Agentic AI That Actually Works

Are You Buying Efficiency, Transformation, or Strategic Optionality?

AI investment is often treated as one portfolio, creating confusion about value, governance, and returns. This episode explains why leaders need to distinguish efficiency, transformation, and strategic optionality before judging AI performance.  It explores how different AI bets create value across different time horizons. TLDR / At a Glance • Efficiency gains in existing work  • Transformation through workflow redesign  • Optionality as future strategic flexibility  • Category errors in AI business cases  • Portfolio metrics by investment type  • Governance matched to value logic The key takeaway is that strong AI portfolios classify investments first, then apply the right proof standard for each category. Support the show [https://www.buymeacoffee.com/KGilmurray] 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 [https://www.linkedin.com/in/kierangilmurray/] 🦉 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 [https://tinyurl.com/MyBooksOnAmazonUK]

The Human Sandwich Versus The Slop Machine

A company lets AI write most of its code, generate its graphics, and handle nearly half of its customer support emails from start to finish. You’d expect redundancies. Instead, they fire nobody and end up with more human work than ever. That tension is the clue to what AI automation is really doing to modern jobs, knowledge work, and the future of work. TL;DR / At A Glance: * • agent employees with names and roles, from Slack monitoring to autonomous customer support * why automation shifts jobs upwards into oversight, routing, boundaries and edge cases * the human sandwich model of co-working, framing then generating then judging * the hidden maintenance burden, drift, integrations breaking and compute token costs * visible residue of human competence, abundance, and the flood of generic work * agent versus agency, why goals still need a framer and a reason to care * the closing challenge, finding the unframeable part of your work and leaning into it Google AI Agents walk through a concrete case study from Dan Schipper’s “After Automation” and unpack two modes of human and AI collaboration.  First are agent employees: named, role-based systems living inside tools like Slack and customer support, drafting proposals, collecting ideas, and even closing tickets autonomously.  Second is co-working in a shared document, the “human sandwich”: we set the frame, AI does the heavy lifting, then we judge, correct, and extend the output. From there we get honest about the hidden costs: maintenance, drift, brittle integrations, and compute token spend that can turn “simple automation” into a complex system that constantly needs care.  Then we zoom out to the economics of generative AI.  When cheap competence floods the market, we get slop: perfectly fine, deeply generic work. The result is a new premium on difference, live context, and human judgement.  We also tackle benchmark hype and “chart psychosis”, why a benchmark is a frozen frame, and why the most valuable skill is often choosing the frame in the first place. If you’ve been feeling that tightness in your chest about AI and job security, this is a practical way to think clearly and act.  Subscribe, share with a colleague, and leave a review with your answer: what is the unframeable part of your work that you should lean into next? Support the show [https://www.buymeacoffee.com/KGilmurray] 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 [https://www.linkedin.com/in/kierangilmurray/] 🦉 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 [https://tinyurl.com/MyBooksOnAmazonUK]

The 2026 CEO Blueprint For Rewiring Business

The “blueprint” for modern business is changing while you’re still trying to build from it, and CEOs are treating 2026 as the year that decides who keeps up and who falls behind.  Google voice agents unpack the latest 2026 IBM Institute for Business Value CEO study, conducted with Oxford Economics, and translate its biggest signals into plain language you can use in strategy meetings, operating reviews, and career planning. TL;DR / At A Glance * the 2026 IBM Institute for Business Value CEO study and why it matters at global scale * AI shifting from productivity tool to operating model rewiring * moving from siloed leadership to real-time “basketball team” execution * decision latency changing from information bottleneck to authority bottleneck * productive friction enabled by shared dashboards and predefined decision rights * the rise of the Chief AI Officer and the growing influence of the CHRO * the AI flywheel: reinvesting savings to compound automation and speed We start at the top: why AI isn’t a productivity layer any more, but a rewiring of how decisions get made. The old relay-race org chart breaks when intelligence is real-time, so authority moves closer to the work, new roles like the Chief AI Officer surge, and “productive friction” replaces pointless spreadsheet battles. Then we follow the AI flywheel as leaders reinvest early savings into deeper automation, aiming for a world where AI executes a huge share of routine operational decisions while humans design the rules, exceptions, and ethical guardrails. From there, we dig into competitive advantage: the shift from generic foundation models to a hybrid AI strategy that blends LLMs with smaller specialised models trained on proprietary data, plus the idea of AI sovereignty and why it matters for security, governance, and differentiation. We also explore what reskilling really means in an AI first workplace, including systems thinking and the reality of AI as a day-to-day workflow orchestrator. Finally, we lift our eyes to the quantum horizon, where ecosystem partnerships and clean, portable data architecture become the real preparation. If this helps you think more clearly about where business is heading, subscribe, share it with a colleague, and leave a review so more people can find the conversation. Source: IBM Rewriting the C-Suite, The Fast Track to 2030 Support the show [https://www.buymeacoffee.com/KGilmurray] 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 [https://www.linkedin.com/in/kierangilmurray/] 🦉 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 [https://tinyurl.com/MyBooksOnAmazonUK]

The Next AI Failure Will Be a Coordination Failure

AI failure is shifting from isolated model mistakes to the way organisations coordinate work around them. As agentic systems cross tools, teams, and customer journeys, weak ownership and unclear handoffs can turn small errors into serious incidents. This episode explores why workflow governance now matters as much as model performance. TLDR / At a Glance • Coordination risk in AI workflows  • Agentic AI and cross-system exposure  • Ownership gaps and escalation failures  • Handoff design as a control point  • Workflow inventories over model registers  • Metrics for operational AI governance The key takeaway is that scalable AI depends on strong coordination systems around every workflow. Support the show [https://www.buymeacoffee.com/KGilmurray] 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 [https://www.linkedin.com/in/kierangilmurray/] 🦉 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 [https://tinyurl.com/MyBooksOnAmazonUK]

How to Redesign a Workflow for AI Without Automating the Mess

AI can speed up individual tasks, but faster activity does not always create better business performance. This episode examines why meaningful AI value comes from redesigning workflows around outcomes, constraints, decisions, and accountability. It explores how leaders can avoid automating inefficient processes. TLDR/ At a Glance • Workflow redesign over task acceleration • Cycle time, handoffs, and bottlenecks • Human judgement and AI role clarity • Autonomy matched to risk • Metrics beyond adoption and usage • Governance embedded in operations If AI is making everything faster, why does work still take forever?  The uncomfortable answer is that most organisations use AI to accelerate tasks inside the same broken process, then wonder why cycle time, quality, and customer outcomes barely change. We unpack the difference between task productivity and workflow performance, and why “faster drafting” can still leave you waiting days for reviews, approvals, escalations, and missing information to be chased down.   We walk through a practical approach to enterprise AI transformation that starts with the outcome rather than the tool: define the business result, map the workflow from trigger to completion, and find the real constraint. From there, AI becomes a design decision: place it where it reduces waiting, improves first-pass quality, lowers coordination overhead, and shortens the path to a decision. We also tackle “automating the mess”, the common failure mode where AI increases upstream output and quietly creates more downstream rework and exception handling.   Autonomy gets special attention because agentic AI is not automatically more valuable. We break down when AI should assist, when it should recommend, and when it can take bounded action with the right controls. To keep things grounded, we share the workflow metrics that actually prove ROI: cycle time, throughput, quality, rework, exceptions, cost per case, stakeholder outcomes, and human review effort, plus what good governance looks like when it is built into everyday work rather than left in a policy document. If you found this useful, subscribe, share it with a colleague redesigning a process, and leave a review with the workflow you would fix first. The key takeaway is that AI creates durable value when organisations improve the flow of work, not simply the speed of isolated tasks. Read the written version on my LinkedIn page  [https://www.linkedin.com/in/kierangilmurray/] Support the show [https://www.buymeacoffee.com/KGilmurray] 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 [https://www.linkedin.com/in/kierangilmurray/] 🦉 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 [https://tinyurl.com/MyBooksOnAmazonUK]