The Digital Transformation Playbook

Kieran Gilmurray
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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

  1. Turning AI Failure Rates Into A Real Strategy

    1D AGO

    Turning AI Failure Rates Into A Real Strategy

    Most AI programmes don’t fail for lack of ideas; they fail because teams choose the wrong path and underestimate the system around the model. We dig into a candid build vs buy vs hybrid framework backed by current research, hard numbers, and battle‑tested operating patterns so you can make a decision that ships value fast and scales safely. TLDR / At A Glance market failure rates and momentum reversaldeterministic versus probabilistic architecture realityDIY failure patterns across data, integration, and governancetalent scarcity, wage premiums, and TCO impactreal costs, timelines, and hidden line itemswhen building makes sense and when it does notpartner advantages for time to value and risk controlthree‑year TCO ranges for DIY and partner pathsexecutive decision matrix and rules of thumbhybrid roadmap with phased capability buildingWe start by grounding the stakes: high abandonment after proof of concept, scarce senior AI talent, and governance gaps that surface at scale. From there, we contrast deterministic software with probabilistic, agentic systems and explain why early architectural choices compound through data pipelines, retrieval strategies, identity, permissions, observability, and compliance.  You’ll hear why DIY efforts often stall on brittle data, weak integration, and unclear goals, and how professional partners change the odds with established patterns, evaluation harnesses, and safety from day zero. Then we get practical. We map real costs and timelines, separating model hype from the true TCO drivers: data plumbing, security, monitoring, and continuous testing.  We walk a three‑year comparison of an internal build versus a partner‑led implementation running on a commercial platform, highlighting time to first value, risk profiles, and success probabilities.  Our decision framework for executives distils five dimensions—core competency, urgency, capability, budget, and long‑term vision—into plain rules of thumb, plus a scenario matrix that points to build, buy, or hybrid recommendations. Finally, we outline a phased hybrid plan that most teams can execute: partner‑led delivery in months one to six to prove value; collaborative ownership by month twelve to codify playbooks; and internal‑led innovation thereafter to invest in defensible differentiators on top of a proven substrate.  If you’re serious about moving from POCs to production, reducing risk, and protecting your moat, this guide will help you choose with your eyes open. If the conversation helps you, subscribe, share with your team, and leave a quick review so others can find it. Like some free book chapters?  Then go here How to build an agent - Kieran Gilmurray Want to buy the complete book? Then go to Amazon or  Audible today. Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 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

    30 min
  2. ROI That Boards Can Believe

    3D AGO

    ROI That Boards Can Believe

    Budgets are climbing, slides are shiny, yet boards still ask the same hard question: where is the ROI? We dig into the paradox of aggressive AI investment with thin or invisible returns and lay out a concrete path to results that show up on the income statement.  Our focus is practical and board-ready: what to measure, how to attribute, and how to avoid pilot purgatory by fixing data, integration, and sponsorship first. At A Glance / TLDR: the ai roi paradox and why it persistsdata quality, ownership and sponsorship as limitersminimum viable data stack and integration pathwaysthree-tier readiness model with timelines and targetsfour-pillar roi framework efficiency, revenue, risk, agilityboard-ready one-page business case and scenariosmetrics baseline, dashboard cadence, and attributionsize-specific guidance for small, mid-market, and enterprisereal-world benchmarks and examplescommon pitfalls vanity metrics, no baseline, hidden costsWe unpack a minimum viable data stack—auditable governance, clear lineage, and API access to systems of record—so agents can read, act, and write back. Then we map a three-tier readiness approach to plan timelines, budgets, and expected payback without hype.  High-readiness teams often move from pilot to production in about 16 weeks; foundation-builders invest in plumbing but still reach solid first-year ROI once adoption stabilises.  Throughout, we translate activity into outcomes using a four-pillar ROI framework: efficiency gains across end-to-end workflows, revenue generation through higher conversion and reduced churn, risk mitigation with quantified avoided costs, and business agility measured by decision speed and time to market. To help you win support, we share a one-page business case format your CFO can audit, with scenario modelling, conservative attribution, and a metrics dashboard that tracks response times, CSAT, unit costs, and churn over time.  We also highlight real benchmarks and examples—from large-scale service operations to sales enablement—showing how integrated data and human-in-the-loop design compress cycle times and unlock capacity. If you’re ready to move from proofs of concept to production value, this playbook shows how to measure what matters, fund what works, and expand across adjacencies with credibility.  Subscribe, share with a teammate, and leave a review telling us which pillar you’re tackling first. Like some free book chapters?  Then go here How to build an agent - Kieran Gilmurray Want to buy the complete book? Then go to Amazon or  Audible today. Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 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

    25 min
  3. Agents At Work

    MAR 5

    Agents At Work

    Imagine asking your assistant to “cut costs by 10%,” then learning it quietly hired five bots, switched your insurance, and exposed you to a lawsuit.  That’s the new reality of agentic AI: software that doesn’t just talk, it acts—spends, negotiates, signs, and delegates at machine speed. We take you inside this shift and show how to keep control when intelligent delegation gets real. TLDR / At A Glance principal–agent misalignment and span of controlauthority gradients, sycophancy, and zones of indifferencecontract-first task decomposition and verifiable outcomesopen agent marketplaces, negotiation, and Pareto trade-offsverifiable credentials, process monitoring, and privacyzero-knowledge proofs and homomorphic encryptionresilience, failover, escrow, and recursive liabilitythreat models and the confused deputy problemmoral crumple zones, meaningful oversight, and de-skillingcurriculum-aware routing and socially intelligent agentsWe start with the human blueprint that still applies: the principal–agent problem, misaligned incentives, span-of-control limits, and authority gradients that make smaller models defer to larger ones. From there, we get practical. Contract-first task decomposition turns fuzzy goals into verifiable promises, enabling open marketplaces where agents bid on work with capability proofs, not just price tags. The delegator must juggle speed, cost, quality, privacy, and safety, seeking Pareto-efficient choices while escalating only when red lines are at stake. To make this safe, we trade flimsy star ratings for verifiable credentials, and we show why outcome checks aren’t enough without process-level monitoring. Trust and privacy take centre stage with zero-knowledge proofs and homomorphic encryption—tools that let agents prove correct work without ever seeing or leaking your secrets. Resilience gets engineered in: smart contracts that define kill switches, instant failover, and escrow that slashes bad actors. Recursive liability pushes accountability up the chain so no one can hide behind a subagent three layers down. We also map today’s threat landscape—from model extraction to the confused deputy problem—and outline practical defences built on least privilege and robust input hygiene. The ethical frontier matters just as much. We unpack moral crumple zones that turn humans into liability shields, and we argue for meaningful oversight with time and authority to intervene. To prevent de-skilling, we explore curriculum-aware routing that intentionally sends tasks to people to preserve judgement.  The destination is clear: an ecosystem of specialised agents governed by provable contracts, strong credentials, cryptographic trust, and responsibility that actually sticks. Subscribe, share with a colleague who runs ops or risk, and tell us: where should we draw the first guardrails? Source:  Intelligent AI Delegation  Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 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

    22 min
  4. From Guardrails To Growth: Building Trustworthy AI At Scale

    MAR 4

    From Guardrails To Growth: Building Trustworthy AI At Scale

    What separates a celebrated AI launch from a brand‑damaging crisis is not a smarter model, but smarter governance. We pull back the curtain on how top performers turn guardrails into a growth engine, showing the concrete steps that keep innovation flowing while risk stays inside appetite. From defining decision rights to knowing exactly when to hit pause, we make governance practical, testable, and fast. TLDR / At A Glance: treating governance as the AI operating systemrising risk and regulatory context with quantified costssafety guardrails across input, output, and processinghuman in the loop approval gates and escalation rulesfail safes, circuit breakers, rollback and incident tiersbrand voice definition, disclosure and consistencycompliance by design mapped to NIST and ISOmetrics for performance, quality and business impacttesting culture with red teaming and canary releasesWe start with the real stakes: escalating breach costs, a crowded regulatory landscape spanning the EU AI Act, GDPR, and state laws, and a board‑level demand for evidence that AI meets enterprise standards. Then we get hands‑on with a three‑pillar framework. You’ll hear how to design input, output, and processing controls that block toxic content, defend against prompt injection, enforce least privilege, and preserve immutable audit trails. We outline human‑in‑the‑loop approvals for high‑stakes actions, plus circuit breakers, blue‑green rollbacks, and incident tiers that compress time to recovery and align with reporting clocks. Brand and compliance take centre stage next. We show how to lock a consistent voice across channels, disclose AI use, and translate legal duties into a living checklist for data governance, consent, explainability, auditability, and the right to contest. With NIST AIRMF, ISO IEC 42001, and COBIT as scaffolding, your controls become systematic and auditable across global operations. We tie it together with quality metrics, observability, and a test culture of red teaming, regression suites, canaries, and A/Bs so you can measure accuracy, satisfaction, and cost without chasing vanity dashboards. Finally, we share an operating model that scales: an executive‑led AI Governance Council, clear day‑to‑day roles in security and ethics, and a maturity path from ad hoc fixes to optimised practice. Real‑world cases in healthcare, banking, and e‑commerce reveal how governance unlocks adoption and ROI, not just risk reduction. If you’re ready to move fast without breaking what matters, press play, take the checklist, and share it with your team. Subscribe, leave a review, and tell us which guardrail you’ll implement first. Like some free book chapters?  Then go here How to build an agent - Kieran Gilmurray Want to buy the complete book? Then go to Amazon or  Audible today. Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 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

    27 min
  5. From Demo To Durable Asset

    FEB 26

    From Demo To Durable Asset

    A flashy prototype is easy; keeping value online, secure, and affordable is the real test. We walk through a practical path from demo to durable asset, showing how reliability, scalability, security, and maintainability turn experiments into systems executives can trust.  The conversation connects architecture choices to financial outcomes, making the case that every decision about serverless, containers, data, and integration is really a budgeting and risk move in disguise. At A Glance / TLDR: • framing demo-to-asset mindset and executive concerns • four pillars reliability, scalability, security, maintainability • market gaps, governance and CEO oversight • architecture as financial strategy for speed and cost • serverless for bursty loads, containers for control • move from static data to streaming pipelines • integration as platform, not project • zero trust identity, encryption, audit trails • cost tiers pilot, department, enterprise • timelines, sequencing ambition, FinOps discipline • reusable integrations and compliance by design • portfolio governance, scale what works We break down the four pillars of production readiness and why they map so closely to CFO and CISO priorities.  You will hear a clear comparison of serverless versus containers, with workload patterns that determine cost, speed to market, and lock-in risk.  We then shift from static documents to real-time streaming, explaining schema governance, observability, and replay, and why faster data loops enable customer service, fraud, inventory, and risk use cases where minutes matter.  Integration takes centre stage as the last mile that decides both timeline and ROI; we outline permissions, backlogs, and reuse strategies that convert brittle pilots into repeatable wins. Security moves from lab shortcuts to a zero trust posture grounded in identity, encryption, and continuous monitoring. We discuss the breach economics that justify early investment and the practical controls that keep secrets out of prompts and logs while preserving auditability.  To anchor planning, we map three cost tiers—pilot, departmental solution, and enterprise platform—with realistic one-time and run-rate ranges, plus timelines that reflect integration maturity and governance.  By sequencing ambition, aligning workloads to the right compute model, adopting FinOps discipline, and treating integrations as products, you build a platform that compounds value quarter after quarter. If this lens helps you steer from hype to durable outcomes, follow the show, share it with a teammate who owns the roadmap, and leave a quick review so others can find it. Like some free book chapters?  Then go here How to build an agent - Kieran Gilmurray Want to buy the complete book? Then go to Amazon or  Audible today. Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 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

    23 min
  6. From Solo Agent To Swarm Mastery

    FEB 24

    From Solo Agent To Swarm Mastery

    When adoption dips, renewals wobble, and compliance blocks progress, a lone AI agent won’t save the quarter. We explore how multi‑agent swarms replace silos with coordinated specialists, turning scattered signals into decisive action across billing, support, product, and finance.  Drawing on proven patterns, we walk through four collaboration modes - sequential handoffs, parallel processing, hierarchical coordination, and peer collaboration - and show how to combine them for speed, accuracy, and clear ownership. At a Glance / TLDR the problem with single‑agent silos and concurrent enterprise issuesfour coordination patterns and when to use eachevent‑driven communication and layered context for coherenceconflict resolution, enforcement agents, and safety protocolsfive specialist roles for customer success swarmsthe coordinator’s dynamic routing, load balancing, and escalationmicroservices, service mesh, and state management patternsmessaging backbones, retries, and dead‑letter handlingcaching, auto‑scaling, and circuit breakers for resiliencestrategic rollout, ROI discipline, and cultural alignmentWe break down the roles that make customer success swarms work: triage as the front door, knowledge as corporate memory with retrieval‑augmented generation, research as the external lens, action as executor across live systems, and follow‑up as quality control.  At the centre sits the coordinator, acting as conductor rather than soloist - dynamically activating agents, balancing capacity, predicting the best route, and enforcing a single source of truth, audit trails, and human escalation. That governance turns autonomy into accountability and reduces risk while improving outcomes. For leaders shipping these systems, architecture matters. Microservices and a service mesh keep services scalable and secure. Event‑driven messaging builds decoupled, high‑throughput collaboration; event sourcing and CQRS maintain consistent state without bottlenecks. Enterprise message buses handle ordering, retries, and dead letters, while caching, auto‑scaling on coordination load, and circuit breakers protect performance and resilience.  We close with the strategic lens: why orchestration will become baseline across enterprise apps, how coordination intelligence compounds over time, and what disciplines - measurement, governance, phased rollout, and cultural alignment - separate lasting value from hype. If this helped you think beyond chatbots toward orchestration, follow the show, share it with a teammate who owns customer retention, and leave a quick review so others can find it. Like some free book chapters?  Then go here How to build an agent - Kieran Gilmurray Want to buy the complete book? Then go to Amazon or  Audible today. Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 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

    21 min
  7. Can AI Tackle Learning Poverty In The Global South

    FEB 23

    Can AI Tackle Learning Poverty In The Global South

    A stark number sets the stakes: seven in ten 10-year-olds in low and middle income countries cannot read a simple sentence. We take that reality out of the abstract and into a crowded classroom, following Saad, who is lost in long division, and Fatima, who is bored because the pace is too slow. F rom there we explore whether AI can truly help systems leapfrog toward quality education, or whether it risks becoming a shiny diversion that deepens inequality. TLDR / At A Glance: • learning poverty at 70 percent among 10-year-olds in low and middle income countries • web of exclusions across gender, disability, conflict, language and culture • access success but quality failure in crowded classrooms • personalised AI tutoring that diagnoses gaps and adapts tasks • high-dosage tutoring gains in Edo State, Nigeria • teacher workload relief through planning and grading automation • Nova Sola WhatsApp chatbot saving one hour per lesson plan • local language content generation to counter colonial curricula • universal AI literacy for critical, ethical use • co-intelligence as a design goal and last-mile inclusion We dig into concrete, on-the-ground examples. An after-school pilot in Edo State, Nigeria used an AI tutor to deliver learning gains equal to one-and-a-half to two years in only six weeks, showing what high-dosage, one-on-one support can do when cost barriers fall. We look at teacher-centred tools too: a WhatsApp-based lesson planning assistant in Brazil that saves an hour per plan, turning automation into time for rest, feedback, or one-on-one care. And because connectivity is the fault line, we unpack “AI unplugged”: paper tests photographed on a single phone, uploaded later, analysed in the cloud, and returned as simple, actionable diagnostics that guide tomorrow’s lesson. We also spotlight the urgent need for culturally relevant content, highlighting rapid generation of children’s books in local languages to replace decades-long shortages. But speed without equity is a trap. We name the Matthew effect at play when solutions assume electricity and broadband that most schools do not have.  We weigh innovation against transformation, asking not only how to teach but what to prioritise when labour markets shift and community knowledge matters.  Alongside sobering OECD futures like “education outsourced,” we argue for universal AI literacy so every child can question sources, spot bias, and understand how recommendations are made. The north star is co-intelligence: humans leading, AI extending reach, with system design that includes infrastructure, teacher training, governance, and language. If you care about closing the learning gap without creating a permanent underclass, this conversation is for you.  Listen, share with a colleague who works in education or development, and leave a review telling us one low-tech idea that could scale in your context.  Your feedback helps more people find the show and keeps this work moving forward. Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 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

    14 min
  8. How 12 Percent Turn AI Into Growth

    FEB 19

    How 12 Percent Turn AI Into Growth

    The hype is loud, but the scoreboard is quiet. We dig into a global study of 1,200 companies and reveal why only 12 percent qualify as true AI achievers - firms that turn models into money, scale beyond pilots, and reshape how they build, price, and deliver products. Instead of vague talk, we map a clear route from ambition to results and show how strategy, culture, and plumbing work together. TLDR / At A Glance: • McCarthy’s definition set against today’s reality • The four AI maturity archetypes and what they miss • Why experimenters fall behind as the gap compounds • Strategy and sponsorship as a board-level mandate • Upskilling domain experts to create hybrid talent • Escaping pilot purgatory with MLOps and trust • Explainable models in R&D and operations • Responsible AI frameworks that reduce risk • Investment shifts toward data hygiene and cloud • Orchestrating all five factors in parallel We start with the four archetypes - achievers, builders, innovators, and experimenters - and explain the traps each group falls into. From there, we unpack the five factors that consistently predict outperformance.  You’ll hear how executive sponsorship turns AI into a board-level priority and why a construction leader bet on generative design to create thousands of viable blueprints, shifting from incremental gains to a new way of making buildings.  We then show how upskilling domain experts beats hiring for code alone, with a frontline engineer-turned-analyst saving seven figures by pairing machine knowledge with data tools. Next, we tackle the hard work of industrialising the AI core moving from demos to production. A consumer goods giant earned scientist trust with explainable models for product formulation, cutting lab cycles and costs, while a century-old metro layered analytics onto legacy assets to trim energy use by 25 percent.  We also dig into responsible AI as scale accelerates: fairness, explainability, human-in-the-loop checks, and audit trails that satisfy regulators and protect customers.  Finally, we follow the money. Achievers invest more in AI, but the edge comes from allocation—funding data hygiene and cloud migration to unlock dozens of high-value use cases instead of one-off wins. The thread running through it all is orchestration. Strategy without data is theater, models without culture are shelfware, and spend without governance is a lawsuit waiting to happen.  We lay out a practical playbook: choose use cases tied to business goals, build the data backbone, upskill the experts closest to the work, embed MLOps and guardrails, and measure adoption and ROI relentlessly.  If you’re ready to move from experiments to enduring advantage, follow along and if this resonated, subscribe, share with your team, and leave a review so others can find it. Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 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

    15 min
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About

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

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