice and the generation effectscaffolding done right through hints, worked examples and calibrated challengeConMigo and CodeHelp as contrasting designs for preventing shortcut learningadaptive AI that captures microinteractions to model misconceptions and emotionsshared regulation to protect learner autonomy and avoid black box tutoringresponsible foundations: explainable AI, privacy-by-context and inclusive personasGoogle Notebook LM agents explore what a true AI learning companion should do differently, grounded in learning science: productive struggle, desirable difficulty, retrieval practice, and the generation effect. Instead of handing over solutions, the companion should ask you to explain, apply, and generate answers in your own words. It should also help with metacognitive calibration, so your confidence starts matching your actual understanding, not just the smoothness of the chatbot’s output. From there Google Notebook LM agents get practical, using real case studies. We look at ConMigo’s shift from strict Socratic tutoring to smarter scaffolding with hints and worked examples, and CodeHelp’s “sufficiency check” that trains students to troubleshoot by providing proper context. Google Notebook LM agents also unpack adaptive learning systems that remember your patterns over time, why shared regulation protects autonomy, and what responsible AI in education requires: explainable recommendations, privacy that fits the learner, and inclusive design that reflects diverse classrooms and lived experience. If you care about AI in education, learning how to learn, or building skills that last, listen now.Subscribe, share with a friend who relies on AI to study, and leave a review with the biggest change you are making to your prompts.Support the show𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.☎️ https://calendly.com/kierangilmurray/results-not-excuses✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com📘 Kieran Gilmurray | LinkedIn🦉 X / Twitter: https://twitter.com/KieranGilmurray📽 YouTube: https://www.youtube.com/@KieranGilmurray📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK
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The Human AI Operating System
1w ago00:22:07Tap to summarizeAI scale depends on more than access to models, pilots, or new tools. This episode examines why enterprise performance comes from designing the organisation around AI, rather than simply deploying technology into existing workflows.It explores the Human AI Operating System as a framework for repeatable AI value.TLDR / At a Glance• Five-layer AI operating model • Workflow redesign for adoption • Decision rights and accountability • Capability beyond basic training • Embedded governance and controls • Value tracking linked to outcomesThe central takeaway is that AI scales when work, decisions, capability, governance, and value operate as one aligned management system.Support the show𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.☎️ https://calendly.com/kierangilmurray/results-not-excuses✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com📘 Kieran Gilmurray | LinkedIn🦉 X / Twitter: https://twitter.com/KieranGilmurray📽 YouTube: https://www.youtube.com/@KieranGilmurray📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK
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The AI Risk Posture Playbook for Boards
1w ago00:12:01Tap to summarizeArtificial intelligence is now a board level risk with implications across strategy, operations, and reputation. Organisations must move from informal awareness to structured oversight to manage AI responsibly.This episode explores how boards define and operationalise an explicit AI risk posture.TLDR / At a Glance• AI as enterprise level risk category • Risk appetite, tolerance, capacity distinctions • Board versus management responsibilities • Red line AI use cases • Escalation thresholds and governance flows • 30, 60, 90 day implementation roadmapA clear AI risk posture enables controlled innovation while maintaining accountability, resilience, and regulatory readiness.Support the show𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.☎️ https://calendly.com/kierangilmurray/results-not-excuses✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com📘 Kieran Gilmurray | LinkedIn🦉 X / Twitter: https://twitter.com/KieranGilmurray📽 YouTube: https://www.youtube.com/@KieranGilmurray📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK
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PegaWorld 2026: The Year Agentic AI Had To Prove Itself
1w ago00:22:13Tap to summarizeEnterprise AI has entered a more demanding phase, where agentic systems must prove they can deliver predictable outcomes in real business operations. PegaWorld 2026 framed that shift around workflow discipline, cost control, governance, and enterprise readiness.This episode explores six lessons for leaders scaling AI beyond pilots.TLDR / At a Glance• Predictable AI and governed execution• Outcome based AI cost control• Closing the strategy to execution gap• Orchestrating agents through approved workflows• Legacy modernisation as AI readiness• Enterprise discipline in AI assisted developmentAI agents have had years to impress us. PegaWorld 2026 forces a tougher standard: prove you can run inside complex enterprises without cost surprises, compliance gaps, inconsistent decisions or another layer of fragmented tech. That shift matters if you own regulated operations, customer outcomes, technology risk, or a budget that has to hold up when usage scales from a pilot to millions of interactions. We dig into six takeaways that keep agentic AI trustworthy. The big one is predictable AI: do the heavier reasoning upfront when redesigning workflows and operating models, then keep live execution tight by using lighter AI to understand intent, select an approved workflow, and follow it consistently. We also unpack why ambiguity is the real project risk, and how tools like Pega Blueprint aim to turn business intent into build-ready workflow designs that can be governed, reused and audited. Cost becomes a board-level conversation when token-based pricing meets long context windows and multi-step processes. We argue for measuring AI economics by outcomes such as cost per completed case, not prompts, tokens or model calls, and explain how deterministic workflows can narrow agent scope to reduce spend and risk. From orchestration and Model Context Protocol through to legacy COBOL modernisation with AWS Transform, we connect the dots between workflow automation, AI governance, and true AI readiness. If you care about enterprise AI that lasts, subscribe, share this with a colleague, and leave a review with the one workflow you would redesign first.#PegaPartnerSupport the show𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.☎️ https://calendly.com/kierangilmurray/results-not-excuses✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com📘 Kieran Gilmurray | LinkedIn🦉 X / Twitter: https://twitter.com/KieranGilmurray📽 YouTube: https://www.youtube.com/@KieranGilmurray📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK
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Why AI Strategies Fail Before They Scale
2w ago00:22:07Tap to summarizeAI strategies often lose momentum when organisations move from pilots into real operating environments. Early progress can look convincing until ownership, governance, capability, workflow design, and value measurement are tested at scale.This episode explores why AI scale depends on organisational absorption.TLDR / At a Glance• Pilot to scale gap • Organisational absorption • Workflow redesign • Decision ownership • Governance and monitoring • Value measurementThe key takeaway is that AI scales when leaders redesign the operating model around trusted, repeatable execution.Support the show𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.☎️ https://calendly.com/kierangilmurray/results-not-excuses✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com📘 Kieran Gilmurray | LinkedIn🦉 X / Twitter: https://twitter.com/KieranGilmurray📽 YouTube: https://www.youtube.com/@KieranGilmurray📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK
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Why Your AI Focus Group Keeps Saying Three
2w ago00:22:02Tap to summarizeYou spend years building a product, polish the packaging, nail the pitch… then you hit the terrifying question: is anyone actually going to buy it? We dig into a 2025 research result from PyMC Labs and Colgate-Palmolive that aims straight at that fear with AI market research, synthetic consumers, and large language models that can simulate purchase intent at scale.TL;DR / At A Glancethe core problem with direct Likert ratings and why LLMs collapse to neutral threeshow semantic similarity rating converts free-text responses into numerical scores using embeddings and cosine similaritywhy follow-up AI grading helps but still trails the embedding-based approachwhat 57 real product surveys and 9,300 human responses reveal about accuracy and distribution matchinghow persona prompting reproduces real demographic patterns across age and income constraintswhy zero-shot LLM methods can beat supervised machine learning models trained on the same domainThe shocker is that the first attempt fails badly. When you make models like GPT-4 or Gemini answer a classic Likert scale with a single number, they hedge and pile up on neutral “3” ratings. The fix is not “better AI”, it is better questioning. Google Notebook LM Agents help us unpack semantic similarity rating: let the model respond in natural language, convert that text into embeddings, and map it to five anchor statements using cosine similarity. You get fast, automated scoring without stripping away the model’s reasoning.From there, we pressure-test the method against thousands of real survey responses across dozens of personal care product concepts, then look at whether AI personas actually reflect real constraints like age and income. We also compare the approach with traditional machine learning models such as LightGBM, and dig into an underrated advantage: synthetic consumers can produce richer, more candid qualitative feedback than many human panels.If you care about product testing, consumer insights, or the future of focus groups, listen through and tell us where you’d trust this and where you wouldn’t. Subscribe, share with a colleague, and leave a review with your take: would you let synthetic consumers influence a real launch?Paper: http://arxiv.org/abs/2510.08338Support the show𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.☎️ https://calendly.com/kierangilmurray/results-not-excuses✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com📘 Kieran Gilmurray | LinkedIn🦉 X / Twitter: https://twitter.com/KieranGilmurray📽 YouTube: https://www.youtube.com/@KieranGilmurray📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK
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AI-First Strategy at Scale: Pega's Roadmap with David Vidoni
2w ago00:06:55Tap to summarizeToken subsidies are fading, AI prices are rising, and suddenly the fun part of experimentation comes with a nasty surprise: runaway spend. We dig into what that shift means for CIOs and IT leaders who still need to ship results, protect budgets, and prove ROI. If you have spent time counting tokens or worrying that one enthusiastic pilot will burn through a month’s AI budget, this conversation is for you.David Vidoni, CIO at Pega, shares why predictable cost matters as much as model capability and how “charging for outcomes” changes the way you govern AI. We talk about the practical tension between creativity and cost control, and why leaders should pause and ask whether AI is genuinely the best tool for a given challenge. The goal is not to slow innovation down, but to stop wasting energy on spend anxiety and refocus on measurable business value.We also get concrete on delivery: how Blueprint supports a design-first approach that clarifies what you are building before you build it, reduces costly mistakes, and speeds up time to first release. You will hear real internal stats, plus what it takes to deliver secure, compliant, repeatable outcomes rather than variable answers. Finally, we explore agentic AI wins in legal and contract work, including significant hours saved and major ticket deflection.Listen, then subscribe, share with a fellow CIO or product leader, and leave a review with your biggest AI cost or governance challenge.Support the show𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.☎️ https://calendly.com/kierangilmurray/results-not-excuses✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com📘 Kieran Gilmurray | LinkedIn🦉 X / Twitter: https://twitter.com/KieranGilmurray📽 YouTube: https://www.youtube.com/@KieranGilmurray📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK
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Enterprise AI Will Not Scale Until You Redesign Work
2w ago00:23:20Tap to summarizeYour AI can write a tidy email summary, but that is not the job. The real leap is from passive text generation to agentic AI that can read context, plan a sequence of steps, use tools through APIs, and execute actions inside real enterprise systems. That leap is thrilling, and it is also where most organisations hit the wall: plenty of pilots, very little production impact, and a growing fear of what happens when an autonomous agent is allowed anywhere near procurement, customer data, or payments.TL;DR:why AI investment keeps rising while production success stays low the scaling wall: latency, compute cost, fragile error handling, messy data the trust gap when autonomous agents can touch procurement, payments, and live systems process inertia and the trap of paving the cow path pragmatic AI mindset: hyper-specialised utility over sci-fi general intelligence six pillars of agentic AI: tool use, action, memory, perception, planning, orchestration multi-agent systems as modular digital specialists that isolate risk and raise accuracy We use Google Notebook LM Agents to take insights from a Deloitte AI Institute report produced with Google Cloud to unpack why scaling enterprise AI is so hard and what actually changes when you build goal-oriented agents. Google Notebook LM Agents break down the practical architecture behind autonomous digital workers, including memory and reflection, multimodal perception, and planning that turns an ambiguous goal into an executable workflow. They also dig into multi-agent systems, where specialised agents work like a kitchen brigade rather than one giant generalist model, and why that modularity improves accuracy while reducing the blast radius when something fails.Autonomy without governance is just risk at speed, so we get specific about controls: an agent OS hub-and-spoke model for visibility, FinOps guardrails and kill switches to stop runaway compute spend, and a defence-in-depth approach to security. That includes linguistic guardrails against prompt injection, sandboxing, semantic checks with constitutional AI auditing before actions execute, and infrastructure-level threat hunting. We also cover IDAMA, identity and access management for agents, so permissions stay least-privilege and accountability stays human-owned.Finally, we bring it back to reality: change management, process redesign, and data gravity. You will hear concrete case studies in accounts payable automation and an agentic knowledge assistant with citations, plus why Apache Iceberg and cross-cloud lakehouse patterns matter for querying data where it lives. Subscribe, share, and leave a review if this helped, and tell us what task you would trust an agent to run first.Support the show𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.☎️ https://calendly.com/kierangilmurray/results-not-excuses✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com📘 Kieran Gilmurray | LinkedIn🦉 X / Twitter: https://twitter.com/KieranGilmurray📽 YouTube: https://www.youtube.com/@KieranGilmurray📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK
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Kieran Gilmurray x Matt Healy: The Reality of Agentic AI
3w ago00:04:39Tap to summarizeAI is moving fast, but enterprise leaders are starting to ask a sharper question: are we getting value for the money we’re spending? Matt Healy from Pega joins us to unpack what “agentic transformation” looks like when it has to survive real-world constraints like compliance, security, and customer-facing reliability, not just a slick prototype.TL;DR:extending AI-driven development into the platform with coding agents such as GitHub Copilot, Codex, and Cloud Codedeploying agents that run predictably against rules, regulations, and compliance needsshifting from token-based consumption to outcome-based agentic pricing for predictable ROIwhy vendor pricing changes can flip an AI use case from profit to lossusing AI to analyse legacy systems, translate code into natural language, and guide modernisationcombining AWS legacy analysis with Blueprint to support mainframe exit and reimagined journeysbuilding enterprise-ready apps that are explainable, secure, scalable, and consistently developedWe talk about AI-driven development and the growing role of coding agents in everyday work, including tools such as GitHub Copilot, Codex, and Cloud Code. Speed is great, but Matt explains why it can also create apps that aren’t explainable, hide vulnerabilities, and struggle to scale. The goal is to keep the acceleration while making the output enterprise-ready: transparent, deployable at massive scale, compliant, secure, and built consistently.Cost control is the other make-or-break topic. Token-based pricing sounds simple until reasoning agents start consuming unpredictably and vendors change their models. Matt lays out an outcome-based approach to agentic pricing that focuses on work done and value delivered, aiming for predictable costs and predictable ROI so promising AI use cases don’t suddenly turn unprofitable.We also dig into Pega Blueprint’s progress on legacy modernisation, including how AWS-powered analysis of legacy languages like COBOL can produce natural language understanding that feeds transformation work. If you care about mainframe exit, cloud modernisation, and reimagining customer journeys rather than lift-and-shift, you’ll find plenty to take away. If you found this useful, subscribe, share it with a colleague, and leave a review so more builders and leaders can find the show.#PegaPartnerSupport the show𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.☎️ https://calendly.com/kierangilmurray/results-not-excuses✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com📘 Kieran Gilmurray | LinkedIn🦉 X / Twitter: https://twitter.com/KieranGilmurray📽 YouTube: https://www.youtube.com/@KieranGilmurray📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK
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Behind the Scenes at PegaWorld: A Conversation with Kara Manton
3w ago00:05:40Tap to summarizeLegacy systems do not fail because teams lack ambition. They fail because nobody has the time to untangle years of code, edge cases and hidden business logic. We sit down with Kara Manton, business director in Pega’s product engineering function, to unpack the biggest PegaWorld announcements aimed at changing that reality, starting with why Pega Infinity 26 is being called one of the best releases in a decade. TL;DR:Infinity 26 as a major step forward for AI powered workflow automationBlueprint AI inside Infinity Studio and an AI assistant that builds rules behind the scenesCalling Pega workflows from different AI tools while keeping execution predictableAWS Transform plus Blueprint to modernise legacy code into production apps in three monthsDesigning business rules and user experience earlier to cut rework laterNo token charging and a shift towards outcomes based pricingWe talk through what it looks like when AI is designed to strengthen workflow automation rather than replace it. Kara explains how Pega Blueprint has evolved from an early idea into a deeper application design experience where you can shape process flows, business rules and user experience before you build. We also dig into Infinity Studio with its built-in AI assistant, where you can chat and have the system generate Pega rules behind the scenes, opening the door for more people to participate in creating workflow applications. The conversation turns to two big enterprise concerns: modernisation speed and AI cost. Kara highlights the on-stage AWS Transform announcement, describing how AWS Transform plus the power of Blueprint can take organisations from a legacy code base to a production app in three months. We also cover Pega’s decision not to charge for tokens, focusing instead on outcomes and predictable cost in a world where tokenomics and model changes can feel chaotic. If you care about practical, governed AI, agentic workflows and faster legacy transformation, this one is for you. Subscribe, share with your team, and leave a review with the workflow problem you want to modernise next.#PegaPartnerSupport the show𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.☎️ https://calendly.com/kierangilmurray/results-not-excuses✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com📘 Kieran Gilmurray | LinkedIn🦉 X / Twitter: https://twitter.com/KieranGilmurray📽 YouTube: https://www.youtube.com/@KieranGilmurray📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK
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