AI Has A Consumption Problem—And Your Organization Is Feeding It Poorly
The intelligence layer they build doesn’t just serve today’s AI tools; it becomes the solid foundation for whatever comes next.
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The intelligence layer they build doesn’t just serve today’s AI tools; it becomes the solid foundation for whatever comes next.
Abstract page for arXiv paper 2606.03363: EntSQL: A Benchmark for Grounding Text-to-SQL in Long-Context Enterprise Knowledge
Cloud has entered the league of transport, energy and telecommunications infrastructure.
Agentic AI will transform enterprise operations. I believe that. But the transformation will not start with the agent. It will start with the data the agent dep...
At a deeper level, AI has tended to be perceived as a technology deployment rather than an enterprise capability.
From data center backlash and new state regulations to shaky ROI and failed enterprise tools, the industry is entering a more skeptical phase.
What tasks do your employees dread that they have to repeat every day? This is where you can benefit most from agentic AI.
Engineering thinking can close the gap between AI experimentation and organization-wide execution.
As companies race to deploy agentic AI, a new consensus is forming around the gap between ambition and readiness. The technology is advancing. The
A framework to understand your firm’s AI transformation
It's time we stopped buying AI capability — and started committing to the outcomes it's supposed to produce.
There needs to be a solution for a control plane before corporations can scale autonomous agents from demos into live production.
Enterprise AI infrastructure demands more than retrieval, requiring DevOps expertise in inference, governance and scaling.
Data governance took most enterprises a decade to get right, and those that started late paid the price.
Organizations are confronting the growing gap between AI hype and measurable business impact. This is exposing major blind spots in governance, usage visibility...
Most companies have poured their energy into picking the right AI model. This is a reasonable question. But it is the wrong one to obsess over.
2,000+ AI-built corporate apps lacked access controls across 380,000 public assets, exposing sensitive data and increasing enterprise risk.
A platform is no longer defined only by how efficiently it stores or processes data but by how reliably it preserves meaning, control and trust.
Agentic AI demands efficient, always-on infrastructure redesign
Digital transformation and now artificial intelligence were supposed to make enterprise buying resemble consumer commerce: frictionless, automated and
Those who embed DI today won’t just respond faster—they’ll anticipate better, act smarter and preserve institutional wisdom before it walks out the door.
Learn how vertical AI models are leveraging AI factories to solve complex, industry-specific enterprise problems and challenges.
The role of chief technology officers is shifting toward becoming architects of an AI agent's competency.
What CTOs need to know about Python development trends in 2026. Discover 10 production-ready shifts, and a maturity readiness map.
Fragmented data pipelines create delays and errors in decision-making. Finance, sales and operations teams often rely on disconnected systems. Structure...
AI is supercharging cyber criminals' ability to deploy ransomware. What are large companies doing to lower the risk?
"These four questions are critical, yet only 2 of them are ever answered."