Chatting GPT

Every company has a CFO. Almost none has anyone whose job is time.Dr Atif Ansar has spent fifteen years at Oxford measuring what happens to the world's biggest projects between the promise and the ribbon cutting. Dams that double their budget. Rail lines that spend four years building a schedule and open four years late. And an uncomfortable finding: the countries famous for building well suffer the same overruns as everyone else.His argument is that we have built an entire profession around controlling cost while leaving time almost ungoverned. There is no double-entry accounting for time. Nobody invoices for it. Yet time is the scarcer commodity, and it is far easier to measure than money.He talks about where AI genuinely changes this and where it is still a slide in a pitch deck, why he thinks contracts should guarantee a maximum time rather than a maximum price, and what that would do to professional indemnity.Plus a question from an architect in Dublin that almost nobody in the industry wants to answer out loud.Guest BioDr Atif Ansar is co-founder and executive chairman of Foresight, an Oxford-based platform that uses AI to forecast and accelerate delivery on major capital projects. He is also an academic at the University of Oxford's Saïd Business School, where he has worked since 2006. For fifteen years he and his colleagues have studied why megaprojects, anything costing roughly a billion euros or more, come in late and over budget.Show NotesThe most expensive belief in the built environment: that your estimate at final investment decision is what you will actually payHow the Oxford team measures overruns, using approved budgets at the start and national audit reports at the endWhy dams double their budgets, rail runs about thirty per cent over, and the Olympics almost never run lateWhy being good at building does not save you, from Berlin Airport to Stuttgart stationTactical AI, which is everywhere, against strategic AI, which takes executive courageThe Hong Kong rail line that spent four years building a schedule and opened four years lateThink slow, act fast, and why organisations that intend to think slowly still fail at itThe chief time officer: no double-entry accounting for time, no invoicing for it, no governance around itGuaranteed maximum time contracts instead of guaranteed maximum price, and what that does to professional indemnityA Dublin architect's question: if AI halves the hours, does the fee drop?The semiconductor fab leader who stopped design at 30, 60 and 90 per cent, and what AI would change about itWhere to start on Monday: find your time data, index it weekly, and take small variances seriouslyGuest LinksDr Atif Ansar on LinkedIn: https://uk.linkedin.com/in/dr-atif-ansar-940bab1Foresight: https://www.foresight.worksAtif Ansar at Oxford Saïd Business School: https://www.sbs.ox.ac.uk/about-us/people/atif-ansarResources MentionedDaniel Kahneman, Nobel Prize in Economic Sciences 2002: https://www.nobelprize.org/prizes/economic-sciences/2002/kahneman/facts/Bent Flyvbjerg, How Big Things Get Done, where think slow, act fast is set out: https://sites.prh.com/how-big-things-get-done-bookKathleen Eisenhardt, Stanford, on information intensity and velocity: https://profiles.stanford.edu/kathleen-eisenhardtClayton Christensen, Harvard Business School: https://www.hbs.edu/news/releases/Pages/clayton-christensen-obituary.aspxUK National Audit Office: https://www.nao.org.ukOracle Primavera P6: https://www.oracle.com/construction-engineering/primavera-p6/Toyota Production System, covering the and on cord: https://global.toyota/en/company/vision-and-philosophy/production-system/index.html

Handing the AI programme to the CIO is the obvious move, and Stephen Redmond thinks it is the wrong one. AI is more than a technology rollout. It changes decision rights, it crosses every function from supply chain to sales, and it does not give the same answer twice, none of which sits comfortably inside IT.Stephen founded Straitéis AI and has written two books on AI leadership. He makes the case for a Chief AI Officer reporting to the CEO, and explains why a fractional appointment often beats a permanent hire.We get into what that person should tackle first, the three skills the job needs, why the chief data officer matters more once your own documents are in play, and what paving a cow path has to do with any of it.Stephen is only the second guest this show has had back.SHOW NOTES:About StephenStephen on LinkedInHis books: Both titlesAI Bowling Bumpers, on governance, was still in final edit at recordingWhy giving AI to the technology lead is the natural mistakeThe three reasons AI does not belong in ITWhat a chief data officer actually does · The case for a chief AI officer ·Why fractional often beats a permanent hireThe first task is working out what the tasks arePaving a cow path · Why the outsider often sees it betterThe three skills a fractional AI leader needsHow the roles sit togetherThree books in under a yearThe EU AI Act, and why enforcement is not only a big-company problemResearch cite: BCG AI Radar 2026Regulation : EU AI Act, Regulation (EU) 2024/1689Transparency obligations discussed are Article 50, in effect since 2 August 2026Webinar, Thursday 17 September, lunchtime, 45 min : RegisterAI Institute

What actually happens when your team uses AI to write a blog post, a tender or a report, and does the law now care? Alan Thornton, founder of the AI governance platform Tipcan Solutions, joins Maryrose Lyons to make the EU AI Act genuinely usable for built environment professionals.Alan walks us through the the latest requirement in EU AI Act compliance that is labelling. If you're using AI and sharing certain types of text content now you must label it, but not all content. A general design blog is fine. A glowing piece about a local councillor is not, unless a named human takes responsibility for it. That idea, that every firm must have real accountability runs through the whole conversation.From there: shadow AI and where the risk really hides, how to properly inventory your AI use cases, and the fines that now apply, including a sneaky second one for handing a regulator incomplete information in a panic. Alan and Maryrose also get into what 'high risk' means once CE marks and national sandboxes enter the picture, and why Ireland's oddly fragmented approach to enforcement is worth watching.Practical, occasionally very funny, and refreshingly free of scaremongering.Show notes:Shadow AI, and why the biggest risk is often the staff member you least suspectThe four risk levels, and why most firms sit in the "limited risk" transparency bandThe public-interest test: one blog post needs an AI label, a near-identical one does notThe release valve, how genuine human oversight makes the labelling duty easeHow to properly inventory your AI use cases, and why a spreadsheet will not cut itThe fines that now apply, including a second, sneakier one for panicking under pressureWhat "high risk" really means, once CE marks and national sandboxes enter the pictureWhy Ireland's fragmented approach to enforcement is worth watchingThe one thing every firm can do this week to get on the right side of the ActAlan Thornton on LinkedIn: https://www.linkedin.com/in/alan-thornton/Tipcan SolutionsEU AI Act · Article 50, transparency · Article 99, penaltiesISO/IEC 42001 (AI management systems)CE marking (EU)RICS AI standard (in force 9 March 2026)UK Data (Use and Access) Act 2025Ireland's EU AI Act implementationWe are AI InstituteJoin us o 17 September for a webinar on: The EU AI Act, your staff, your risk: ignorance is no defenseRegister now

Andreessen Horowitz has a thesis that every building is still designed by software from the 1990s. Niklas Lindgren agrees, and he is building the company to change it. His claim is blunt: AI is about to change how 70 to 80% of the hours on an engineering project get done.Niklas is the founder and CEO of Endra, a Stockholm-based AI platform built specifically for mechanical, electrical and plumbing (MEP) engineers. While we don't normally have vendors on the podcast, we made an exception in this case because Endra looks like the Revit killer everyone's been waiting for after all these years. Andreessen Horowitz certainly seems to think so, having invested in the Swedish based AI start up. In this episode, Niklas walks Maryrose through why MEP has been so slow to change, why jobs are not disappearing even as the work transforms, how firms can build entirely new services on the back of AI, and why outsourced teams are the most exposed. He closes with the one practical step every engineering firm should take this week.A sharp, grounded conversation about the AI rebuild of the built environment, from someone doing it inside the largest firms in the industry.In this episodeWhy MEP engineering has barely changed since the 1990s, and the three layers of complexity that kept it stuckWhy Autodesk is a partner, not a competitorWhat Endra has learned from working with world-class engineering teamsWhy AI will change 70 to 80% of project hours, and why jobs are going nowhereThe new products and pricing firms can build on the back of AIWhy outsourced, commoditised work is the most exposedThe one practical step every engineering firm should take this weekWhy this is a people game that takes real time, not a quick pilotLinksEndra WebsiteNiklas Lindgren on LinkedIn

Karen Lewis Enright is a change management professional with over 20 years of transformation experience across global organisations including Johnson & Johnson and Wipro. She holds a Masters in organisational psychology, but what makes her stand out is two decades spent helping leaders turn strategy into meaningful change. She specialises in AI strategy and is the first change management professional to join ChattingGPT.In this conversation, Maryrose and Karen get into the human side of AI adoption: how to lead when you're uncertain yourself, using the image of a slipping cup of coffee to explain where people's attention goes under stress, why the sceptics belong in the room alongside the enthusiasts, and how to build trust by being honest with people about what AI changes in how they work. Karen tells the story of a self-parking car that never gets used, a miniature version of every stalled AI rollout, and makes the case for celebrating the curious champions most organisations try to shut down.She talks through the change management tools she reaches for, from change agent networks on-site to reverse mentoring between graduate engineers and senior partners, to knowing when to pause a BIM rollout or a new tender process and let a stretched team catch up. She closes on the advice she keeps returning to: start with the why, and get the data, the governance and the change management right before you scale the technology across the practice.Connect with Karen Lewis Enright on LinkedIn.

Most of the AI conversation is a numbers game: licences bought, people "using AI", whether the firm has a strategy yet. James Garner thinks that is the wrong number to watch. The money is flowing and the strategies are written, but the thing almost no one is funding is capability — whether ordinary professionals can actually use the tools well. That gap, he argues, is widening, and it is where the real risk now sits.James is Head of AI at Gleeds, a Fellow of RICS, and co-founder of the Project Flux podcast. He has been a quantity surveyor since 1998, a profession he jokes you could pull someone into from 1890, give a day's training, and they would manage fine today — which is exactly what makes this moment different. He sat on the RICS working group behind its new responsible-AI standard, in force since March, which for the first time makes baseline AI literacy a requirement for every chartered surveyor, not a nice-to-have.James walks Maryrose through why an "AI strategy" that is not a business strategy powered by AI will fail, the difference between transparency and explainability (and why he prefers "bookending" to "human in the loop"), the model-cost reckoning coming for anyone on a flat monthly plan, and the eight Claude files every professional should set up to get real value from Claude Code. Along the way, the habit that keeps him ahead: half an hour, every Friday, to do nothing but experiment.A practical, clear-eyed and genuinely useful conversation about closing the gap between owning AI and actually using it, before it widens any further.James Linkdln