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Is it one rule for AI? Or no rules at all? Trump's new "ONE RULE" AI Executive Order is wiping out most state-level AI laws. The rationale? Reduce compliance burden, speed innovation, beat China. States passed have passed ~100 AI laws covering deepfakes, hiring discrimination, biometric privacy. The replacement is voluntary standards and liability shields for AI companies. On Lanai’s blog I break down why this creates a strange dynamic for global companies: You'll still need to build to EU AI Act standards (the strictest regime), then back-map compliance narratives to the more permissive U.S. environment. (Check out link to my full piece in the comments.) The strategic question becomes: Which system produces trustworthy, globally acceptable AI. And who pays the cost? Speed vs. oversight. Centralization vs. protection. The next phase of AI governance is here.

How do you measure AI transformation? Watch Lexi Reese and Xero CIO Fred Laury discuss the one metric that actually matters.

What’s the one metric that reveals real AI impact? Watch Lexi Reese, CEO of Lanai, and Xero CIO Fred Laury break it down.

The The Wall Street Journal highlights the ways AI is shaping reorgs, a trend we're likely to see more of ... but here's a more complete picture of how companies are actually restructuring around AI. I see four key changes happening right now (some of which the Journal covered): 1️⃣ 𝗙𝗹𝗮𝘁𝘁𝗲𝗻𝗶𝗻𝗴 𝗵𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗲𝘀: We see companies eliminating middle managers as AI handles routine tasks, creating "barbell" structures with leaders managing 50+ direct reports and more self-organizing teams. 2️⃣ 𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗔𝗜-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗿𝗼𝗹𝗲𝘀: New C-suite positions are emerging (like Chief AI Officers), as well as things like AI Centers of Excellence, hiring prompt engineers, AI ethicists, and specialized product managers focused entirely on AI integration. 3️⃣ 𝗠𝗲𝗿𝗴𝗶𝗻𝗴 𝗱𝗲𝗽𝗮𝗿𝘁𝗺𝗲𝗻𝘁𝘀: AI is encouraging breaking silos — for example, Moderna merged HR and IT under one "Chief People and Digital Technology Officer" to better integrate human and AI capabilities. 4️⃣ 𝗕𝗿𝗼𝗮𝗱𝗲𝗿 𝗔𝗜 𝗮𝗰𝗰𝗲𝘀𝘀: Companies are upskilling existing workers, providing no-code AI tools, and making AI widely accessible rather than keeping it in tech teams. That will inevitably change team strucures. ⚠️ But here's the critical gap (that the Journal doesn't quite get to): All this restructuring assumes you can see and manage AI usage across your flattened, democratized organization. But most leaders can't. Check out the WSJ's coverage here: https://lnkd.in/gdypY5N3

Thx to Fortune for this!! Every technology era has its “system of record” moment. Splunk turned logs into signal. Datadog made cloud metrics visible. Snowflake unlocked data at scale. Now, we’re at the same moment with AI. Companies are investing billions, but most AI use is fragmented and invisible: 1️⃣ personal tools in work browsers or embedded in SaaS, 2️⃣ sanctioned enterprise apps and homegrown models, 3️⃣ emerging workflow “agents” acting across systems with company data. Lanai is making these interactions visible, governable, and scalable — turning noise into an operating system leaders can actually manage for ROI, risk, and adoption. 💡 2026 planning is here, and every board deck has “AI” in bold letters. The problem isn’t ambition — it’s visibility. If you don’t know how AI is being used, you can’t measure outcomes. And if you can’t measure them, you can’t manage them. That’s not hype. That’s operations. Read full piece linked in the comments below ⤵️

The real challenge in enterprise AI is understanding how people and AI work together every day (not adoption). Most organizations still treat AI like a tool, not a partner. But the ones seeing real ROI are mapping and scaling those human–AI interactions intentionally. This article breaks down how enterprises are turning shadow AI into systematic success, and what that means for leadership in 2026.