Ah yes, the well-known sneakers-to-AI infra pivot.
BUSINESS
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Three Keys for Companies to Master AI in 2026-2028
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My takeaway is simple: The next 2 to 3 years will reward companies that do 3 things well: → use AI internally, so leadership understands it firsthand
→ build with a clear path from pilot to industrialization
→ take control of their own models, data, and evaluation strategy -
Enterprise AI Success: Evaluation Over Model Selection
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Most enterprises do not have an AI model problem.
— Ronald van Loon (@Ronald_vanLoon) 15 avril 2026
They have an evaluation problem.
That was one of my biggest takeaways from my conversation with @karibriski from Nvidia and @Toucas from Mistral AI at GTC.
In the agentic era, the winners will not be the companies running the… pic.twitter.com/woUGWQp3MkMost enterprises do not have an AI model problem. They have an evaluation problem. That was one of my biggest takeaways from my conversation with @karibriski from Nvidia and @Toucas from Mistral AI at GTC. In the agentic era, the winners will not be the companies running the
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Running shoe company pivots to AI data centre operations strategy
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Why is everyone freaking out? Pivoting from running shoes to running AI data centres is such an obvious idea
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Transform OT Teams From Defenders to Co-Authors
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A strategy that transforms OT teams from defenders to co-authors changes everything. Their buy-in becomes ownership, their resistance becomes partnership.
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OT Veto: Quality Check for Manufacturing Data Strategy
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The OT Veto is the most important quality check your manufacturing data strategy will face.
— Lucian Fogoros (@fogoros) 15 avril 2026
It forces the essential question: does this respect production reality? Partner content with @InfluxDB. #influxdata_iiot #InfluxDB pic.twitter.com/6ewe6431HKThe OT Veto is the most important quality check your manufacturing data strategy will face.
It forces the essential question: does this respect production reality? Partner content with @InfluxDB
. #influxdata_iiot #InfluxDB -
AI Researchers Challenge Founder Understanding of AI Systems
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This #AI Researcher Says the People Building AI Don’t Understand How It Works. Here’s What Every Founder Needs to Know
by Daniel Robbins @Inc Learn more: https://
bit.ly/4cdqf1b #MachineLearning #ArtificialIntelligence #ML #MI -
Connected AI Systems Create Real Competitive Advantage
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The takeaway: AI doesn’t create advantage on its own
Connected AI does If your systems don’t talk to each other, your AI won’t either I break this down in detail in the video, including real use cases. Learn More: -
AI Tools Fragmentation Creates Data Silos in Enterprises
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I keep seeing the same pattern inside large orgs: → Marketing uses one AI tool
→ Sales relies on CRM AI
→ Devs use something completely different → Data is everywhere
→ Context is nowhere So what happens? Decisions require stitching together 5 systems
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Enterprise AI Fragmentation Slows Decision Making Adoption
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Every enterprise is “doing AI”…
— Ronald van Loon (@Ronald_vanLoon) 15 avril 2026
But almost none are actually operating with it.
The real problem isn’t adoption.
It’s fragmentation.
AI is everywhere, yet decisions are still slow.
Thanks to @Adapt for partnering with me on this, because this is exactly the problem they’re… pic.twitter.com/bxL2whZU0TEvery enterprise is “doing AI”… But almost none are actually operating with it. The real problem isn’t adoption.
It’s fragmentation. AI is everywhere, yet decisions are still slow. Thanks to @Adapt for partnering with me on this, because this is exactly the problem they’re