Human x Intelligent

Where does your judgment still matter? - Season 3 Premiere

Send us an email! The more people trusted AI on a task, the less critical thinking they said they did on it. Season 3 of Human x Intelligent starts here, with an eight-minute premiere on where your judgment still matters. I opened the season with a short solo episode, because the real conversations start next Monday. Here I look at what's happening in the market right now, what I mean by the human moat and what this season will look like, including an app I'm building called Human Plus. And I ask the question I can't stop asking: if AI does the practice, where does the next group get good? What you'll learn: Why the more people trusted AI on a task, the less critical thinking they reported and why I hold that study loosely Why I changed my mind about the human moat and the definition I use now What Gallup found about how US workers use AI and why the first draft is quietly changing hands The Stanford result that found no widespread job loss and still made me stop: employment for workers aged 22 to 25 in the most AI-exposed jobs is about 19 percent below where it would be What Season 3 looks like, from Monday interviews to practical videos to Human Plus A one-week challenge with a strange name: count your nothings Next Monday: the first interview of the season!!! Enter the early birds for Human Plus and tell me what's missing: https://humanxintelligent.com/human-plus In this episode, I cover: 0:00 The study that made me uncomfortable 1:15 Welcome to Season 3 1:37 Who this show is for 2:01 The human moat, revised 2:41 Gallup: who writes the first draft now 3:07 Stanford: the youngest workers in AI-exposed jobs 3:42 If AI does the practice, where does the next group get good? 4:08 What Season 3 looks like 4:36 Practical videos, starting with Miro, Claude and Lovable 4:56 Human Plus and how to sign up 6:17 Count your nothings 7:10 Next Monday: the first interview Referenced: Microsoft Research and Carnegie Mellon, "The Impact of Generative AI on Critical Thinking" (CHI 2025): https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/ Gallup, "Rising AI Adoption Spurs Workforce Changes" (July 2026): https://www.gallup.com/workplace/704225/rising-adoption-spurs-workforce-changes.aspx Stanford Digital Economy Lab, "Canaries in the Coal Mine?" (August 2026 update): https://digitaleconomy.stanford.edu/news/canariesaug26/ Tools referenced: Miro: https://miro.com Claude: https://claude.ai Lovable: https://lovable.dev The Nest, where I taught the workflow: https://lnkd.in/p/gF7dzGRh Where to find Madalena: Human Plus sign-up: https://humanxintelligent.com/human-plus LinkedIn: https://www.linkedin.com/company/human-x-intelligent/ Instagram: https://www.instagram.com/humanxintelligent/ Website: https://humanxintelligent.com/calendar Subscribe so you catch the first interview: https://www.youtube.com/@humanxintelligent Listen on your podcast app: #HumanxIntelligent #AIatWork #Podcast Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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Are you using AI or is AI using you? | Ricardo Luiz | Season finale

Send us an email! We talk a lot on this show about what AI can do. This one's about what it can quietly take, if you let it. Ricardo Luiz has spent over twenty years building products at the intersection of psychology, UX and AI, and his warning is simple: the moment you outsource what to think, you've outsourced who you are. AI is good enough and fast enough that most people won't notice the handoff happening. We get into exactly where the line sits between AI amplifying you and AI quietly making you dependent on it. Ricardo's test: if the tool disappeared tomorrow, would you panic or would you just slow down? Panic means you were never really driving. We also get into why Anthropic named 'understanding what's happening inside the black box' a real 2026 goal, why intent is the one word Ricardo wants you to sit with before opening another AI tool and a story involving a dog, a cancer diagnosis and a vaccine formula that shouldn't have existed. Plus a study on why people trust an AI's medical answer over an actual doctor's, even when it's wrong. And we get honest about the economics nobody wants to talk about. The tools feel cheap right now because the real cost isn't being charged yet. Ricardo's bet: give it two years and the businesses behind these tools will have to charge what they actually cost and not everyone will be able to afford what they've gotten used to. Fair warning: this one might change how you use every AI tool you touch after it. About Ricardo: Ricardo is a product leader focused on AI-native product development, mentorship and the cultivation of communities of practice. He's spent his career turning ambiguous problems into shipped products and individual contributors into the teams everyone wants to work on. He mentors emerging and senior PMs across geographies, hosts peer-learning circles and believes the most effective thing a senior practitioner can do is multiply other practitioners. - LinkedIn: https://www.linkedin.com/in/uxluiz/ - UXDX: https://uxdx.com/profile/ricard-luiz/ - WUD Portugal: https://wudportugal.com/orador/ricardo-luiz/ Mentioned in this episode: 'The Urgency of Interpretability' - Dario Amodei: https://darioamodei.com/post/the-urgency-of-interpretability The study on over-trust in AI-generated medical responses: https://arxiv.org/abs/2408.15266 Coverage of the Claude Max plan usage-limits lawsuit (Engadget): https://www.engadget.com/2194626/anthropic-hit-with-lawsuit-over-its-claude-max-usage-limits/ Season 2 ends here. Season 3 arrives in September and takes the show beyond tech; we will have doctors, chefs, scientists, artists, all asked the same closing question. Subscribe now so you're there for episode one. Human × Intelligent is a podcast at the intersection of design, AI and human agency. Hosted by Madalena Costa. → humanxintelligent.com → https://www.instagram.com/humanxintelligent/ → https://www.linkedin.com/company/human-x-intelligent/ → https://www.instagram.com/designwithmaddie/ → https://www.linkedin.com/in/madalenafigueirasdacosta/ 📩 Want to be a guest on Human X Intelligent? Reach out to Madalena at madalena@humanxintelligent.com Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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Is AI making us dumber? The science, the warning and what to do about it

Send us an email! You've heard the productivity pitch. AI makes you faster. AI makes you more efficient. AI makes your work better. But what if it's also making you worse at working without it and faster than you think? In this episode of Human × Intelligent, I go deeper into a cluster of research and stories that, taken together, paint a picture we can't afford to ignore. A study from Carnegie Mellon, Oxford, MIT and UCLA found that just 10 minutes of AI assistance was enough to impair independent problem-solving. Researchers have shown that inaudible sounds hidden inside background music can hijack AI notetakers in your meetings without you knowing. MIT professor Max Tegmark has pointed out that AI is currently less regulated than a sandwich shop. And the movie Idiocracy, a 2006 comedy barely anyone saw, is starting to feel less like satire and more like a schedule. This isn't a doom episode. It's an episode where I share a perspective. I walk through what the science actually says, what it means for how we work and lead and what to do about it. From the three questions you should be asking every AI vendor you work with, to the one habit that protects your cognitive independence, to what it actually means to be an irreplaceable human professional in 2026. If you use AI at work and you do, this is the episode to sit with. Reading list: AI Assistance Reduces Persistence and Hurts Independent Performance - arXiv Using AI for 10 minutes damages intellect, study shows - Euronews AI: the "boiling frog" effect on cognition - Futurism AI assistants can be hijacked by inaudible sounds - Cyber Insider AI is less regulated than sandwiches - Euronews Idiocracy: A Prophetic View of an AI-Driven Future - Aragon Research A Place for Human Talent in the AI Age - IMF Redesigning work around human skills - EY Connect with us: 🌐 humanxintelligent.com 📸 Instagram: @designwithmaddie 📸 Instagram: @humanxintelligent 💼 linkedin.com/in/madalenafigueirasdacosta 💼 linkedin.com/company/human-x-intelligent Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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From prototype to production: Why building reliable Agentic AI is still so hard | Joana Mesquita

Send us an email! Building an AI agent has never been easier. But getting it to production? That's where most projects quietly die. In this episode of Human X Intelligent, host Madalena Costa sits down with Joana Mesquita, Machine Learning Engineer at Swiss Post and former ML practitioner at Adidas and Feedzai, to explore what fundamentally changes when you move from deterministic software to probabilistic, generative AI systems and why so many teams are unprepared for that shift. Joana has spent years building scalable AI systems, implementing MLOps practices and even developing tools to measure the carbon footprint of machine learning workflows. She's one of the clearest thinkers working at the intersection of AI engineering and responsible development. In this episode, we cover: Why building a working prototype is easy, but building a reliable agentic system is a completely different challenge What fundamentally breaks when you move from deterministic to probabilistic, generative systems Why traditional governance models fail for agentic AI and what needs to replace them How to embed governance into the product itself Input, output, data and tool guardrails with practical examples Why evaluation needs to start on day one (and the data behind why it matters) The risks and trade-offs of using LLMs as judges and how actually to align them What breaks in the prototype-to-production transition: data quality, cost, latency and governance How to move from 'trust me, it looks good' to trust backed by evidence and measurement How organizations can balance innovation speed with responsible AI development What sustainable AI scaling actually means, including environmental impact One idea that will stay with you: 'Stop thinking about AI products as only the model. Start thinking about them as a system that learns over time.' Whether you're an ML engineer, a product manager or a technical leader navigating the GenAI transition, this conversation will change how you think about what it actually takes to build AI that works in the real world. Connect with Joana Mesquita: → LinkedIn: https://www.linkedin.com/in/joanamesquita96/ → Medium: https://medium.com/@joana.c.mesquita.f Human × Intelligent is a podcast at the intersection of design, AI and human agency. Hosted by Madalena Costa. → humanxintelligent.com → https://www.instagram.com/humanxintelligent/ → https://www.linkedin.com/company/human-x-intelligent/ → https://www.instagram.com/designwithmaddie/ → https://www.linkedin.com/in/madalenafigueirasdacosta/ 📩 Want to be a guest on Human X Intelligent? Reach out to Madalena at madalena@humanxintelligent.com Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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How to use NotebookLM to do real product research (with the prompts)

Send us an email! NotebookLM can do in an afternoon what used to take a research team a week, if you know how to prompt it. In this episode of Human × Intelligent, we walk through a complete 8-step AI-assisted research workflow using a real Spotify UX interview study as our working example. Eight participants, 60-minute sessions and a set of raw transcripts, turned into personas, empathy maps, Jobs to Be Done analysis, How Might We questions, an opportunity matrix and a full synthesis report. Every step includes the exact prompt to paste into NotebookLM. No vague instructions. No, just ask AI to help you. Real prompts, real frameworks, real output. What you'll learn: How to orient NotebookLM before any analysis begins (and why this matters) How to build 3 grounded user personas, including a tension map that shows where their needs conflict How to create empathy maps per persona using actual participant language How to identify functional, emotional and social Jobs to Be Done and rank those that are most underserved How to generate and prioritise How Might We questions that open up real solution space How to build a feature opportunity matrix and effort vs impact quadrant How to affinity cluster raw insights into a 3-level observation → insight → opportunity hierarchy How to generate an executive summary, full research report and stakeholder presentation outline All 16 prompts are included in the show notes as a ready-to-use guide. This workflow applies to any qualitative research: user interviews, usability test notes, support tickets, survey responses. If you can put it in a document, NotebookLM can help you make sense of it. Show Notes: Full prompt guide PDF NotebookLM Follow Human × Intelligent Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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AI doesn't fix broken products. It amplifies them | Michelle Brito

Send us an email! AI won’t fix your product. In many cases, it makes things worse. In this conversation, Michelle Brito explains why most companies are getting AI adoption wrong and what they should be doing instead. From her work at Volkswagen Digital Solutions, Michelle shares practical insights on designing AI-powered products that actually deliver value and not confusion. In this episode, you’ll learn: - When artificial intelligence actually makes sense in a product - Why AI is often used as a shortcut for deeper problems - The difference between AI and simple automation - How to evaluate if your workflows are ready for AI - Why user trust breaks when AI is introduced too early ⚠️ The biggest mistake? Starting with the technology instead of the problem. 💡 Key takeaway: AI doesn’t fix broken systems. It amplifies them. Connect with Michelle Brito: → LinkedIn: https://www.linkedin.com/in/michelle-brito-47342554/ Human × Intelligent is a podcast at the intersection of design, AI and human agency. Hosted by Madalena Costa. → humanxintelligent.com → https://www.linkedin.com/company/human-x-intelligent/ → https://www.instagram.com/humanxintelligent/ → https://www.instagram.com/designwithmaddie/ → https://www.linkedin.com/in/madalenafigueirasdacosta/ Guest bio Michelle Brito is a Senior Product Designer at Volkswagen Digital Solutions and a mentor at Ladies that UX Lisbon, with over 15 years of experience spanning journalism, editorial design, and digital product strategy. Based in Lisbon, she currently leads design efforts for B2B search engines and researches the integration of AI-driven solutions within the automotive sector. Her background is uniquely multidisciplinary, combining a Master’s in Communication Sciences with a d.MBA and specialized training in UX/UI design. Throughout her career, which includes work for publishing houses, government agencies and marketing firms, Michelle has focused on bridging the gap between business goals and user needs through benchmarking, usability testing and visual thinking. Chapter timestamps 00:00 – Why companies are asking the wrong AI question 00:42 – Introduction to Michelle Brito 01:15 – Is AI the right starting point? 02:20 – Where companies misuse AI (simple problems, wrong solutions) 03:18 – AI as a shortcut for deeper issues 03:39 – Why organizations rush into AI 04:44 – When AI creates confusion and distrust 05:30 – How to push back on stakeholders 06:14 – How to know when AI actually makes sense 07:27 – Why users don’t adopt AI tools 08:24 – Questions to evaluate AI vs automation 08:40 – AI driven by hype vs real need 09:28 – Real example: AI making simple tasks harder 10:10 – Red flags in AI product decisions 11:03 – Why research still matters (even if it’s “boring”) 12:07 – Responsible AI in regulated environments 13:28 – Who is accountable for AI decisions? 14:48 – What healthy AI adoption looks like inside teams 16:40 – Where to start with AI (the right way) 17:21 – The most overlooked first step 18:16 – Making decisions under pressure 19:21 – AI requires simplification, not complexity 19:53 – Practical advice to avoid AI traps 21:06 – Final thoughts on AI hype vs reality Concepts to explore further: → AI vs Automation → AI as a multiplier (not a fixer) → Problem-first vs technology-first thinking → User trust in AI systems → AI readiness (data, workflows, goals) 👉 Subscribe for more conversations on AI, product design and human-centered technology. Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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The interface trap: Why your AI adoption is failing (and how to fix it) | Kaisa Martiskainen

Send us an email! Is your team actually using AI, or are they just playing with it? In this episode of Human X Intelligent, host Madalena Costa sits down with Kaisa Martiskainen, AI Operations lead, to uncover the hidden gap in corporate AI adoption. While usage metrics might be up, true understanding is often lagging. Kaisa explains why providing access to chatbots isn’t the same as building capability and how the 'Interface Trap' prevents organizations from seeing the real value of AI. In this episode, we explore: The missing conceptual layer: Why mental models are more important than tool proficiency. The interface trap: How limiting AI to a chatbot window narrows your strategic vision. Human learning vs. Machine speed: Why humans need friction and failure to truly 'get' AI. Predictors vs. knowers: Understanding the three foundational concepts every employee needs before their first prompt. Beyond surface level: How to transition from "interacting" with AI to 'integrating' it into your organizational DNA. If you’re a leader, manager or individual contributor feeling overwhelmed by the AI hype, this conversation will help you shift from reactive usage to intentional system thinking. Connect with Kaisa Martiskainen: → LinkedIn: www.linkedin.com/in/kaisamartiskainen → Substack: https://mamaknowsai.substack.com Human × Intelligent is a podcast at the intersection of design, AI and human agency. Hosted by Madalena Costa. → humanxintelligent.com → https://www.linkedin.com/company/human-x-intelligent/ → https://www.instagram.com/humanxintelligent/ → https://www.instagram.com/designwithmaddie/ → https://www.linkedin.com/in/madalenafigueirasdacosta/ Guest bio Kaisa works at the intersection of technology and human understanding. She helps organizations and individuals understand how to work with artificial intelligence in practical, thoughtful ways, focusing not just on tools, but on how technology changes the way people think, learn and make decisions Chapter timestamps 00:00 – Use vs. Understand 01:09 – Real AI Adoption 02:49 – AI Mental Models 04:29 – The Metrics Myth 05:35 – How Humans Learn AI 07:21 – 3 Rules of Prompting 10:06 – The Interface Trap 12:38 – Access ≠ Capability 15:47 – AI as a Collaborator 17:28 – The Teaching Problem 19:54 – A Learning Challenge 21:43 – The "Ideal" AI Org 24:32 – The Best Investment 25:49 – Where to Learn More 26:57 – Final Takeaways Concepts to explore further: → AI vs Automation → AI as a multiplier (not a fixer) → Problem-first vs technology-first thinking → User trust in AI systems → AI readiness (data, workflows, goals) 👉 Subscribe for more conversations on AI, product design and human-centered technology. Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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The future of UX: design that knows you better than you know yourself | Joana Cerejo

Send us an email! What does it mean to truly anticipate a user and not just what they'll click next...but what they're trying to become? In Episode 19 of Human × Intelligent, Madalena Costa is joined by Joana Cerejo, design lead, AI product designer and author of the Anticipatory Design Playbook. Together, they explore the real depth of anticipatory design, how behavioral science fits into modern AI product work and why most systems fail not because of bad technology but because of a fundamental misunderstanding of human intent. In this episode: - The three layers of anticipation: needs, behavior and outcomes - Why designing for agency can't be an afterthought - Behavioral science frameworks every AI designer should know - The filter bubble problem and collective manipulation - What the Nest Thermostat gets wrong about resilient design - Why transparency is the foundation of everything Connect with Joana Cerejo: → LinkedIn: https://www.linkedin.com/in/jcerejo/ → Website: https://jcerejo.com/ → The Anticipatory Design Playbook (Amazon): https://www.amazon.es/-/pt/dp/1041079109 → Watch Why Personas Fail AI (And What Works): https://www.youtube.com/watch?v=_7dSuJB6M1o&t=897s Human × Intelligent is a podcast at the intersection of design, AI and human agency. Hosted by Madalena Costa. → humanxintelligent.com → https://www.instagram.com/humanxintelligent/ → https://www.linkedin.com/company/human-x-intelligent/ → https://www.instagram.com/designwithmaddie/ → https://www.linkedin.com/in/madalenafigueirasdacosta/ Guest bio Joana Cerejo is a design lead and AI product designer working at the intersection of user experience, behavioral science, and intelligent systems. With nearly a decade of experience designing AI-powered products across fintech, e-learning, and manufacturing, she specializes in making systems that are human-centered, trustworthy, and ethically grounded. She is the author of the Anticipatory Design Playbook, exploring how AI can move beyond predicting behavior to genuinely supporting people in meaningful, long-term ways. Resources & tools section Frameworks mentioned in this episode: → Prochaska Transtheoretical Model - stages of behavioral change; helps design systems that meet users where they actually are → Fogg Behavior Model - behavior happens when motivation, ability, and prompt align at the same time → Nudge Theory - the right intervention at the right moment can make or break a service Book: The Anticipatory Design Playbook by Joana Cerejo - available on Amazon Concepts to explore further: → Filter bubble effect → Human-in-the-loop design → Foresight/futures thinking methodology → AI literacy and explainability Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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Is AI fixing your teams or just making the cracks more visible? | PART II | Hugo Froes

Send us an email! In Part II of this conversation, Madalena Costa and Hugo Froes move from diagnosis to direction, exploring what conscious AI adoption actually looks like inside product teams, why designers may be the most underestimated players in the AI era and whether any organization should even be trying to become AI-first. Hugo Froes is Director of Product Strategy at Nagarro and former Head of Product Operations at OLX and Farfetch, with over 25 years of experience building and transforming product organizations. In this episode, you’ll learn: - Why designers may become the most valuable players in the AI era - How to redefine team structures around skill sets - Why the PM, designer and engineer trio may need to be completely rethought - The hidden danger of AI-generated code that looks production-ready but isn’t - Why LinkedIn is no longer a reliable signal of someone’s actual capability - How recommendations and trust networks are becoming the new hiring filter - What organizations should be asking instead of ‘how do we become AI-first?’ - Why adding AI to a broken product just creates a more broken product, faster Key ideas explored: - The designer’s moment: systems thinking and human empathy position designers as critical infrastructure - Team structure rethink: the future isn’t about roles, it’s about skill sets distributed differently - The trust filter: as AI floods the market with content and code, personal recommendations become the real signal - AI-aware not AI-first: the better question is always does AI reduce friction here, or add it? - The role of judgment: the hardest things to automate are the most human: taste, framing, empathy, direction Chapters 00:00 The Cycle of Information Quality 01:47 Understanding System Functionality 04:03 The Role of Designers in AI 07:38 Redefining Team Structures 11:04 The Future of Product Management and Design 13:14 Navigating Titles and Roles in UX 16:35 The Challenge of Hiring in the AI Era 20:49 Should Organizations Be AI-First? Links Website: humanxintelligent.com Join the conversation: https://forms.gle/qdnd3pMnr6KBDCA1A LinkedIn: @hugofroes Instagram: @thehugofroes LinkedIn: @human-x-intelligent Instagram: @humanxintelligent LinkedIn: @madalenafigueirasdacosta Instagram: @designwithmaddie // Human x Intelligent explores how humans and AI design, build and collaborate in intelligent systems // Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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AI didn't break your company. It just exposed it. | Hugo Froes

Send us an email! Is AI fixing your company or just exposing what was already broken? Discover how AI is accelerating organizational dysfunction, reshaping product teams and redefining what it means to build consciously in this conversation with product operations leader Hugo Froes. In this episode of Human x Intelligent, Madalena Costa sits down with Hugo Froes, product operations and transformation expert formerly of OLX and Farfetch, to explore one of the most uncomfortable truths in tech right now: AI isn't creating your organization's problems. It's just making them impossible to ignore. From broken processes to bloated team structures, Hugo shares a ground-level perspective on what's actually happening inside product organizations and what feels genuinely different and dangerous about this moment. If you're a product designer, product manager, founder, or team lead navigating AI pressure from leadership, Part I will help you understand what's really going wrong and why the urgency you're feeling is part of the problem. In this episode, you'll learn: - Why AI accelerates dysfunction instead of fixing it - Why 'AI-first' is the wrong question for most organizations - The real cost of reducing product teams to a minimum - Why the pressure to adopt AI is functioning as a dark pattern - How AI is creating silos inside product teams - Why 80% of AI initiatives are failing and what they have in common - The hidden cost of AI at enterprise scale - Why conscious adoption beats fast adoption every time - Why this matters As AI accelerates execution, the real differentiator shifts toward: - organizational clarity - conscious decision making - depth over speed - systems thinking - human judgment Key ideas explored: - AI as an accelerant: AI doesn't fix broken processes; it exposes them faster and at greater scale - The urgency trap: the pressure to go AI-first is itself a pattern worth scrutinizing - The cost reality: most teams have no real notion of what AI costs at enterprise scale - Quality collapse: as shipping gets easier, the percentage of truly valuable products may actually shrink - Conscious adoption: the organizations winning with AI are the ones being deliberate, not reactive Links Website: humanxintelligent.com Join the conversation: https://forms.gle/qdnd3pMnr6KBDCA1A LinkedIn: @hugofroes Instagram: @thehugofroes LinkedIn: @human-x-intelligent Instagram: @humanxintelligent LinkedIn: @madalenafigueirasdacosta Instagram: @designwithmaddie // Human x Intelligent explores how humans and AI design, build and collaborate in intelligent systems // Subscribe for more on: - AI product design - organizational design - human-AI collaboration - future of work - agentic systems - product leadership - UX and systems thinking Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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Cognitive debt: are AI tools making you worse at thinking?

Send us an email! Cognitive debt is what happens when AI tools do your thinking instead of supporting it and the research is now proving it costs you more than you realise. In this episode of Human × Intelligent, I walk through what the science actually says about AI tools and critical thinking, share a practical framework for structuring your thinking before you open any tool and give you three concrete practices for keeping your judgment intact. MIT Media Lab's 2025 study 'Your Brain on ChatGPT' found that people who relied on AI for cognitive tasks showed up to 55% weaker brain connectivity and 83% were unable to recall what they had just produced. Harvard Business School and BCG's study of 758 knowledge workers found that using AI on the wrong type of task makes your output 19% worse and highly skilled professionals couldn't tell which tasks those were. Microsoft Research (CHI 2025) found that higher confidence in AI is directly associated with less critical thinking. This is not an argument against AI tools. It is a framework for using them without losing your judgment. What's covered in this episode: → The cognitive debt research MIT, Harvard/BCG, Microsoft and what it means for product people → The goal, problem, process → The three thinking modes: capture, synthesise, decide and which tool belongs in each → Think first, mode check, own your conclusion: 3 daily practices for keeping your thinking sharp → Why the tools will keep changing and the process is what stays with you The tools will change but the process is yours. Connect with Madalena: 🌐 humanxintelligent.com 📸 Instagram: @designwithmaddie 📸 Instagram: @humanxintelligent 💼 linkedin.com/in/madalenafigueirasdacosta 💼 linkedin.com/company/human-x-intelligent Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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Is AI replacing UX designers? (The reality no one talks about) | Bruno Figueiredo

Send us an email! Is AI redefining UX design or replacing it? In this episode of Human × Intelligent, Madalena Costa sits down with Bruno Figueiredo, founder of UXLx and one of the longest-standing voices in UX in Europe, to explore how artificial intelligence is reshaping the design field. From early web design to today’s AI-powered tools, Bruno brings a long-term perspective on technological shifts and explains why this moment feels fundamentally different. As AI accelerates tasks like coding, research synthesis and interface generation, the role of designers is starting to evolve in unexpected ways. But while AI can generate outputs faster than ever and not all parts of design are equally solvable. When we move from code to creativity, from execution to judgment and from data to human behavior, the limitations of AI become more visible. This episode explores what AI can and cannot do in UX today and what designers, researchers and product teams need to understand to work with these systems effectively. We discuss: Why AI may be the biggest shift UX has ever experienced Why code is easier for AI than creative design The limits and risks of synthetic users and automated research Why accessibility still depends on strong design foundations The growing problem of AI transparency and training data How AI is reshaping UX roles: specialists vs generalists Why AI should be treated as a collaborator, not a replacement What junior designers should focus on to stay relevant //Human x Intelligent explores how humans and AI design, build and collaborate in intelligent systems// Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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The end of the chatbot? Designing AI interfaces that act and not just answer

Send us an email! The End of the Chatbox? Designing AI interfaces that act, not just answer In this episode of Human × Intelligent, Madalena Costa explores one of the biggest design shifts happening in AI products right now: the possible end of the chatbox as the default interface for artificial intelligence. Chat interfaces made AI accessible to millions of people. They are familiar, flexible and great for brainstorming, research, writing and exploration. But as AI systems become more agentic, able to plan, use tools, act across workflows and move work forward, the traditional chatbox starts to reveal its limitations. When AI moves from answering questions to taking actions, the design problem changes. This episode explores why chat interfaces can become inefficient inside real workflows and what product designers, UX professionals and product teams should start learning now to design more embedded, contextual and trustworthy AI experiences. We discuss: Why chat became the dominant AI interface Why chat breaks in action-based workflows The shift from conversation interfaces to action-driven experiences UX patterns for agentic systems: previews, rationale, progress, undo and adjustable autonomy How designers can move from building chat interfaces to designing human-AI collaboration The future of AI interfaces is likely not 'no UI' or invisible magic. It’s embedded intelligence that supports work directly inside the product experience. Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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Figma → Claude → Figma: The AI workflow product designers should know

Send us an email! This episode was originally going to be about something else, but a conversation over the weekend reminded me of a workflow I use quite often when developing and designing applications. So instead, I decided to share one Human × Intelligent workflow I keep coming back to: Figma → Claude → Figma Rather than treating AI as a chatbot outside the workflow, this setup connects Claude directly to the design environment using Model Context Protocol (MCP). That means the model can analyze interfaces, reason about product systems and help accelerate design thinking. In this episode, I talk about: What changes when AI connects directly to design tools Why context makes AI much more useful for product design Real workflows I use: UX audits, design systems extraction, dashboard analysis and component generation The difference between Official Figma MCP and Figma Console MCP What worked well, what didn’t work so well and what I’m still experimenting with Why I think the real shift is AI becoming part of the workspace This is not about replacing designers, but actually, it’s about building better collaboration between human judgment and intelligent systems. If you’re exploring AI workflows for product design, design systems or complex SaaS products, this episode should give you a practical mental model for where things are heading. Example workflows mentioned in the episode UX audit of a flow: Analyze selected screens for hierarchy, cognitive load, accessibility, spacing consistency, CTA clarity and user flow friction. Design system extraction: Analyze selected UI and identify typography scale, color tokens, spacing tokens, component patterns and layout grid. Reusable component generation: Convert layouts into base components, variants and nested structures optimized for scale. Dashboard refactoring: Audit dashboards for information hierarchy, data density, scanning patterns, visual grouping and progressive disclosure. Retention system mapping: Map a product UI to triggers, actions, rewards, feedback loops and habit formation patterns. Setup steps Sign up for a Figma Pro seat and Claude Pro or Max Install Node Install Claude Code Create a Figma token Enable Figma Dev MCP mode Configure the Figma MCP server Install Figma Console MCP locally Install the design systems MCP assistant Install the Desktop Bridge plugin Install the Figma MCP server in Claude Desktop Restart Claude Desktop Run 'check Figma status' -- Links: Episode page: Madalena on LinkedIn: /madalenafigueirasdacosta Subscribe: https://substack.com/@humanxintelligent — 🎙️ Human × Intelligent explores how humans and intelligent systems evolve together, across product, behavior and culture. --- #AIAdoption #EnterpriseAI #HumanInTheLoop #ResponsibleAI #AIGovernance #AIWorkflows #AITrust #AILeadership Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

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AI adoption in teams: The #1 sign you’re moving too fast (trust breaks here) | Krystel Leal

Send us an email! 'Most AI pilots don’t fail in the demo. They fail inside the workflow.' In this episode of Human × Intelligent, Madalena Costa speaks with Krystel Leal, a fractional AI deployment lead working at the intersection of enterprise AI implementation, customer success and real-world AI adoption. Krystel shares a simple signal that reveals when teams are moving too fast with AI: If no one can explain why the AI produced an output, the team doesn’t understand the guardrails, the workflow or the problem being solved. We explore what actually changes when AI starts working inside a team, where AI trust breaks and why human judgment and ownership still matter in AI-driven organizations. The conversation also breaks down one of the most common mistakes teams make today: delegating decisions to AI instead of delegating tasks. --- In this episode, we explore: - What changes first when AI works in a team: behavior vs mindset - Why enterprise AI pilots often fail after the demo - The difference between delegating tasks and delegating decisions - The biggest signal that a team is moving too fast with AI - Why human-in-the-loop is an ownership problem and not a checkbox - How fear and misconceptions appear when teams start using AI daily - Why companies must become AI education systems - How human communication principles apply to AI prompting - Why 'made by humans' may become a differentiator in an AI-driven world --- Key takeaway AI does not replace judgment. The most successful teams use AI as a thinking partner and not as a decision maker. --- About the guest Krystel Leal is a fractional AI deployment lead who spent years working in Silicon Valley tech startups before specializing in enterprise AI implementation. She works with organizations to turn stalled AI pilots into real production systems, redesigning workflows, ownership structures and verification processes so AI adoption actually delivers value. Her core belief: Most AI investments fail not because of the technology, but because the system around them was never built. Connect with Krystel on LinkedIn. --- 🎙️ Human × Intelligent explores how humans and intelligent systems evolve together, across product, behavior and culture. Hosted by Madalena Costa. --- Links: - Episode page: https://humanxintelligent.com/episodes/if-you-cant-explain-why-ai-output-happened-youre-moving-too-fast - Krystel on LinkedIn: https://www.linkedin.com/in/krysteleal/ - Subscribe for more Human × Intelligent: https://substack.com/@humanxintelligent Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

AI cosplay - When intelligence becomes a performance | Krasi Bozhinkova

Send us an email! In this episode, I’m joined by Krasi Bozhinkova to explore AI cosplay, the shift from AI as a tool to AI as a performed intelligence, where emotion, presence and perceived personhood become more persuasive than proof itself. This conversation goes beyond capability. We talk about: - The moment AI moved from a tool to performing intelligence - Why humans respond to emotional UX as if it were personhood - What signals show users are no longer interacting with a system but with someone - Why perception now competes with performance - What responsibility do product teams carry when persuasion becomes indistinguishable from intelligence This is not a conversation about what AI can do. It’s about what that means for the future of product design, trust and human decision-making. 📤 https://owtcome.com/signal-brief-report-jan-26 — 🎙️ Human × Intelligent explores how humans and intelligent systems evolve together, across product, behavior and culture. — 💬 Join the conversation Have something to say about AI, creativity or what it means to stay human in an intelligent world, we would love to hear from you. 👉 Join the conversation: https://forms.gle/HLAczyaxqRwoe6Fs6 👉 Visit the website: humanxintelligent.com 👉 Connect on LinkedIn: /humanxintelligent 👉 Follow on Instagram: @humanxintelligent 📩 For collaboration or guest submissions: madalena@humanxintelligent.com Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

The agentic leader: how leadership changes when your 'team' is a mix of humans and agents

Send us an email! Episode 11 (season finale) - The agentic leader: How organizational design changes when your team is a mix of humans and agents AI is no longer just transforming products. It’s transforming organizations, leadership and professional identity. In the Season 1 finale of Human × Intelligent, Madalena introduces the concept of the agentic leader, a new model of leadership for a world where your team is no longer fully human. As organizations adopt autonomous systems, agents and AI-enabled workflows, leadership shifts from managing tasks to designing environments. In this episode, you’ll hear: The full arc of Season 1: agency, autonomy, multi-agent systems, intent, and verifiability The Agentic Governance Framework and its three pillars: The Decision Boundary Matrix Legibility Reversibility How leadership changes across Product, Engineering, Marketing, and Operations Why Human × Intelligent companies are built on accountability, not automation What becomes more valuable as intelligence becomes a commodity This is the most reflective episode of the season. It's a synthesis, a manifesto and a threshold. Season 2 begins at the end of the month and will feature guests and short perspectives on what it means to be a Human × Intelligent company and why it matters. 🎙 If this season helped you think differently about AI, leadership and systems design, share it with someone building the future of work. --- 💬 Join the conversation Have something to say about AI, creativity or what it means to stay human in an intelligent world, we would love to hear from you. 👉 Join the conversation: https://forms.gle/HLAczyaxqRwoe6Fs6 👉 Visit the website: humanxintelligent.com 👉 Connect on LinkedIn: /humanxintelligent 👉 Follow on Instagram: @humanxintelligent 📩 For collaboration or guest submissions: madalena@humanxintelligent.com Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

The verifiability gap: How trust survives when systems act without asking

Send us an email! As AI-powered products become more autonomous, intelligence is no longer the hard part. Trust is. In this episode of Human × Intelligent, Madalena explores the verifiability gap, the invisible space between: 1. what AI systems do 2. what users understand 3. what product teams can actually observe and validate. You’ll learn: Why trust breaks before AI systems fail The 3 control layers inside every agentic product (professionals, users and AI) Why 'human-in-the-loop' should be a workflow and not an approval step How trust, transparency, explainability and feedback work together as system infrastructure Practical UX and product strategy patterns to retain users in autonomous systems This episode connects the dots between signals, personalization, retention and agency. It gives teams concrete ways to design AI systems that are fast and trustworthy. Next week: the season finale, Episode 11: The agentic leader, on how leadership and organizational design change when your team is a mix of humans and agents. Season 2 starts at the end of the month. 🎙 If this episode helped you think differently about trust in AI-powered products, share it with someone building systems that act on behalf of humans. --- 💬 Join the conversation Have something to say about AI, creativity or what it means to stay human in an intelligent world, we would love to hear from you. 👉 Join the conversation: https://forms.gle/HLAczyaxqRwoe6Fs6 👉 Visit the website: humanxintelligent.com 👉 Connect on LinkedIn: /humanxintelligent 👉 Follow on Instagram: @humanxintelligent 📩 For collaboration or guest submissions: madalena@humanxintelligent.com Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

The interface of intent: How humans stay in control when systems act

Send us an email! AI systems no longer just respond. They plan, decide and act and often without asking. In this episode of Human × Intelligent, we explore a critical question for the age of agentic AI: How do humans stay in control once systems can act on our behalf? The answer isn’t more prompts, smarter models or bigger Dashboards. It’s the interface of intent, the layer that makes autonomy understandable, predictable and governable. In this episode, we cover: Why do prompts stop working once systems become autonomous The difference between instructions and delegation Why Dashboards explain the past but fail the future How visibility before action builds trust Where designers must decide that autonomy stops This episode connects the dots between: The age of agency Designing autonomy without losing control Multi-agent systems and coordination If you’re designing, building or leading AI-powered products, this episode will change how you think about control, trust and human agency. 🎧 Next episode: The verifiability gap --- 💬 Join the conversation Have something to say about AI, creativity or what it means to stay human in an intelligent world, we would love to hear from you. 👉 Join the conversation: https://forms.gle/HLAczyaxqRwoe6Fs6 👉 Visit the website: humanxintelligent.com 👉 Connect on LinkedIn: /humanxintelligent 👉 Follow on Instagram: @humanxintelligent 📩 For collaboration or guest submissions: madalena@humanxintelligent.com Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist

The multi-agent organization: From agentic drift to systemic coherence

Send us an email! Autonomy scales intelligence. But without coordination, it creates conflict. In this episode of Human × Intelligent, we explore the shift from single-model AI to multi-agent systems and why intelligence at scale starts to behave less like software and more like an organization. We break down what happens when multiple autonomous agents work together, where things go wrong and how to design for coherence instead of chaos. You’ll learn: Why the 'single model' era breaks under complexity How task decomposition enables distributed intelligence What agent drift is and why it’s a structural risk and not a bug A real travel app case study where agents competed instead of collaborating The hidden token costs of multi-agent systems A five-layer orchestration blueprint for coordinated intelligence Autonomy without coordination creates conflict. Coordination without intent creates noise. Intent turns systems into teams. 🎧 Next episode: how we move beyond the chat box and design the interface of intent. --- Show notes/links > Follow Human × Intelligent for weekly episodes > Subscribe on your favorite podcast platform > Share this episode with someone building intelligent products 📬 Follow the Substack for diagrams, orchestration blueprints and deep dives into multi-agent systems 💬 Join the conversation Have something to say about AI, creativity or what it means to stay human in an intelligent world, we would love to hear from you. 👉 Join the conversation: https://forms.gle/HLAczyaxqRwoe6Fs6 👉 Visit the website: humanxintelligent.com 👉 Connect on LinkedIn: /humanxintelligent 👉 Follow on Instagram: @humanxintelligent 📩 For collaboration or guest submissions: madalena@humanxintelligent.com Together, we’re shaping a new way of working, one reflection, one insight and one conversation at a time. Support the show 🎙️ Human × Intelligent - a podcast about trust, transparency and human agency in AI systems, for product designers, PMs and founders building with AI. 🔔 Subscribe so you don't miss the next episode 🌐 humanxintelligent.com Hosted by Madalena Costa · Senior product designer and AI systems strategist