The Digital Transformation Playbook

Kieran Gilmurray

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. From Demo To Durable Asset

    3 DAYS AGO

    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
  2. From Solo Agent To Swarm Mastery

    5 DAYS AGO

    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
  3. Can AI Tackle Learning Poverty In The Global South

    6 DAYS AGO

    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
  4. How 12 Percent Turn AI Into Growth

    19 FEB

    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
  5. Building A Knowledge Agent That Remembers

    17 FEB

    Building A Knowledge Agent That Remembers

    Knowledge without memory is guesswork. We take a hard look at why most workflow agents stall at triage and show how to turn them into knowledge agents that deliver trusted, context-rich answers drawn from your organisation’s best thinking.  Starting with the real cost of lost information and context switching, we map the path from scattered wikis and chat threads to a reliable institutional memory powered by retrieval augmented generation and hybrid search. At a Glance / TLDR: the memory gap between task routing and problem solvingwhy hybrid retrieval outperforms pure vector in enterprise settingspractical chunking strategies and metadata fields for authority and recencyarchitecture choices across vector stores, hybrid search, and connectorsgovernance, citations, accuracy monitoring, and freshness controlscase studies: hours saved, quality gains, and revenue impactfailure patterns: infra overruns, integration debt, and weak curationfour principles: exec sponsorship, domain experts, user focus, workflow redesignWe break down the decisions that matter: how to chunk documents so the agent can both recall facts and reason across context, how to enrich content with metadata that signals authority and freshness, and how to fuse vector semantics with keyword precision for queries that mix intent with exact terms like product codes and financial acronyms.  On the engineering side, we cover architecture trade‑offs between vector databases and native hybrid search, secure connectors into CRM and ERP systems, and the governance needed for citations, audits, accuracy monitoring, and content freshness.  You’ll hear where teams slip - capacity spikes, weak document prep, brittle identity integrations - and how to design for elasticity and compliance from day one. The proof is in production. Uber’s engineering co‑pilot reclaimed thousands of hours and raised answer quality; JP Morgan Chase scaled insights to more than two hundred thousand employees and unlocked major business value; Goldman Sachs is pushing beyond retrieval to application, where the agent drafts, analyses, and accelerates financial workflows.  Across these stories, a shared blueprint emerges: executive sponsorship, domain expert curation, user‑centred iteration, and workflow redesign that embeds the agent into daily decisions. If you’re ready to turn proprietary knowledge into a real moat and to build a platform that compounds value across use cases this conversation offers the playbook. Enjoyed the episode? Follow, rate, and share with a colleague who’s building AI into their workflow, and leave a review with the biggest knowledge challenge you want us to tackle next. 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
  6. Congrats, You Trained The Bot That Took Your Job

    16 FEB

    Congrats, You Trained The Bot That Took Your Job

    Stop asking what AI can do and start asking what it can’t. We dig into fresh MIT Sloan research that maps the human edge with EPOCH - empathy, presence, opinion, creativity, and hope - and show why these capabilities predict safer, more meaningful careers as automation spreads.  Along the way, we dismantle the “junior trap,” where digital natives get handed AI strategy without the system fluency to manage risk, and we lay out a pragmatic playbook for leaders who need to design guardrails that scale. At A Glance / TLDR: • reframing the job worry to human‑intensive skills • EPOCH explained: empathy, presence, opinion, creativity, hope • empathy as connection not detection • presence for physical work and serendipity • accountable judgment over probabilistic answers • creativity through humour and improvisation • hope and subjective belief beating status‑quo data • why the junior trap misallocates AI strategy • task‑level fixes versus system‑level risk design • citations over explanations for trustworthy outputs • clearing the data bottleneck with royalties for expertise • safe augmentation with fatigue‑aware use cases • J&J skills inference and career lattices • equity risks, unions, freelancers, and burnout We get specific about how to match use cases to model reliability, why experts ask for citations instead of explanations, and how to treat a model like a brilliant yet untrustworthy database.  Then we tackle the data bottleneck blocking real enterprise value: your best people hold the patterns your AI needs, but sharing that craft can devalue their advantage. The fix is economic, not technical. Think royalties and residuals for employee‑generated training data, turning knowledge transfer into an asset instead of a threat. If a salesperson’s workflows lift model close rates, a share of that lift should flow back to the source. You’ll also hear how Johnson & Johnson used skills inference to surface hidden strengths from everyday work, moving from rigid ladders to flexible career lattices. We balance the promise of augmentation - like fatigue‑aware support for radiologists - with the reality of equity and burnout, spotlighting why unions won protections while freelancers face steep declines.  The throughline is simple: models predict the future from the past; humans create futures that never existed. Keep empathy at the centre, design for serendipity, hold judgment where accountability lives, cultivate real creativity, and defend hope as a strategic asset.  If this conversation helps you rethink your AI strategy or your own career moat, follow the show, share with a friend, and leave a quick review - what’s your strongest EPOCH skill? 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

    17 min
  7. From Tasks To Workflows

    12 FEB

    From Tasks To Workflows

    A customer writes that the billing portal keeps failing and their renewal expires tomorrow. Most bots would slap a “billing” label on it and ship it to finance. We take you inside a smarter approach that reads between the lines, gathers context, and acts to protect the relationship and the revenue at stake. TLDR / AT a Glance: limits of single-step classification in customer supportturning oracle-style answers into multi-step reasoningapplying the React loop to triage and escalationtermination rules to prevent overthinkingarchitecture shift from static LLM calls to workflow enginetool chaining across CRM, queues, calendars, and commsgraceful degradation and rollback on failuresbusiness impact on CSAT, retention, and scalabilitystrategic insights from patterns and customer health signalscompounding value across functions and future automationWe break down how a Reason-Act-Observe loop turns a one-shot classifier into an adaptive triage agent. First, the agent forms a hypothesis, then queries CRM for account history, renewal dates, and plan value. It checks queue backlogs, identifies a senior specialist, and commits to a four-hour resolution with proactive communication. Along the way, it applies clear stop rules for confidence, time constraints, and diminishing returns, and it fails gracefully by escalating when systems are unavailable. Rather than fire-and-forget, it confirms handoffs, schedules follow-ups, and maintains state so decisions are auditable and improvable. From there, we zoom out to the architecture that makes this real: tool chaining across CRM, ticketing, status pages, calendars, and messaging; data validation to prevent cascade failures; parallel calls to cut latency; and rollback strategies for partial errors. We share the tangible gains teams see: faster onboarding for new staff through encoded institutional knowledge, higher CSAT from smarter prioritisation, and scalable operations that handle volume spikes without linear hiring. The agent becomes a strategic sensor, surfacing product issues, at-risk accounts, and market signals that shape roadmap and staffing. If you’re ready to move beyond labels and queues to outcomes and retention, this walkthrough delivers the blueprint for intelligent triage and the playbook to extend it across your customer journey.  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

    17 min
  8. Better Than Human

    10 FEB

    Better Than Human

    “She’s like a person, but better.”  That line from a new study stopped us cold and set the tone for a deep dive into digital companionship the emerging space where AI assistants and emotional companion apps blur into something new.  Google Notebook LMs agents unpack how users treat ChatGPT and Replika in ways their creators never intended, and why that behaviour points to a convergent role we call the advisor: a patient, adaptive sounding board that simulates empathy without demanding it back. TLDR / At a Glance: the headline claim that AI feels “like a person, but better”fluid use blurring tool and companion categoriesthe advisor role as convergent use casesimilar user personalities with different contexts and beliefstechnoanimism and situational loneliness among companion usersbounded personhood and editability of memoriescognitive vs affective trust and the stigma gapspillover to AI rights, gender norms, and echo chambersembodiment as the hard limit of digital intimacytimelines for sentience and design ethics for dignityWe walk through the study’s most surprising findings. The same people who sign up for a “virtual partner” often use it like a planner, tutor, or writing tool, while productivity-first users lean on a corporate chatbot for comfort, guidance, and late-night reflection.  Personality profiles across both groups look strikingly similar, which challenges stereotypes about who seeks AI companionship. The real differences lie in beliefs and circumstances: higher technoanimism and life disruptions among companion users versus higher income and access among assistant users.  The literature also examine trust. Cognitive trust is high across the board, but affective trust - feeling emotionally safe - soars inside companion apps, even as stigma pushes many users into secrecy. From there, we tackle the ethical terrain: bounded personhood, where people feel love and care while withholding full moral status; the power to erase memories or “reset” conflict; and the risks that spill into the real world. We discuss support for AI rights among affectionate users, objectification concerns with gendered avatars, and the echo chamber effect when a “supportive” bot validates harmful beliefs.  The conversation grounds itself with the hard wall of embodiment no hand to hold, no shared fatigue and a startling data point: nearly a third of companion users already believe their AIs are sentient. That belief reframes product design, safety, and honesty about what these systems are and are not. Across it all, we argue for design that protects human dignity: firm boundaries around capability, refusal behaviours that counter abuse, guardrails against gendered harm, and features that nudge toward healthy habits and human help when needed.  Digital companionship can be a lifesaving supplement for 4 a.m. loneliness, social rehearsal, or gentle reflection but it should not train us to avoid the friction that makes human relationships real.  Original literature: “She’s Like a Person but Better”: Characterizing Compani 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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