Behind the Scenes at PegaWorld: A Conversation with Kara Manton

Kieran Gilmurray x Matt Healy: The Reality of Agentic AI

AI is moving fast, but enterprise leaders are starting to ask a sharper question: are we getting value for the money we’re spending? Matt Healy from Pega joins us to unpack what “agentic transformation” looks like when it has to survive real-world constraints like compliance, security, and customer-facing reliability, not just a slick prototype. TL;DR: * extending AI-driven development into the platform with coding agents such as GitHub Copilot, Codex, and Cloud Code * deploying agents that run predictably against rules, regulations, and compliance needs * shifting from token-based consumption to outcome-based agentic pricing for predictable ROI * why vendor pricing changes can flip an AI use case from profit to loss * using AI to analyse legacy systems, translate code into natural language, and guide modernisation * combining AWS legacy analysis with Blueprint to support mainframe exit and reimagined journeys * building enterprise-ready apps that are explainable, secure, scalable, and consistently developed We talk about AI-driven development and the growing role of coding agents in everyday work, including tools such as GitHub Copilot, Codex, and Cloud Code. Speed is great, but Matt explains why it can also create apps that aren’t explainable, hide vulnerabilities, and struggle to scale. The goal is to keep the acceleration while making the output enterprise-ready: transparent, deployable at massive scale, compliant, secure, and built consistently. Cost control is the other make-or-break topic. Token-based pricing sounds simple until reasoning agents start consuming unpredictably and vendors change their models. Matt lays out an outcome-based approach to agentic pricing that focuses on work done and value delivered, aiming for predictable costs and predictable ROI so promising AI use cases don’t suddenly turn unprofitable. We also dig into Pega Blueprint’s progress on legacy modernisation, including how AWS-powered analysis of legacy languages like COBOL can produce natural language understanding that feeds transformation work. If you care about mainframe exit, cloud modernisation, and reimagining customer journeys rather than lift-and-shift, you’ll find plenty to take away.  If you found this useful, subscribe, share it with a colleague, and leave a review so more builders and leaders can find the show. #PegaPartner 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]

Behind the Scenes at PegaWorld: A Conversation with Kara Manton

Legacy systems do not fail because teams lack ambition. They fail because nobody has the time to untangle years of code, edge cases and hidden business logic. We sit down with Kara Manton, business director in Pega’s product engineering function, to unpack the biggest PegaWorld announcements aimed at changing that reality, starting with why Pega Infinity 26 is being called one of the best releases in a decade.  TL;DR: * Infinity 26 as a major step forward for AI powered workflow automation * Blueprint AI inside Infinity Studio and an AI assistant that builds rules behind the scenes * Calling Pega workflows from different AI tools while keeping execution predictable * AWS Transform plus Blueprint to modernise legacy code into production apps in three months * Designing business rules and user experience earlier to cut rework later * No token charging and a shift towards outcomes based pricing We talk through what it looks like when AI is designed to strengthen workflow automation rather than replace it. Kara explains how Pega Blueprint has evolved from an early idea into a deeper application design experience where you can shape process flows, business rules and user experience before you build.  We also dig into Infinity Studio with its built-in AI assistant, where you can chat and have the system generate Pega rules behind the scenes, opening the door for more people to participate in creating workflow applications.  The conversation turns to two big enterprise concerns: modernisation speed and AI cost. Kara highlights the on-stage AWS Transform announcement, describing how AWS Transform plus the power of Blueprint can take organisations from a legacy code base to a production app in three months.  We also cover Pega’s decision not to charge for tokens, focusing instead on outcomes and predictable cost in a world where tokenomics and model changes can feel chaotic. If you care about practical, governed AI, agentic workflows and faster legacy transformation, this one is for you.  Subscribe, share with your team, and leave a review with the workflow problem you want to modernise next. #PegaPartner 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 Powerful Strategic Subtraction Test for Smarter Decisions

AI can accelerate work, but it can also multiply clutter when obsolete processes stay in place. This episode examines strategic subtraction as a leadership discipline for improving AI value, capacity, and operating focus. It explores how leaders decide what to remove, redesign, protect, or simplify.  TLDR / At a Glance • Strategic subtraction discipline  • Automation before redesign risk  • Workflow clutter and decision friction  • The VITALS subtraction test  • Capacity release and governance focus  • Protecting trust, compliance, and learning AI can make your organisation faster while quietly making it worse. If we use copilots and agents to accelerate reports nobody reads, approvals nobody trusts, and meetings that never end in a decision, we are not transforming anything, we are scaling clutter. We take on the most common starting point for AI transformation and argue it is strategically dangerous: asking what can be automated. The better first question is tougher and far more useful: should this work still exist in its current form? From there, we explore why AI shifts the economics of production but does not fix the real constraint in many businesses, which is attention, coordination, and the ability to absorb information without drowning in it. To make subtraction practical, we walk through a simple leadership tool: the Strategic Subtraction Test, built around six prompts on value, interference, duplication, assurance risk, liberation of capacity, and strategic fit. You will hear how to apply it to real work objects such as meeting series, dashboards, approval steps, governance forums, workflows, and tools, plus concrete examples of actions like simplifying low-risk approvals, consolidating overlapping governance, substituting decks with live views, and hiding specialist reports from default circulation. We also get specific about what not to cut. Some work that looks slow is actually trust infrastructure: legal controls, cyber checks, privacy safeguards, incident reviews, escalation routes, and learning loops. If we remove those without redesign, we can damage compliance, resilience, and judgement. If you want AI strategy that delivers capacity release rather than work intensification, subscribe, share this with a leader who owns “AI rollout”, and leave a review telling us what work you would stop carrying forward. The key takeaway is that effective AI transformation depends on removing low value work before accelerating the system around it. Support the 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]

AI’s Impact on Junior Productivity and Skill Development

AI is dramatically reshaping how junior professionals learn and perform at work. New evidence shows novices reaching competency in a fraction of the time, with significant implications for productivity and talent development. This episode explores how AI changes learning mechanics, performance outcomes, and risk management for junior talent. TLDR / At a Glance • Accelerated time to competence  • Disproportionate gains for juniors  • AI-driven feedback and scaffolding  • Overreliance and accuracy risks  • Enterprise access versus shadow tools  • Leadership guardrails and training AI can compress years of learning into months, but only when paired with structured oversight, calibration, and secure implementation. Juniors reaching veteran-level productivity in a fraction of the time should make every leader curious and a little nervous. We dig into what recent evidence says about AI copilots, coding assistants, and AI tutors, and why the biggest performance gains consistently appear in the least experienced employees. When AI surfaces the right information at the right moment, it doesn’t just speed up tasks, it rewires the day-to-day learning loop. We walk through the mechanisms behind the jump in output and quality: tighter feedback cycles, just-in-time knowledge retrieval, and scaffolding that handles routine work so juniors can focus on judgement. But speed has a shadow side. When teams treat confident AI output as truth, accuracy can fall on complex tasks, and juniors can mistake AI fluency for genuine mastery. That “illusion of competence” becomes a long-term capability risk, not just a short-term mistake. We also tackle the growing policy divide. Organisations that provide secure enterprise AI accelerate development safely, while blanket bans often push people into shadow AI tools, raising data privacy, compliance, and IP risks. Our practical takeaway is straightforward: give safe access early, train for prompting and verification, keep peer review, set clear guardrails, and measure more than productivity by tracking how often people verify and how they perform without AI. If you found this useful, subscribe, share it with a manager or mentor, and leave a review. What guardrail would you put in place first? Support the 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]

AI Fluency Is Not What Most Organisations Think It Is

Many organisations mistake frequent AI tool use for genuine AI fluency. This episode examines why visible activity often masks shallow capability, fragmented workflows, and inconsistent business value. It explores how leaders can move AI from experimentation into structured execution. TLDR / At a Glance • Usage versus fluency  • Fragmented adoption patterns  • Workflow integration  • Repeatable AI practices  • Behaviour and judgement  • Operating standards for AI The key takeaway is that real AI fluency emerges when AI becomes embedded in how work is designed, delivered, measured, and improved. 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]