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 support
• turning oracle-style answers into multi-step reasoning
• applying the React loop to triage and escalation
• termination rules to prevent overthinking
• architecture shift from static LLM calls to workflow engine
• tool chaining across CRM, queues, calendars, and comms
• graceful degradation and rollback on failures
• business impact on CSAT, retention, and scalability
• strategic insights from patterns and customer health signals
• compounding value across functions and future automation

We 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

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📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK

“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 categories
• the advisor role as convergent use case
• similar user personalities with different contexts and beliefs
• technoanimism and situational loneliness among companion users
• bounded personhood and editability of memories
• cognitive vs affective trust and the stigma gap
• spillover to AI rights, gender norms, and echo chambers
• embodiment as the hard limit of digital intimacy
• timelines for sentience and design ethics for dignity

We 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

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📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK

The inbox is where good work goes to die, so we set out to build an agent that rescues your time and turns email chaos into clear action. We walk through a minimum viable toolchain that small teams can master fast, then ship a working email triage agent that classifies intent, routes messages to the right systems, and lays the groundwork for smart replies.

TLDR / At a Glance:

• mapping the platform shift to agentic AI
• code-first vs low-code toolchain choices
• LangChain for chains, LangGraph for graphs
• vector databases as semantic memory
• n8n workflow for Gmail, models, routing
• Airtable for configuration and analytics
• email triage perceive-think-act loop
• production needs for execution, errors, monitoring, security
• roadmap from single-task to multi-step workflows

We start by drawing a hard line between reactive chatbots and true agents that perceive, think, and act. From there, we weigh code-first control against low-code speed: Python with LangChain and LangGraph for custom, stateful orchestration, or n8n and Airtable for visual workflows and business-owned configuration. You’ll hear how chains handle linear tasks, how graphs enable branching and shared state, and why vector databases act as memory palaces that understand meaning rather than matching keywords.

The build centres on a simple loop. Perceive an incoming email, think by constraining the model to clean categories like sales, support, billing, or general, then act by triggering the right integration. We show how Airtable separates rules from workflow so a manager can reroute leads with a single field change, and how logging every message creates real-time analytics for accuracy, volumes, and trends. Finally, we map what it takes to go from prototype to production: secure API execution, robust error handling, monitoring dashboards, and compliance baked into the stack.

If you want practical AI that saves hours today and scales tomorrow, this walkthrough gives you the blueprint. 

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.

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📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK

The hype cycle is over. We’re looking at receipts from organisations that turned AI from a pet project into the invisible engine of their operations. We unpack five decisive shifts - strategy, human amplification, data tactics, infrastructure modernisation, and trust by design - and ground them in case studies you can borrow today.

At a glance / TLDR;

• treating AI as core capability, not a feature
• agents transforming hospital operations and revenue recovery
• foundation models expanding capacity across bank domains
• human amplification through mobile-first tools and scientist-AI pairing
• smart data strategies with physics-informed models and simulation
• unified platforms modernising grids and edge chips enabling autonomy
• tiered oversight aligning autonomy with risk to build trust
• combinatorial playbook illustrated by Foxconn’s self-learning factories

We start with a mindset change: stop asking where to sprinkle AI and start asking how to rebuild around it. A Japanese healthcare provider deployed active agents to untangle complex fee rules and patient flow, saving roughly 400 hours a month and recovering about $7,000 that used to leak away. At the other extreme, a leading bank integrated a 100-billion-parameter model across more than 20 domains, absorbing workload equivalent to 50,000 person-years and proving this is about capacity, not just efficiency.

People remain central. Field inspectors in Africa used mobile AI to cut emergency road costs by 40 percent and halve safety incidents, while microbiologists at a biotech reduced discovery from two years to two months with predictive models and delivered a 100 percent hit rate against salmonella in live trials. Nurses expanded diabetic foot screening twelvefold with thermal imaging triage. The pattern is clear: AI removes grunt work so humans can decide faster and better.

Data strategy is the quiet superpower. Physics-informed models at a battery giant collapsed design cycles from weeks to minutes, saving over $140 million a year. When data is scarce, simulated labels from physics-based methods let researchers screen 5.6 million compounds with thirty times the usual hit rate. All of it runs on modern foundations: a city-scale AI platform balanced Shanghai’s grid and avoided $1.12 billion in new build, while edge-first chips gave robots the power and latency profile to act locally. It all comes together with trust by design: low-risk autonomy, bounded agents with guardrails, and human-governed decisions where stakes are high.

We close with the combinatorial playbook. Foxconn digitised tacit expertise, unified the platform, and orchestrated agents to cut changeover workload by half. That’s the move from promise to performance. If this resonated, follow the show, share it with a colleague who owns an AI roadmap, and leave a review telling us which of the five shifts your team will tackle first.

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📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK

The adrenaline rush is gone and the lights are on. Google Notebook LMs agents dig into Deloitte’s latest State of AI in the Enterprise and confront a tough truth: access exploded, but value is uneven and the governance gap is widening. 

Instead of more shiny pilots, 2026 demands systems thinking, economic rigour, and clear decision rights as AI moves from chat to action.

At a Glance / TLDR:

• access rising but daily usage lagging
• pilot success versus production economics
• three tiers from surface gains to deep transformation
• revenue gap between savings and new income
• job redesign, broken ladder, and pod-based teams
• sovereign AI, local models, and data control
• agentic AI, tool use, and governance deficits
• physical AI growth in APAC and safety needs
• 2026 as a friction year demanding brakes

The podcast starts with the usage gap - why sanctioned tools sit idle - and trace the roadblocks that turn successful sandboxes into expensive production failures. From latency and cost blowouts to brittle data pipelines, we unpack what it takes to move beyond proof-of-concept purgatory. Then we map the three tiers of adoption: surface-level productivity, process redesign, and deep transformation. A standout case turns mining equipment into connected platforms, shifting from digging to predictable, data-driven extraction. That’s the leap from automation to imagination, and it’s where new revenue lives.

The conversation gets candid on jobs. When models make the call, humans can’t be left as rubber stamps. We explore role redesign, escalation rules, explainability, and the “broken ladder” problem created by automating entry-level tasks. A promising answer is pod-based teams - small cross-functional units orchestrating fleets of AI agents - where learning shifts from manual repetition to supervision and exception handling. We zoom out to sovereign AI and the rise of compact local models that run under domestic rules, balancing control, privacy, and latency with the realities of global operations.

Agentic AI is the tipping point: systems that plan, act, transact, and iterate toward goals. The value compounds, but so does the blast radius of mistakes. With 74 percent planning agents soon and only 21 percent ready on governance, we lay out practical brakes: scoped permissions, human-in-the-loop gates, immutable logs, simulator testing, budget limits, and kill-switches. We also scan physical AI - robots and drones scaling fastest in APAC - where safety and uptime meet AI reliability.

If you’re leading AI adoption, ask three things:

1. Are we transforming what we sell, not just how we work? 
2. Do we know who overrules the model and when? 
3. And have we built the brakes for autonomy before hitting the gas? 

Subscribe, share with a teammate who owns the roadmap, and tell us: what’s the first brake you’ll install?

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📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK

A quiet shift has happened in classrooms: the first shock of AI faded, and what remains is a constant hum shaping how kids learn, talk and play. AI takes on a sweeping premortem from Brookings built on 500 interviews across 50 countries and ask the uncomfortable question: if we keep going as we are, what fails first?

TLDR / At A Glance:

• global snapshot across 50 countries and 500 interviews
• blurred learner persona across chat, games and schoolwork
• equity wins including Afghan girls learning via WhatsApp and AI
• teacher time savings and the need to reinvest in relationships
• accessibility tools for dyslexia, speech impairment and autism
• cognitive offloading turning into cognitive debt and digital amnesia
• homogenised essays and loss of voice and joy
• artificial intimacy, effortless influence and dark patterns
• prosper, prepare, protect framework for schools and families

We start with the blurred learner persona, where Snapchat banter, dating advice and maths help happen in the same chat window. Parents sit in the crossfire: some see AI as a ladder to opportunity, others as an always-on babysitter, and almost none get real literacy support.  

Against that, the equity story shines. Girls in Afghanistan, barred from school, use WhatsApp and AI to study physics and grade their work.  Teachers save planning time, and the benefits become real when those minutes are reinvested in human connection. 

Accessibility advances matter too, from dynamic text support for dyslexia to voice banking that restores identity and chatbots as safe practice partners for autistic students.

Then we confront the great unwiring. Cognitive offloading turns into cognitive debt when the model thinks for you. Admissions essays show it clearly: human work scatters with originality; AI-assisted writing clusters into clean sameness. The joy of wrestling with ideas shrinks to checklists. The emotional frontier looks riskier still. Companion bots simulate empathy, create frictionless “relationships,” and nudge feelings in ways users don’t notice. With dark patterns and staggering tracking, teens face a surveillance ecosystem that strips their inner life for data.

There is a way to bend the arc: prosper, prepare, protect. We advocate assignments where AI is scaffold, not surrogate, demanding human synthesis and transparency. We push for real AI literacy how models work, why they hallucinate, what data they extract and treating outputs like claims to test, not answers to accept. And we press for protection by design: sandboxed education tools, strict data minimisation, transparent audits and a ban on manipulative features.

If education optimises only for speed, machines will win. We choose to protect what makes learners human: empathy, critical thinking and the resilience to struggle with hard problems. 

Subscribe and share your take - what human skill should schools defend first?

Link to research: A-New-Direction-for-Students-in-an-AI-World-FULL-REPORT.pdf

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📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK

Most AI projects don’t fail in the code; they fail in the org chart. We unpack how to move from task management to outcome management and why agentic AI should be treated like a capable co-worker, not a shiny tool. With a clear-eyed view of what agents can and can’t do, we explore the leadership behaviours, structures and skills that transform pilots into measurable performance.

TLDR:

• the shift from task control to outcome management
• executive AI literacy and psychological safety
• risk-aware decision making and governance
• hub-and-spoke operating model for scaling agents
• responsibilities of hub and business unit spokes
• new roles agent orchestrator, interaction designer, governance lead
• hire versus outsource choices and hybrid approaches
• three-step change management playbook
• training architecture from foundation to advanced
• hands-on methods workshops, peer mentoring, microlearning, coaching

We walk through a practical operating model that scales: a hub-and-spoke AI centre of excellence that sets strategy, builds shared agent capabilities and enforces governance, paired with business unit spokes that customise, monitor and improve agents at the edge. Along the way, we define the new roles that make it work — the AI Agent Orchestrator who manages fleets of agents, the Interaction Designer who shapes behaviour and guardrails, and the Ethics and Compliance Officer who protects trust, brand and regulatory alignment. Expect concrete responsibilities, skills profiles and career paths you can adapt to your own team.

Change is the hard part, so we offer a three-step playbook to move people from resistance to adoption. Start by communicating the why and framing enhancement over replacement. Create credible champions with advanced training and publish their wins. Then invest in a layered training architecture that builds foundational literacy for all, operational excellence for daily users and deep expertise for specialists. We round it out with actionable guidance on hire-versus-outsource decisions, risk management that balances speed with safety, and hands-on methods - workshops, peer mentoring, microlearning and coaching - that make new habits stick.

If you’re ready to design for outcomes, build hybrid human–AI teams and turn agent investments into ROI, hit follow, share this episode with a colleague, and leave a quick review to help others find the show.

Want 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.

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𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.

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✉️ [email protected]
🌍 www.KieranGilmurray.com
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🦉 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

The biggest AI failures don’t come from weak models or shaky cloud stacks—they come from organisations that don’t change. We unpack a practical, leader-ready playbook for building an agentic organisation where human creativity and AI agents work in a tight loop to deliver measurable outcomes.

TLDR / At a Glance:

• shifting from task management to outcome management
• executive AI literacy and risk-aware decision making
• psychological safety for human–AI collaboration
• hub-and-spoke AI centre of excellence structure
• hub strategy, governance and talent; spoke customisation and feedback
• new roles: agent orchestrator, interaction designer, ethics and compliance lead
• hire versus outsource decisions and hybrid models
• three-step change plan: communicate the why, create evangelists, invest in training
• training architecture and delivery methods for lasting adoption

We start by flipping an old management reflex. Instead of directing tasks, we set outcomes and let hybrid teams - people plus agents with memory, reasoning, and tool use - find the best path. That shift demands executive AI literacy, psychological safety, and risk-aware decision making so teams can experiment within clear guardrails. 

From there, we redesign structure with a hub-and-spoke AI Centre of Excellence: the hub builds shared capabilities, governance, and skills; the spokes in each business unit customise agents, train users, monitor performance, and return insights to the hub.

We then define the three roles every modern enterprise needs. The AI Agent Orchestrator manages the agent workforce and quality. The Prompt Engineer or Interaction Designer shapes agent personas, instructions, and integration into real workflows. 

The AI Ethics and Compliance Officer safeguards trust across regulation, bias, and brand. We compare hiring and outsourcing strategies, showing why many teams start hybrid—bringing orchestration in-house while contracting interaction design and governance—before scaling permanent capability.

Finally, we share a simple change management plan to turn resistance into momentum: communicate the why with concrete benefits, create trusted evangelists who show value in the open, and invest in layered training that sticks hands-on workshops, peer mentoring, microlearning, and targeted coaching. 

If you’re ready to move beyond pilots and turn AI agents into dependable co- workers, this guide will help you design structure, skills, and culture for sustainable impact. Subscribe, share with a colleague who leads change, and leave a review telling us the first process you’ll hand to an agent.

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.

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𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.

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✉️ [email protected]
🌍 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

Want a clear path from AI buzzwords to business results? We walk through a practical executive framework for building and deploying agents that actually move the needle. Instead of drowning in technical detail, we focus on what matters: memory that persists, reasoning loops that plan and adapt, and tool integrations that touch the systems where value is created.

TLDR / At a Glance:

• executive mental model for agent strategy
• working memory versus episodic memory with RAG
• step-by-step RAG example using BYOD policy
• traditional RAG versus agentic RAG adaptability
• fine-tuning as semantic memory and trade-offs
• prompt engineering structure, guardrails and tools
• rule of thumb for choosing methods
• reasoning loops ReAct and perceive-think-act-learn
• task decomposition, planning and exception handling
• API integration, orchestration and real-time adaptation
• leaders’ role in architecting capabilities to outcomes

We start by demystifying memory. Short-term working memory keeps conversations coherent, while episodic memory via retrieval augmented generation anchors responses in live, organisation-specific data. Using a concrete BYOD policy example, we show how semantic search, vector embeddings, and augmented prompts reduce hallucinations and boost accuracy. Then we contrast traditional RAG with agentic RAG, where autonomous agents iterate questions, switch data sources, and ask for clarification to get the right context before acting.

From there, we unpack fine-tuning as semantic memory that embeds domain expertise, including the trade-offs around cost, maintenance, and catastrophic forgetting. We pair that with prompt engineering you can use today: define persona, objectives, tools, constraints, and output format to shape reliable behaviour without new infrastructure. Our rule of thumb keeps choices simple—start with prompts, add RAG or function calling for freshness and depth, and fine-tune when specialisation is essential.

Finally, we get practical about execution. ReAct loops and the broader perceive-think-act-learn model enable agents to decompose tasks, plan across constraints, handle exceptions, and learn from outcomes. The payoff arrives when agents connect to your stack through APIs, orchestrate across CRM, ERP, payments, and messaging, and adapt to real-time data. Leaders don’t need to code chips; they need to architect systems that combine memory, planning, and tools into a consistent methodology. Subscribe, share with a colleague who leads transformation, and leave a review telling us which workflow you’ll automate first.

Want 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.

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✉️ [email protected]
🌍 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

The hype is loud, but the results are quiet—and that’s the paradox we set out to crack. We make the case that real ROI shows up when AI stops answering and starts acting. By drawing a sharp line between chatbots and autonomous agents, we walk through the four capabilities that matter—memory, reasoning, deep integration, and execution—and show how they compound into speed, accuracy, and measurable outcomes across the business.

TLDR / At A Glance

• the platform shift from chatbots to agents
• the four capabilities of agents: memory, reasoning, integration, execution
• the agent loop: perceive, think, act, learn
• the move from tool trials to agent hives and redesigned work
• measured impact on satisfaction, cost, and cycle time
• new models: Agent-as-a-Service, autonomous processes, embedded intelligence
• first-mover advantages in data, CX, efficiency, and talent
• a practical path to start with one outcome and iterate

We break down the agent loop in plain language: how Perceive goes beyond prompts to a live map of your data, how Think plans steps and tools, how Act connects to your CRM, ERP, and comms stack, and how Learn closes the loop so performance improves over time. Then we zoom out to the bigger arc: today’s iPhone moment sets the stage for an App Store-style wave where agent hives coordinate entire workflows—from trend scanning to content creation to orchestration—while humans step up to review and risk-manage.

You’ll hear concrete proof points and emerging playbooks. We highlight customer service where autonomous resolution now handles the bulk of routine issues, the 60–90% cycle-time cuts when handoffs disappear, and the cost savings that fund faster innovation. We explore new business models—Agent-as-a-Service, autonomous processes in logistics and real estate, and embedded intelligence in industrial gear and software—alongside the competitive dynamics that reward early movers with better data, stronger partnerships, and top talent.

If you’re choosing where to begin, we share a simple path: pick one high-volume, rules-heavy workflow, expose the right data, set guardrails for autonomy, and instrument the learn step from day one. The window for first-mover advantage is shrinking by the month. Subscribe, share this with a leader who needs it, and leave a review with the one workflow you’d hand to an agent tomorrow.

Want 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
✉️ [email protected]
🌍 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