Life Sciences leaders are presenting on modern SCE 5/12 @ Rev PHL Agenda: → @ucb_news on moving off legacy SCE w/out losing scope → Merck on maintaining auditability across SAS, R, and Python
→ @Novartis on lessons learned from an SCE journey RSVP: https://
hubs.ly/Q04dCNZj0
ENTERPRISE AI
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Life Sciences Leaders Share SCE Modernization Strategies
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Unified Namespace Evolves into Industrial Data Control Plane
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The unified namespace is evolving into the data control plane. Partner content with @HighbyteInc. Companies need applications to manage and control industrial data flows throughout their enterprise operations. #highbyte_iiot pic.twitter.com/KW1Pdcc68t
— Lucian Fogoros (@fogoros) 28 avril 2026The unified namespace is evolving into the data control plane. Partner content with @HighbyteInc
. Companies need applications to manage and control industrial data flows throughout their enterprise operations. #highbyte_iiot -
Efficiency-Driven Choices Compress Optionality With Strategic Costs
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Your parallel is relevant.
Efficiency-driven choices tend to compress optionality over time, and rebuilding it later comes at a higher cost, but with a very different strategic value. -
Google Cloud AI Hub launched in Visakhapatnam with $15B investment
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AI Patnam is born! With a $15B investment and a 1 GW hyperscale data centre, “AI Patnam” will drive innovation across sectors—from healthcare to defence. Union Minister Shri Ashwini Vaishnaw just presided over the groundbreaking of the Google Cloud AI Hub in Visakhapatnam.
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Building AI Foundations Before Scaling: Data Governance and Process
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The big takeaway for me: Before scaling AI, build the foundation. → Start with the business problem, not the model
→ Define the process, policies, and metrics
→ Govern access to certified data products
→ Keep humans in the loop to validate and monitor That is how you get -
AI Agents Data Alignment The Self-Service BI Paradox
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The clearest analogy is self-service BI. We democratized data.
We sped up access.
But we also created endless KPI debates. Why? → Different teams used different definitions
→ Different reports showed different numbers
→ Humans could still stop, argue, and align AI agents -
Context as Operating Model for AI Systems
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That is why “context” is not a soft concept. It is the operating model of the business, made usable for AI. In practice, that means: → What does this metric actually mean?
→ When should this rule apply?
→ Why does this policy exist?
→ What is this decision trying to -
Enterprise AI Fails Without Business Context, Not Weak Models
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Most enterprise AI does not fail because the model is weak.
— Ronald van Loon (@Ronald_vanLoon) 28 avril 2026
It fails because the business context is missing.
That is the real bottleneck, and it gets worse as companies move from one copilot to hundreds of autonomous agents.
I unpacked this with Teresa Rojas & Tom Dejonghe… pic.twitter.com/CX39TQn4SiMost enterprise AI does not fail because the model is weak. It fails because the business context is missing. That is the real bottleneck, and it gets worse as companies move from one copilot to hundreds of autonomous agents. I unpacked this with Teresa Rojas & Tom Dejonghe
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Connectivity as Business Infrastructure: Adaptive Performance and Security
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Connectivity is still treated as a convenience, but many business decisions now depend on stable performance everywhere. Combining adaptive performance and security helps turn it into a reliable working environment. @TMobileBusiness
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AIKosh Platform Launches Major Updates for AI Innovation
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Big updates are here! AIKosh just got more powerful with new features, making data contribution, analysis, and innovation smoother than ever.
Whether you're a researcher, policymaker, or builder – AIKosh is evolving to work smarter for you. Discover what’s shaping AI on AIKosh