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You can ask AI to complete a task. Someone still has to decide which task matters, how it connects to the rest of the business, and what happens next. Sam Pasupalak, co-founder and CEO of Skyfall AI, joins me to talk about his team's ambition to build an AI CEO. We discuss why he sees planning as the missing piece, how world models could help a system anticipate the consequences of business decisions, and why learning continuously remains a difficult research problem. Sam also draws a line between the operations he wants to automate and the employee relationships and motivation he believes will stay human. And we get into where he expects automation to arrive first: small online businesses, including SaaS and direct-to-consumer companies, with very different implications from those he sees for larger organizations.

Candidates use AI to write applications. Companies use AI to filter them. Everyone ends up frustrated. So how do we break the hiring doom loop? Sharawn Tipton, Chief People Officer at Greenhouse, joins Johannes Sundlo to discuss what happens when you give candidates a chance to speak before judging their CV. We explore voice AI interviews, the candidates recruiters might otherwise miss, and why transparency, the option to opt out, and clear human accountability matter. Sharawn also shares how her own HR team uses AI every day, how she thinks about AI slop, and what she believes HR leaders should stop doing on Monday. Plus, a lesson from her grandmother that applies surprisingly well to AI.

You can get everyone using AI and still have very little to show for it. Syed Ali Abbas, Global VP of People for Business Units at HelloFresh, joins me to talk about what the company is doing with AI and how it judges whether the work is paying off. We discuss the data cleanup behind its AI efforts, a US pilot offering 350 recipes to more than 25,000 customers, and what happens when the pace of change leaves employees and managers struggling to keep up. Abbas also explains why HelloFresh gave people time to learn before building AI into performance expectations, starting with a skills self-assessment that neither HR nor managers could see. And we get into his objection to HR leaders trying to become the CTO, along with the work he wants HR to focus on: redesigning workflows, developing people, and helping the business get results.

Kantar has around 12,000 employees and around 15,000 agents. Andy Doyle is responsible for both, and he explains how they reached that scale in 18 months without an engineering army. In this episode: Why the CEO pushed for the title Chief People and Agent Officer, and what it changes in practiceThe bet behind giving all 12,000 people Copilot in three months, from 300 pilot licenses to everybodyChange influencers instead of change champions, and the control Andy gave up to make it workThe agent that cut a monthly HR and finance reconciliation from a week to 10 minutesKantar's target of resolving 95% of HR service queries without a human, and the three agents that guard the risk lineThe agent factory, run out of HR, with 70 citizen builders and none of them from the technology functionWhy the HR community has the highest AI usage of any function at Kantar With Andy Doyle, Chief People and Agent Officer at Kantar. Links mentioned: Andy Doyle on LinkedIn → https://www.linkedin.com/in/ajdoyle/

In May I argued that AI is a work tool, not a perk. Three months and a lot of client work later, the gap between the people who have it and the people who don't is not closing. It's growing, and most organizations are fuelling it without meaning to.In this episode:- Why well-intentioned license decisions end up splitting your workforce in two- The two cohorts living side by side in the same organization, one running fast with their own Claude and ChatGPT setups, one barely started- The mechanism behind it, licenses follow whoever is comfortable asking- Why one client is taking AI access to the union negotiating table, and why I think they're right- Equal opportunity has to include the tools, not just the loud and vocal- The real missing piece, nobody has defined why AI matters for this organization- Why leaders can't be strategic about AI without being practical with it firstLinks mentioned:- AI Is a Work Tool, Not a Perk → https://www.fullstackhr.io/p/ai-is-a-work-tool-not-a-perk Subscribe to the FullStack HR newsletter → https://fullstackhr.io

First episode of the new interview series with people who do stuff with AI, not people who talk about AI. Joe Shahmoradian runs talent acquisition for EMEA at Morningstar, and everything in this episode is running in his team today. In this episode: How his team cut post-call admin by 75% with AI transcription on screening calls, and why the first tool got killedAmira, the AI teammate that works like a journalist and turned five-day market intelligence reports into 90-minute video briefingsThe AI clone Joe built before paternity leave, and what it did when he prompted it maliciously at 1 AMMorningstar's playbook for adoption at scale, an AI Academy, a champions community that grew from 5 to nearly 200, and an idea hub with real ownershipWhat Joe would do in his first 90 days at a company starting from zero, and why governance comes before agentsWhy the workforce of the future is humans and agents as teammates, and why the discourse needs more boldnessWith Joe Shahmoradian, Director of Talent Acquisition EMEA at Morningstar. Links mentioned: Joe Shahmoradian on LinkedIn → https://www.linkedin.com/in/joeshahmoradian/FullStack HR newsletter → https://fullstackhr.io

Subscribe to the FullStack HR newsletter → https://fullstackhr.io Claude started watermarking every text it writes. Substack scores your posts for AI. Recorded from the car after two weeks off, this episode is about why both miss the point. In this episode: What Anthropic's new invisible watermark does, and what it can't doHow Substack's Pangram scan works, and why it's easy to circumventWhy the human-versus-AI framing is the wrong testThe pieces I let AI write on its own, and why they tankedThe pieces I wrote alone that got the exact same feedbackMy workflow, dictate in the car, let Claude turn it into an articleWhy turning off the AI scan reads as shame, and what to do insteadLinks mentioned: How Claude's text watermarking works → https://www.anthropic.com/news/claude-text-watermarkHow can I detect AI on Substack → https://support.substack.com/hc/en-us/articles/50891130623508-How-can-I-detect-AI-on-SubstackPangram → https://www.pangram.com

Subscribe to the FullStack HR newsletter → https://fullstackhr.io A week at Almedalen made one thing obvious. Most leaders shaping how Sweden thinks about AI have only ever touched Copilot or the free ChatGPT, and that single reference point is quietly capping what entire organizations believe AI can do. In this episode: Why almost nobody picks their own model, and how a Microsoft license becomes an AI strategy by accidentThe screwdriver vs power drill problem. Copilot solves the job, but it sets your expectations far too lowHow a single default tool shrinks the whole organization's sense of what AI is capable ofThe shadow IT trap, where your best people quietly run Claude or ChatGPT on the side and you lose both security and their hard-won knowledgeWhy limiting people to one tool becomes an employer branding problem over timeWhat the real frontrunners do instead. Securing Copilot, ChatGPT, Claude, and Gemini and letting people chooseThe blunt take on IT departments that claim multi-model access is not doable on security grounds

Anthropic just dropped Claude Fable 5, and I've run it through the same test suite he's used on every major model for 2.5 years. The verdict is clear, but the bigger lesson isn't about the model at all. In this episode:- Why Fable 5 feels like the most human-like model yet, and how it gives honest, direct feedback instead of patting you on the back- The worker scheduling test (30 people, legal requirements, hard constraints) and how Fable 5 handled it- A real client case where Fable 5 reworked a Copilot Studio workshop deck and improved the flow- The Mythos connection, the security guardrails, and why hacking headlines might be part PR- The pricing cliff on June 22. $10 per input token, $50 per output token, and Johannes's plan to pair Opus 4.8 for creation with Fable 5 for evaluation- The real bottleneck. Models are capable, organizations are not, and why experimentation time is the thing HR must fight for- McKinsey's warning that HR risks becoming a sub-department of IT if it doesn't take the driver's seat now Links mentioned:- McKinsey HR Monitor 2026 → https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/hr-monitor

Subscribe to the FullStack HR newsletter → https://fullstackhr.io Organizations keep telling Johannes Sundlo, the host here, "we've got the basics covered, skip ahead." Then he asks them to show how they use AI, and gets blank stares. This episode is about AI shame, where it comes from, and the one step you can't skip. In this episode: AI shame, what it looks like and why it's more common in 2026 than 2025The pattern behind it. Buyers say "we're proficient, go straight to processes," but the proficiency is a sliver of what the models can doWhy this shows up most in leadership teams, and why leaders who skip the grunt work can't set credible AI expectations for their organizationThe missing step. You can't jump to agentic processes before you've done the unglamorous work of learning what the model can doEducate everyone, including truck drivers without computers, because employees are already getting AI advice on salary and managers in their spare timeWhy being practical beats strategic-only thinking in AI adoption right nowLessons from roughly 110 workshops this winter and spring, and why Johannes is scaling that down

Subscribe to the FullStack HR newsletter Yes, you can connect your HR system, ATS, or engagement tool to Claude and ChatGPT. In this episode Johannes Sundlo walks through the exact steps to add an MCP server to both tools, using a safe read-only demo. In this episode: What an MCP server unlocks for your AI, explained simplyAdding a pre-made connector in ClaudeAdding a custom MCP server in Claude with a URLWhy you should start with read-only accessEnabling Developer mode and creating an app in ChatGPTConnecting and testing the server in ChatGPTAuthentication, security, and why to bring IT in earlyNew to MCP? Start with the written primer that explains connectors and MCP from scratch:https://www.fullstackhr.io/p/connectors-and-mcp-explained-for Links mentioned: MCP and connectors explainer → https://www.fullstackhr.io/p/connectors-and-mcp-explained-for

Subscribe to the FullStack HR newsletter → https://fullstackhr.io OpenAI just ran an event called Intelligence at Work, and the releases say a lot about where the world of work is going. Here is what dropped and why it matters for anyone leading AI adoption. In this episode: Why the bottleneck has shifted from model intelligence to a company's ability to absorb the techThe stat to sit with: 74% of AI's economic value is captured by 20% of companiesCodex moving into ChatGPT, and what that means if you are on ChatGPT EnterpriseSix role-specific agent plugins, including a data analytics agent that writes queries, builds charts, and produces a deckAnnotations: editing one piece of an output instead of regenerating the whole thing, and whether it solves the "last mile" problemSites: turning Codex outputs into shareable, live dashboards and internal apps (Lovable meets Artifacts)Why OpenAI keeps framing adoption as a human transformation, not an IT projectSam Altman on the next phase: proactive agents that fix things in the background before you ask Links mentioned: OpenAI Intelligence at Work (the event) → https://openai.com/sv-SE/business/intelligence-at-work/

Subscribe to the FullStack HR newsletter Performance reviews have looked the same since the 1950s, and they are still mostly task-based. AI breaks that. This episode asks what we should reward when the tasks get automated and the only thing left to value is human judgment.In this episode:- Why the task-based performance review is the wrong tool for the AI era- The judgment problem. If an agent does the work, how does an employee prove their value, and does the manager even see it- A real CHRO dilemma. Two standard performers became superstars with AI, two former high performers fell behind, and bonus season arrived- Rewarding the behavior of using AI versus rewarding valid, validated output- Why AI makes the manager's job harder, not easier- The hidden cost of a thumbs-up task that creates a ripple of rework downstream- Should we scrap the performance review, hand growth to AI plus manager, or build something new Links mentioned:- Nate B. Jones, Nate's Substack

Subscribe to the FullStack HR newsletter → https://fullstackhr.io Three and a half years into the AI shift, most leaders still know AI matters but have no idea how to use it. This episode is about why that gap exists, and why the answer is getting practical. In this episode: Why leaders keep asking for agentic workflows before they've done the basic Copilot grunt workThe uncomfortable question every AI educator should answer: are you in the weeds, or only working "strategically"?Why you can't set new expectations for your team until you understand the new way of working yourselfWhat buyers should demand from anyone selling AI upskillingHow to do this for free with YouTube and disciplined facilitation if you don't want to hire someoneWhy AI should serve your existing strategy, not become a separate "AI strategy"The one-gear Ferrari problem: everyone owns Copilot, almost nobody drives it past first gearThis podcast is hosted by Johannes Sundlo, one of Europe's leading voices on AI adoption in HR and the workplace. He helps organizations move from talking about AI to actually using it, through hands-on workshops, keynotes, and the FullStack HR newsletter.

Subscribe to the FullStack HR newsletter An impromptu between-episodes take on the AI bubble panic. Johannes cuts through the headlines about rising token costs, Microsoft cutting Claude Code, and Uber's COO questioning AI ROI, and explains what's signal and what's noise. In this episode:- Why nobody can actually call a bubble, and what the market is and isn't telling us right now- The arguments for a bubble (circular money between NVIDIA and OpenAI) versus against it (Anthropic's first profitable quarter)- Why "rising token costs" is a mislabel. Per-token prices are flat or lower, total spend is what's climbing- How Moore's Law underpins the whole thing: more powerful models for the same or lower price- The full context on Uber blowing its AI budget, and why the COO is far more nuanced than the headlines suggest- Why early subsidize-then-correct cycles are normal, same as railroads, electricity, and the internet- How to do the token economics yourself: if an agent solves a problem cheaper than a human at equal quality, you have your answer- Why the model companies are targeting a ~$58 trillion global labor market, and what that means for their valuations Links mentioned:- Rapid Response podcast, Andrew Macdonald (Uber COO) interview - Fortune coverage of the Uber AI budget story