Workplace Trends

IKEA automated the task, not the people. I have always believed that the purpose of automation should be to remove repetitive work, but IKEA’s approach shows what that principle looks like when a company applies it at scale. Its AI chatbot, Billie, began handling routine customer enquiries, which could easily have turned into a familiar cost-cutting exercise. Instead, IKEA reskilled 8,500 call-centre employees in areas such as remote interior design, digital sales, relationship-building, and more complex customer support. What I find especially interesting is that IKEA did not treat those employees as redundant simply because part of their work had become automatable. It looked at the knowledge they had already built about customers and products, then asked how that experience could create more value elsewhere. The wider remote sales channel generated €1.3 billion in revenue in 2022. We cannot attribute that entire figure to reskilling alone, but the strategic direction still matters. In my Human+AI Equation, technology provides scale and speed, while people provide judgment, creativity, and connection. IKEA did not choose between the two. It redesigned the work around both. The best automation strategy does not begin by asking, “How many people can we remove?” It begins by asking, “What more valuable work can our people do now?” How would your AI strategy change if every automation plan also required a reskilling plan? #HumanAgentOrchestrator #AITransformation #HybridManagement #WorkforceReskilling

A border officer won’t accept a photo of your passport. Your bank builds identity decisions from one every day 😳 That's the foundation of online identity verification in 2026. A process designed for in-person checks, duct-taped onto the internet. And it's breaking. Deepfakes now pass liveness checks. Synthetic identities clear onboarding flows. The selfie-plus-document model is done. Governments see it. That's why they're building something different: → 36% of Europeans already use government-backed eIDs → 80% of EU citizens will carry a Digital Identity Wallet by 2030 → eIDAS 2.0 mandates banks, insurers, and telecoms to accept them But here's the problem nobody talks about: there are 150+ eID schemes worldwide. Different standards. Different assurance levels. Different specs. A fintech operating across 10 EU markets needs 10 separate integrations. That doesn't scale. This is exactly where payments were before Visa and Mastercard built the network layer. Identity needs the same thing. That's what Hopae is building - a single integration into 100+ government-backed digital identity schemes globally. The Visa layer, but for identity. They built a 2-minute assessment to get your personalised compliance readiness score instantly. Get your score here: https://lnkd.in/djCqip95 Most organisations think they have until 2027 to sort this out. They're wrong. The deadline is 2027, but the infrastructure decisions are happening right now. Payments got their network layer, and it changed everything. Identity is next. The only question is whether you're building on it or scrambling to catch up.

🪂 How To Make Your Design System AI-Ready (https://lnkd.in/dtnpy7CM), a practical guide on how to reduce drifts, minimize mistakes, maintain context and improve the quality of AI-generated prototypes — with structured spec files, automated auditing and token layers. Put together by Hardik Pandya from Atlassian. --- 🔹 1. Design Decisions Are Infrastructure AI-generated prototypes often don't deliver consistently decent results because of tiny inconsistencies scattered all across a design system. Often it's decisions made but not documented, hard-coded values never cleaned up, or relying too much on AI making sense of mock-ups or design flows on its own. Unsurprisingly, better AI prototypes come from better data — but also from better human guidance. We shouldn’t assume that AI knows how to choose the right component, and how to design with accessibility in mind. It needs priorities, a clear path on how we make decisions, design principles, examples, do's and don'ts. In fact, we should treat design decisions as infrastructure. That means that every time we make a decision — not just a design decision, but even decision on how actually prioritize our work and how we make decisions around here — it must find a path into the spec file that is then consumed by AI. --- 🔶 2. Three Layers: Spec Files + Token Layer + Audit To ensure quality, we establish design principles, guidelines, rules in a form of “spec files”). It's structured Markdown files that include spacing rules, color choices, component usage guidelines, priorities etc. AI is going to read and reuse that spec file every time it's going to generate a prototype. Because the spec files are text files, it's much more cost-effective, but also much more accurate just because we don't rely on AI recognizing or decoding patterns from mock-ups, but gets specific guidelines instead. In fact, extending code is often a more effective way than generating code from mock-ups. Token layer lists and keeps updated all tokens used throughout the design system. AI always chooses from a closed set of named variables instead of inventing plausible values ad-hoc. An audit script catches what AI gets wrong. It scans the prototype and flags every hard-coded value and flags it if necessary. It can be a regular software doing that, with AI waiting for its feedback to come back. Finally, when a design system ships updates, a sync routine flags which spec files need updating. The goal is to make sure that AI always reads up-to-date, current specs, not the ones written against an outdated version. --- 🔺 3. Examples of AI-Ready Design Systems ⌾ Atlassian: https://lnkd.in/dVsGc3Cp ⌾ Carbon: https://lnkd.in/d4zq4WWb ⌾ CMS Design System: https://lnkd.in/dHHzV3en ⌾ Nordhealth: https://lnkd.in/d8C4j2ZA Yet again, AI can’t magically resolve technical debt or design debt — it needs guidance, decisions, priorities and principles.

Electric buses have gone from 12% to 56% of new city bus sales in Europe in just six years – crossing a clear tipping point. More than half of all new buses are now battery-electric, with several countries already at or close to 100%. In some cases, the transition has been extremely rapid. Estonia is a striking example: ➡️ 0% electric bus sales in 2023 ➡️ 84% in 2024 ➡️ 100% in 2025 At this rate, Europe's 2035 target of 100% zero emission bus sales could be reached as early as 2028. Why are buses leading the switch to electric? ✅ Fixed routes make charging predictable ✅ High utilisation makes fuel savings significant ✅ Depot charging avoids the need for widespread public infrastructure This is where electrification makes immediate economic sense – high mileage and centralised operations mean electric buses are already cheaper to operate than diesel. With relatively fast fleet turnover, this shift will show up on the road far sooner than many expect, accelerating emissions reductions while also delivering quieter streets and cleaner city air.

Context graphs are quickly becoming one of the most talked about ideas in enterprise AI, with investors Jaya Gupta and Ashu Garg calling them a “trillion-dollar opportunity.” The reason? If we want agents to do real work, they don’t just need better models. They need a reliable model of how work gets done inside an organization. A context graph connects your people, content, and systems to the time‑ordered traces of actions between them, so agents can see real processes, not just static data. At Glean, we’ve found that building a useful context graph comes down to three core elements: • Observe real work, not just final states. Capture fine‑grained activity across the tools where work happens (edits, comments, messages, status changes, meetings) instead of relying only on the current record in a single system. • Put structure on top of that activity. Use knowledge graphs (projects, customers, products, teams) and personal graphs (what each person is working on) to turn noisy events into coherent tasks and end‑to‑end processes. • Continuously learn from humans and agents. Treat every successful resolution (whether done by a person or an agent) as another trace in the graph, so the system’s “playbooks” improve over time. Before we shipped this, we tested it on ourselves. With employees’ opt‑in, we analyzed real work sequences for flows such as AE mid‑market deal cycles, SE proofs‑of‑concept, on‑call incident response, and PM feature launches to understand what effective paths actually look like in practice. Our team has published a deep dive on how we build these context graphs at Glean, from deep connectors and knowledge graphs to agentic feedback loops. https://lnkd.in/gBXRmvM5

Our Private Markets Quarterly is out now. Here’s what we’re seeing across asset classes:   Private Equity: Deal and exit activity have picked up compared to last year, while #fundraising remains a significant challenge. Managers are increasingly focused on value creation through operational improvements, margin expansion, and revenue growth within portfolio companies.   Private Credit: While fundamentals remain solid, market dislocations are rising with spreads compressing and the likelihood of declining yields. Still, fundraising is robust, with larger, established managers dominating capital raised. Overall, #directlending remains an attractive option for investors, with yields still around 10% even as spreads have compressed.   Private Real Estate: We believe weakness in publicly traded US REITs is masking improving fundamentals in private US commercial real estate. Investors are capitalizing on price declines across several asset classes, while banks are also now more willing to lend to #CRE investors. Multifamily and industrial remain favored sectors owing to strong long-term demand. See the full report below from Jennifer Liu, Daniel Scansaroli, Ph.D., and Christopher Buckley, CAIA® with contributions from Leslie Falconio, Jonathan Woloshin, CFA, and John Murtagh.

The hydrogen bubble has finally burst — and today’s news is another reminder of why. The Financial Times reports that nearly 60 major low-carbon hydrogen projects have been cancelled or put on hold this year alone, including high-profile plans from BP, Exxon, Equinor, ArcelorMittal and others. Taken together, these shelved projects represent several times more capacity than the world has actually installed to date. Why is this happening? Because the fundamentals still don’t stack up. - Lack of buyers: The hoped-for demand from steel, trucking, aviation and industry has not materialised at the scale developers assumed. - Costs remain stubbornly high: Even with cheap renewables, green hydrogen is significantly more expensive as grey hydrogen. - Infrastructure is lagging: Storage, pipelines and transport networks simply aren’t there yet. All of this reinforces something many energy analysts have said for years: hydrogen is essential - but only for the sectors where there are no viable alternatives. That means targeted use in fertilisers, refining, long-durational storage and possibly long-distance shipping and aviation - not widespread deployment across heating, industrial process heat, and road transport. We should see the current wave of cancellations not as failure, but as market correction. Reality is catching up with hype.

When I was an analyst at Goldman, I once printed out my model, went line by line with my VP, and got called out for using the wrong underline - Single, not Single Accounting - tab 7, row 350. The lesson wasn’t “underline correctly,” it was “sweat the details, take the hit, and fix it.” That ritual - 2AM, dead quiet, fluorescent lights buzzing, pride on the floor - is how generations of bankers learned discipline. Wall Street's apprenticeship model is built on repetition until precision becomes instinct. Last week, news broke that OpenAI is hiring 100+ former bankers to train models on entry-level finance for $150/hr - building LBOs, structuring IPO comps, and maybe catching an errant underline. And because the universe loves irony, the same week JPMorgan opened its new 1,400 ft Park Avenue HQ. A monument to financial power, unveiled as OpenAI begins encoding the knowledge work that built it. Some thoughts: 1. Expertise is Training Data Model companies aren’t scraping the web anymore - they’re harvesting human expertise. The advantage now lies in controlling the best domain-specific feedback loops. Specialized intelligence >> Generic intelligence. Finance is just the first testbed for “expert distillation.” Law, consulting, pharma - every high-value domain will follow: extract institutional memory, convert into weights, and sell it back as software. 2. Apprenticeship Erodes Banking’s grind was never just about Excel. It was a bootcamp for judgment - debugging logic at 3AM, staying sharp under pressure, sensing when the numbers feel off. If AI takes over that work, the next generation skips the crucible. The human muscle memory of how a deal, a model, or a decision feels is what made good bankers great ones. Without that formative layer, firms risk breeding operators of intelligence systems, not builders. That’s the paradox of automation: it preserves productivity while eroding the human process that produced it. 3. Intelligence is Homogenized The irony of institutionalizing expertise is that it eventually makes every institution look the same. We already see it in text - millions drawing from the same statistical median of human reasoning. When workflows become training data, they also become templates. The assumptions that once defined firms - how Goldman models risk, how McKinsey frames strategy - get averaged into a single institutional baseline. Efficiency rises, but originality flattens. The next moat isn’t data but deviation - the ability to think differently from what the model suggests. In a world where every model starts from the same “best practices,” value will accrue to those who break pattern. The timing of JPMorgan’s HQ opening couldn’t be more symbolic. The old aspiration was vertical - the taller your building, the greater your reach. The new one is horizontal - scale through code, not concrete. Both are feats of engineering meant to signal dominance. One reshapes the skyline; the other reshapes the labor pyramid.

They asked me to present my "diversity strategy" to the board. I brought a mirror. Set it right in the middle of their mahogany table. Watched them shift in their leather chairs as they stared at their own reflection. "This is your diversity problem," I said. Twelve faces. Same color. Same gender. Same golf club. They wanted me to solve what they refused to see: You can't fix diversity from the outside when the rot starts at the top. I'd spent six months building a framework that would've transformed their pipeline. Created pathways for Black talent they claimed they "couldn't find." Designed retention strategies for the ones who kept leaving. My research showed they'd lost 47 Black employees in 18 months. Exit interviews all said the same thing: "No path to leadership." Hard to see a path when every road leads to a room that looks like a country club board meeting from 1952. The CFO cleared his throat. "This feels... aggressive." Aggressive? Showing you a mirror is aggressive? But excluding us from every decision isn't? Paying us 63 cents to your dollar isn't? Having our faces on your website but not in your C-suite isn't? I picked up my mirror. My $75K framework. My 200-slide deck. "You don't need a diversity strategy. You need diverse leadership. And that starts with looking at why this mirror makes you so uncomfortable." They hired a different consultant. Someone who wouldn't hold up mirrors. Someone who'd make them feel good about their 2% improvement metrics. That company? Still bleeding Black talent. Stock down 30%. Getting roasted on Glassdoor. Me? I only work with boards ready to shatter their mirrors and rebuild their tables. With seats for people who don't look like them. With budgets that match their press releases. With leadership that leads by example, not excuses. Your diversity strategy isn't about finding us. We're right here. It's about finding the courage to look in the mirror and admit why we're not in your boardroom. Some reflections are meant to be broken. Thank You; It's True.™ #ExecutivePresence #BlackWomenInBusiness #ThankYouItsTrue

Saudi Arabia just pulled off the largest leveraged buyout in history. Their $925 billion sovereign wealth fund announced last week that it’s taking Electronic Arts private in a $55 billion deal — paying a 25% premium to the stock price. But this isn’t just about video games. It’s about power. In less than a decade, Saudi Arabia has become the single largest source of capital in global sports. They’ve poured billions into Formula 1, WWE, boxing, and LIV Golf. They bought Newcastle United. They signed Cristiano Ronaldo. And they even secured hosting rights for the 2034 FIFA Men's World Cup. Now they own EA — the company behind FIFA, Madden, and some of the most popular sports franchises in the world. Why? Because the future of sports isn’t just on the field. It’s in gaming, streaming, esports, media rights, and digital communities. And EA is the bridge. This deal is a Trojan Horse — a way for Saudi Arabia to embed itself at the center of how billions of people consume sports. If successful, it could reshape the global sports industry for decades. So for today's newsletter, I broke down: • How Saudi Arabia financed the deal • Why falling oil prices pushed them into this strategy • What fans can expect from EA in the future • And how this fits into their real ambition with Vision 2030 This is a fascinating topic (even if you don't like sports). Read the full breakdown here: https://lnkd.in/e62kFDsM #sports #sportsbiz #linkedinsports