AI Can Write Your OKRs. That Doesn't Make Them Strategy

AI adoption fails when organisations treat it as a software rollout rather than a change to work, identity and accountability. In Series 4, Episode 9 of Wicked Problems, Toby Corballis speaks with behavioural scientist Dr Gleb Tsipursky, CEO of Disaster Avoidance Experts and author of The Psychology of AI Adoption at Work: From Resistance to Results. They discuss AI work slop, metacompetence, professional identity, shadow AI, governance bottlenecks and the changing role of human judgement. They also examine whether humans should always own strategy or whether AI may eventually develop it within goals and values set by people. Book sample and AI adoption assessment Want to know more about new ways of working for the digital age, visit the Strategic Flow's website To learn more about AI Literacy and leadership training, visit the Cortessian website

AI can generate objectives and key results in seconds. That does not mean it understands your strategy. In Series 4, Episode 8 of Wicked Problems, Toby Corballis speaks with Mike Horwath, founder of The OKR Hub, about how OKRs make strategy measurable, why the process of creating them matters, and where AI can genuinely improve execution. They explore KPIs, context and data quality, capability maturity, Kanban, portfolio reporting, token costs and the risk of strategic homogenisation. Mike Horwath on LinkedIn | Mike's website | The OKR Hub

This long-form episode of Wicked Problems is a conversation about how EV and AI adoption share the same communication problem: the people designing and promoting these technologies tend to foreground complexity that users neither need nor value. Toby Corballis and e-mobility communications specialist Jess Shanahan examine why clearer, more human communication matters—from selling electric cars to introducing AI inside organisations. Their conversation also moves beneath the messaging. Legacy systems, reactive leadership, weak AI literacy and poorly designed change all shape whether new technology creates value or merely adds cost and risk. Key themes: Closing the gap between what organisations want to say and what their audiences need to hear Making EV communication relevant to mainstream drivers rather than technical enthusiasts - How AI-generated content converges towards sameness and plausible-sounding errors Cognitive offloading and the erosion of critical-thinking capability Why legacy automotive manufacturers struggle to move at software speed The danger of reactive transformation and indiscriminate AI adoption Shadow AI, data risk, model choice and the need for practical AI literacy Designing technology that is straightforward without concealing meaningful choice Chapters: 00:00 - Jess introduces her background 00:40 - How a memorable EV press stunt led to a career in the electric mobility sector 02:04 - What the 49 Collective is and why it matters for women in EV 04:22 - Why EV marketing often misses the mark with mainstream drivers 06:22 - B2B messaging needs to speak to people, not just organisations 08:10 - How AI output can sound convincing while still being wrong 10:28 - Cognitive offloading - what happens when AI does too much thinking for us 16:06 - Legacy automotive versus newer EV brands and their very different structures 17:28 - Why modern EV companies can iterate faster with software-style operating models 18:55 - Why legacy manufacturers are pushing for more time and more regulatory flexibility 21:06 - Why people, not just process, determine whether transformation succeeds 22:27 - Why piling new technology onto old systems often fails 23:01 - Why the ICE vehicle fleet means legacy businesses cannot simply be switched off 24:29 - Why reactive companies chase quick wins instead of building a stronger strategy 27:06 - When AI is the wrong tool and why risk tolerance matters 29:26 - How uncontrolled internal usage drives token bills through the roof 31:50 - The problem of shadow AI and accidental data breaches 32:49 - AI literacy requirements and why boards cannot claim ignorance 33:41 - Jess on training organizations in AI risk, prompting, and output checking 35:11 - Model drift and the challenge of choosing the right AI model for the job 38:39 - Should AI interfaces be smart enough to choose the model for the user? Jess' Links: Jess Shanahan on LinkedIn Jess' Personal Website Jet Social, Jess' professional brand, closing the communication gap Charge Happy, a place to find the most interesting places to charge your car Toby's Links: - Toby Corballis on LinkedIn (https://www.linkedin.com/in/tobycorballis/) - Strategic Flow, Toby's consulting firm Other Links mentioned: The 49 Collective Deloitte AI refund in Australia - The Guardian  The EU AI Act - European Commission More episodes: https://wickedproblems.fm/ 

This long-form episode of Wicked Problems is a practical conversation about AI adoption after the initial hype. Toby Corballis and Marcellus Lindsay look beyond the promise of automation and ask what happens when AI is introduced into organisations that cannot yet govern its cost, risk, data, accountability or human impact. The discussion moves between real-world implementation, change leadership, AI literacy, trust, hallucinations, token costs, agentic AI, and the familiar pattern of technology running ahead of organisational capability. The point is not that leaders should avoid AI. It is that they need enough understanding to ask better questions before they scale it. Marcellus brings the perspective of a consultant implementer: someone who can work with senior leaders while also understanding what happens on the ground. Together, he and Toby argue for controlled speed, domain expertise, human-in-the-loop review, and governance that helps organisations move quickly without building on flawed foundations. Key themes - AI governance, risk and accountability as leadership responsibilities, not technical afterthoughts. - Why many corporate AI initiatives fail to produce clear return on investment. - The gap between exponential technology change and slower organisational change. - How AI hallucinations, confidence and error rates create hidden implementation risk. - Why human trust in AI can exceed human trust in other people. - Token pricing, cost controls and the danger of unbounded AI usage at scale. - The role of domain expertise in judging where AI should and should not be applied. - Why agentic AI needs stronger guardrails than simple chat-based experimentation. - How flawed assumptions early in a programme compound into expensive failure later. - Why AI literacy should be basic, practical and principle-based for senior leaders.   To register intrerest in one of the forthcoming webinars mentioned, sign up at Strategic Flow

Is your firm spending money on new software when the real problem is hiding in plain sight, inside the processes, handovers, and habits your team has quietly built around a broken system? In this episode, Toby Corballis is joined by Winnie Barnwell, a digital transformation consultant and project and change manager who works primarily with law firms and professional services businesses. Winnie brings a structured, diagnostic approach to operational improvement, helping organisations understand not just what is going wrong, but why, and what to do about it in a way that actually sticks. Winnie's work sits at the intersection of workflow design, system implementation, and change management. She is frequently brought in when a firm knows something is not working but lacks the clarity to define the problem, let alone fix it. Her focus is always on getting the process right before reaching for the technology.   What We Cover Why clients almost always misdiagnose their own problems, and what the real root cause usually turns out to be How discovery sessions and probing questions surface the issues that sit beneath the symptoms The law firm losing 18 hours a week on admin, and why the answer had nothing to do with changing their software Why a consultant does not need to know your specific CRM, and what they actually need to understand instead The cost of delay: how to quantify the financial impact of an unresolved problem and use it to prioritise Cross-pollinating methods across industries, and when bringing in a specialist really does make sense Translation as a core consulting skill, from C-suite to accounts, and why speaking the right language to the right person is everything Building solutions that work for everyone, including the people who are not comfortable with technology What a consultant's working week actually looks like, from discovery through to Friday playback sessions   Whether you are a practice manager trying to make sense of a system that never quite does what it should, a leader considering a costly technology migration, or a consultant looking to sharpen your diagnostic process, this conversation will give you a clearer framework for finding the real problem and making change that lasts.     Chapters 00:00 — Episode begins 00:15 — Introduction 01:41 — What Winnie does: digital transformation and workflow consultancy 02:41 — The discovery process: diagnosing root causes, not symptoms 04:06 — Do consultants need to know your software? 07:04 — Case study: the law firm losing 18 hours a week 09:00 — When the fix is organisational, not technological 11:45 — The cost of delay: quantifying unresolved problems 14:59 — Cross-pollinating methods across industries 17:18 — Translation as a consulting skill 20:28 — Building anti-discriminatory solutions 24:52 — A day in the life of a digital transformation consultant   Resources Wicked Problems wickedproblems.fm   Yonda Limited yondalimited.com   Winnie Barnwell on LinkedIn linkedin.com/in/winnie-barnwell   Yonda Limited on LinkedIn linkedin.com/company/yonda-limited   McKinsey — The Art of the Root Cause Analysis mckinsey.com  

Are your engineering decisions based on what your system actually does, or on what your team believes it does? Most software teams are navigating in the dark. Developers spend more than half their time reading code, yet reading doesn't scale, and it rarely produces accurate decisions. As AI tools generate code faster than ever, the problem is getting worse, not better. This episode challenges the assumption that delegating understanding to AI is the answer. Tudor Girba is the co-creator of Moldable Development and the Glamorous Toolkit, and a co-founder of Feenk, the research company behind both. With decades of experience working on some of the most complex legacy system challenges in enterprise software, Tudor brings a rigorous, engineering-discipline lens to a problem the industry has largely left unaddressed: how do you make a system legible, without reading it line by line? Tudor is also co-authoring a book with Simon Wardley called Rewilding Software Engineering, written openly and available free at moldabledevelopment.com. The work sits at the intersection of software engineering, decision-making, and organisational change, and is increasingly relevant to anyone responsible for large or ageing codebases.   What We Cover Why reading code is the single largest cost in software development, and why no one talks about optimising it How moldable development works, and why it is closer in spirit to test-driven development than to conventional documentation Why AI cannot replace human understanding of a system, and what happens when teams try to make it do so How asking an LLM to build you a tool produces better results than asking it to answer your question directly Why a diagram drawn by a human about a system is a belief, not a fact, and what the difference costs you How Feenk helped an organisation find an entire system they did not know they had, two weeks into an engagement Why only 26 percent of software modernisation efforts succeed, and what the outliers are doing differently What it looks like to migrate more than 20 legacy systems in under a year, every time, without failure How the Glamorous Toolkit enables thousands of custom, context-specific development tools to compound over time Why Tudor believes we are nowhere near the peak of human ability, and why perception is the bottleneck, not intelligence   Whether you are a CTO navigating a legacy modernisation programme, a senior engineer trying to reduce the time your team spends reading unfamiliar code, or a technology leader being asked to rely more heavily on AI for engineering decisions, this episode will give you a clearer framework for where human understanding must remain, and the tools that make it possible.   Chapters 00:00 — Reading Code Is the Biggest Cost Nobody Is Optimising 02:26 — What Moldable Development Actually Solves 07:17 — Why LLMs Miss 20-30% of Your System, and What to Do Instead 09:23 — Diagrams Are Beliefs, Not Facts 10:12 — Make the System Draw Itself 13:26 — The User Interface Insight That Changes Everything 18:37 — Verifying AI Output Against Reality 21:04 — The Data Pipeline That Did Not Move for Three Years 24:03 — 26% Success Rate: Why Modernisation Efforts Fail 25:13 — The Bottleneck Is Perception, Not Intelligence   Resources Wicked Problems Podcast https://wickedproblems.fm Feenk (Tudor Girba's company) https://feenk.com Moldable Development (book and resources) https://moldabledevelopment.com Glamorous Toolkit https://gtoolkit.com Rewilding Software Engineering (book, written in the open with Simon Wardley) https://moldabledevelopment.com

Is the steel industry's carbon problem actually a resource problem in disguise? In this episode of Wicked Problems, we explore one of the most promising, and least-discussed, frontiers in industrial decarbonisation. Dr Harriet Kildahl is the CTO and co-founder of Perocycle, a deep tech spin-out from the University of Birmingham focused on eliminating carbon emissions from heavy industry through a closed-loop thermochemical process. As a chemical engineer and entrepreneur with a background in pioneering industrial decarbonisation, Harriet brings both the scientific rigour and the commercial ambition needed to tackle one of the hardest problems in the green transition. Perocycle's approach is grounded in a deceptively simple insight: the CO2 that steel furnaces emit is not just a waste product, it is also the raw material needed to keep the process running. By splitting that CO2 back into carbon monoxide and recycling it directly into the furnace, Perocycle creates a loop that displaces coal, coke, and natural gas without requiring carbon capture and storage. In this episode, we cover: Why fossil fuels in steelmaking are not just an energy source but a chemical input, and why that makes decarbonisation so difficult How Perocycle's thermochemical catalyst splits CO2 into carbon monoxide and recycles it back into the furnace in a continuous loop The difference between blast furnace and direct reduced iron (DRI) steelmaking, and how Perocycle's process works with both Where Perocycle sits on its commercialisation journey, from lab-scale catalyst work to a mobile pilot plant targeting 1,000 tonnes of CO2 per year Why the economics of decarbonisation matter as much as the science, and how Perocycle's model is designed to save steel mills money, not just emissions How a lean, partnership-led approach is helping Perocycle de-risk its path to first commercial deployment What the green transition looks like as a second industrial revolution, and why the UK needs to be quicker at turning innovation into execution Whether you are an investor tracking the industrial decarbonisation space, an operator in the steel or heavy industry sector, or a green tech founder navigating the path from deep tech to commercial scale, this episode offers a clear-eyed and technically grounded view of what the transition to a closed carbon economy could look like in practice. Chapters 00:00 — The Only Way Decarbonisation Happens Is If It Saves Money 00:18 — Why the UK Must Execute on Green Innovation, Not Just Invent It 01:00 — Introducing Perocycle: Deep Tech Spin-Out With a Closed Carbon Loop 02:28 — How Steel Is Made, and Why Carbon Is So Hard to Remove 04:42 — The Circular Process: Splitting CO2 and Putting It Straight Back In 05:13 — How the Thermochemical Catalyst Actually Works 06:40 — From Lab Reactor to Mobile Pilot Plant 21:22 — Ambition and Innovation as Bedfellows: Building for Scale 22:26 — Staying Lean: Team Structure and the Partnership Approach 27:57 — Decarbonisation Only Works If It Makes Economic Sense   Resources Wicked Problems wickedproblems.fm Perocycle perocycle.com Perocycle on LinkedIn linkedin.com/company/perocycle Zero Cycle Newsletter (Perocycle) perocycle.com UK Government Net Zero Strategy gov.uk/net-zero UK Industrial Decarbonisation Strategy gov.uk/industrial-decarbonisation-strategy University of Birmingham Clean Futures birmingham.ac.uk

Is your strategy a vision built on solid foundations , or a story you've told yourself with nothing underneath it? And if the industry shifted tomorrow, would you even see it coming? This episode of Wicked Problems goes deep on Wardley Mapping , one of the most rigorous and underused frameworks in strategy today. Simon Wardley, the creator of Wardley Maps, joins the show to explain how the methodology works, where it came from, and why most of what businesses call a "map" is actually just a graph. Simon's path to mapping began not in a consulting firm or business school, but in a crisis of self-awareness: he was a CEO with no idea what he was doing, until a bookseller in Charing Cross handed him Sun Tzu's The Art of War. What followed was two decades of developing a tool now used by governments, investment firms, and , as Simon discovered at a conference , NASA. What We Cover Why everything called a "map" in business , mind maps, process maps, systems maps , is technically a graph, and why the distinction matters for strategy How components in any value chain evolve from genesis through custom-build, product, and commodity , and how to use that to your advantage Why the real value of Wardley Mapping isn't the map itself, but the conversations that happen while building it How to use maps for pre-mortem and post-mortem analysis , and why taking multiple snapshots over time reveals patterns you can't see any other way Why a useful map must be imperfect, and why chasing a perfect map is a guaranteed path to paralysis How investment firms use Wardley Maps to identify which industries are heading for disruption , including groups that use them specifically to short sectors How the UK government used mapping to save £450 million on a single project, and what that required at the level of spend control Why great leaders being great storytellers is a liability in strategy discussions , and how maps sidestep that problem entirely How to apply different management methodologies (Agile, Lean, Six Sigma) to the correct parts of a map rather than picking one and applying it everywhere Where to start if you've never mapped before, and what the learning curve actually looks like   Whether you're a business leader trying to validate a strategy before committing resources, an operator in a fast-moving sector like EV or fintech trying to understand where the market is heading, or a consultant who needs a better tool for driving alignment across senior stakeholders , this episode gives you a framework that sharpens how you see competitive landscapes and where to move next. Chapters 00:00 Introduction 03:57 The CEO Who Had No Idea What He Was Doing (It Was Me) 05:55 Maps vs. Graphs: Why Almost Everything Called a Map Isn't One 08:40 How Every Component Evolves , From Genesis to Commodity 10:17 Methodologies, Finance, and the £450 Million Government Save 13:18 Nine Years to Get Good , and Why Practice Is the Only Path 21:14 Maps Are a Snapshot: What Tectonic Plates Teach Us About Strategy 24:53 The Real Value of Mapping Is in the Conversation, Not the Artifact 25:56 Why a Useful Map Must Be Imperfect 27:25 Mapping in Government: GDS, Spend Control, and the Cabinet Office   Wicked Problems Podcast https://wickedproblems.fm Simon Wardley Simon's website and contact https://swardleymaps.com Wardley Maps (online book) , medium.com/wardleymaps https://medium.com/wardleymaps   Wardley Mapping Community & Tools Wardley Maps community hub (Chris Daniels) https://wardleymaps.com Learn Wardley Mapping (Ben Mosior) https://learnwardleymapping.com Online mapping tool https://onlinewardleymaps.com  

Are your teams working flat out, but delivery still feels slow, unpredictable, and hard to explain to stakeholders? In this episode of Wicked Problems, Toby speaks with Sonya Siderova, founder and CEO of NAVE, a flow analytics platform that helps software and delivery teams identify process bottlenecks, reduce cycle times, and make performance visible through data. Sonya brings over a decade of experience in software development, product management, and process optimisation. NAVE has been on the market for eight years and was built around a simple but powerful observation: the problems slowing your teams down are usually invisible, and the instinct to push people harder makes them worse, not better.   What We Cover Why optimising for busyness actively hinders your delivery process, and what to focus on instead How flow analytics reveals that 70% of work time is often spent waiting, not being worked on The mindset shift from managing people to managing the flow of work through a system Why 'stop starting, start finishing' is so difficult for management to accept, and how data makes the case How NAVE's AI layer translates complex flow metrics into plain-language narratives for non-technical stakeholders Why deploying AI upstream of an unresolved bottleneck makes the bottleneck significantly worse How to use flow data to have strategic conversations about reprioritisation and work in progress limits What reports from Atlassian, MIT, and Oxford reveal about why AI initiatives are hitting problems in delivery teams   Whether you are a transformation lead trying to build the case for change, an engineering manager looking to improve predictability, or a consultant working with delivery teams, this episode gives you a clear framework for understanding where work is really getting stuck and how to talk about it in language that resonates with leadership. Chapters 00:00 — Why AI Amplifies Broken Processes, Not Just Good Ones 00:21 — Introducing NAVE and the Series on Transformation Tools 02:34 — What Flow Analytics Actually Measures 04:30 — The 70/30 Problem: Most Work Time Is Waiting, Not Working 06:02 — Why Keeping People Busy Is Killing Your Delivery 07:17 — Getting Comfortable With Data and a Different Way of Working 10:00 — Stop Starting, Start Finishing: The Management Mindset Shift 19:00 — How NAVE's AI Turns Flow Metrics Into Plain-Language Narratives 27:04 — Your Job Is Now the Change Work, Not the Data Translation 29:35 — The Bottleneck Problem: What Happens When You Speed Up the Wrong Thing Resources Wicked Problems Podcast https://www.wickedproblems.fm NAVE, Flow Analytics Platform https://getnave.com/ Sonya Siderova on LinkedIn https://www.linkedin.com/in/siderova/ Kanban Guide (Kanban University) https://www.kanban.university/kanban-guide Atlassian State of Teams Report https://www.atlassian.com/blog/state-of-teams  

Electric vehicles aren't just for driving — they're powerful batteries on wheels. In this clip, Claire Miller breaks down how new V2G (vehicle-to-grid) technology could let you earn money by simply keeping your EV plugged in. From smart tariffs to grid balancing, this is the future of energy — and it's already rolling out.