Podcast
The Digital Transformation Playbook
Hosted by Kieran Gilmurray · EN
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
284episodes
Episodes
Newest firstAll episodes
PegaWorld 2026: The Year Agentic AI Had To Prove Itself
Jun 2000:22:13Tap 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
Jun 1800:22:07Tap 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
Jun 1600:22:02Tap 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
Jun 1500:06:55Tap 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
Jun 1400:23:20Tap 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
Jun 1100:04:39Tap 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
Jun 1100:05:40Tap 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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The Powerful Strategic Subtraction Test for Smarter Decisions
Jun 1000:11:59Tap to summarizeAI can accelerate work, but it can also multiply clutter when obsolete processes stay in place. This episode examines strategic subtraction as a leadership discipline for improving AI value, capacity, and operating focus.It explores how leaders decide what to remove, redesign, protect, or simplify. TLDR / At a Glance• Strategic subtraction discipline • Automation before redesign risk • Workflow clutter and decision friction • The VITALS subtraction test • Capacity release and governance focus • Protecting trust, compliance, and learningAI can make your organisation faster while quietly making it worse. If we use copilots and agents to accelerate reports nobody reads, approvals nobody trusts, and meetings that never end in a decision, we are not transforming anything, we are scaling clutter.We take on the most common starting point for AI transformation and argue it is strategically dangerous: asking what can be automated. The better first question is tougher and far more useful: should this work still exist in its current form? From there, we explore why AI shifts the economics of production but does not fix the real constraint in many businesses, which is attention, coordination, and the ability to absorb information without drowning in it.To make subtraction practical, we walk through a simple leadership tool: the Strategic Subtraction Test, built around six prompts on value, interference, duplication, assurance risk, liberation of capacity, and strategic fit. You will hear how to apply it to real work objects such as meeting series, dashboards, approval steps, governance forums, workflows, and tools, plus concrete examples of actions like simplifying low-risk approvals, consolidating overlapping governance, substituting decks with live views, and hiding specialist reports from default circulation.We also get specific about what not to cut. Some work that looks slow is actually trust infrastructure: legal controls, cyber checks, privacy safeguards, incident reviews, escalation routes, and learning loops. If we remove those without redesign, we can damage compliance, resilience, and judgement. If you want AI strategy that delivers capacity release rather than work intensification, subscribe, share this with a leader who owns “AI rollout”, and leave a review telling us what work you would stop carrying forward.The key takeaway is that effective AI transformation depends on removing low value work before accelerating the system around it.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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AI’s Impact on Junior Productivity and Skill Development
Jun 1000:09:12Tap to summarizeAI is dramatically reshaping how junior professionals learn and perform at work. New evidence shows novices reaching competency in a fraction of the time, with significant implications for productivity and talent development.This episode explores how AI changes learning mechanics, performance outcomes, and risk management for junior talent.TLDR / At a Glance• Accelerated time to competence • Disproportionate gains for juniors • AI-driven feedback and scaffolding • Overreliance and accuracy risks • Enterprise access versus shadow tools • Leadership guardrails and trainingAI can compress years of learning into months, but only when paired with structured oversight, calibration, and secure implementation.Juniors reaching veteran-level productivity in a fraction of the time should make every leader curious and a little nervous. We dig into what recent evidence says about AI copilots, coding assistants, and AI tutors, and why the biggest performance gains consistently appear in the least experienced employees. When AI surfaces the right information at the right moment, it doesn’t just speed up tasks, it rewires the day-to-day learning loop.We walk through the mechanisms behind the jump in output and quality: tighter feedback cycles, just-in-time knowledge retrieval, and scaffolding that handles routine work so juniors can focus on judgement. But speed has a shadow side. When teams treat confident AI output as truth, accuracy can fall on complex tasks, and juniors can mistake AI fluency for genuine mastery. That “illusion of competence” becomes a long-term capability risk, not just a short-term mistake.We also tackle the growing policy divide. Organisations that provide secure enterprise AI accelerate development safely, while blanket bans often push people into shadow AI tools, raising data privacy, compliance, and IP risks. Our practical takeaway is straightforward: give safe access early, train for prompting and verification, keep peer review, set clear guardrails, and measure more than productivity by tracking how often people verify and how they perform without AI.If you found this useful, subscribe, share it with a manager or mentor, and leave a review. What guardrail would you put in place 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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AI Fluency Is Not What Most Organisations Think It Is
Jun 1000:09:14Tap to summarizeMany organisations mistake frequent AI tool use for genuine AI fluency. This episode examines why visible activity often masks shallow capability, fragmented workflows, and inconsistent business value.It explores how leaders can move AI from experimentation into structured execution.TLDR / At a Glance• Usage versus fluency • Fragmented adoption patterns • Workflow integration • Repeatable AI practices • Behaviour and judgement • Operating standards for AIThe key takeaway is that real AI fluency emerges when AI becomes embedded in how work is designed, delivered, measured, and improved.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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