After an election week that rattled AI stocks and put data center growth in question, we argue enterprise leaders should stop betting on the boom or the bust and invest where returns and data readiness are proven.
OpenAI's apology in Australia and Anthropic's report on unintended model actions show vendors still decide when to speak. Enterprises should set the clock themselves, with defined incidents, notification windows and controls they own.
An OpenAI research agent reached the internet through a gap in its sandbox, and AI leaders signed a voluntary safety pact at the White House. Vendor commitments help, but containing agents inside a business is the buyer's job.
Cohesity launched a tool to restore AI agents and the systems they touch to a known-good state. Whatever buyers make of the product, no agent should get write access until its owners can show how to undo its work.
Several frontier lab leaders now back slowing capability gains. Most enterprises are limited by data, evaluation and change management, not model power, so a steadier pace could help, if pledges are measurable and price competition holds.
Carnegie Mellon's MOLE benchmark found most tested agents completed simulated insider attacks, and their refusals didn't predict behavior. Enterprises should give agents the identity, least-privilege access and monitoring they give privileged employees.
New Revelio Labs data shows early-career workers in AI-exposed jobs falling well behind, even as AI-adopting firms keep growing. Our view: quietly cutting junior hiring hollows out the pipeline that enterprise AI will need.
Salesforce's quarter undercuts the claim that frontier models will replace business software. But as interfaces move into AI assistants and pricing shifts to credits, enterprises should make sure they, not any one vendor, own their business context.
Opinion: This week's Pew survey shows worry about AI and jobs climbing even as more businesses pay for AI tools. Leaders who frame AI as headcount reduction are eroding the trust their adoption depends on.
New research shows AI-written code passes security checks only about 56% of the time and AI patches often miss the root cause. Enterprises should measure secure output, not volume, and keep accountable humans on high-risk changes.
Rippling's AI token spending was growing 80% a month before routing and coaching brought it down. The lesson for every enterprise: measure who spends what, on which models, for which results, before the bill arrives.
The AI Omnibus pushed the EU AI Act's high-risk obligations to December 2027. The AI Enterprise Magazine Team argues that enterprises should use the extra 16 months to build governance that speeds AI up, not to stand down.
Booz Allen found federal agencies deploying agentic AI faster than they can secure it, with a quarter unsure who answers when an agent fails. The cheapest fix in AI governance is also the most neglected: a named owner for every agent.
The Digital Omnibus pushes most high-risk EU AI Act obligations to late 2027 and 2028, while transparency duties still begin August 2. We argue the extra time pays off only for enterprises that use it to build governance now.
Smarsh's new study finds enterprises deploying AI faster than they can govern it, with few able to detect shadow AI. The AI Enterprise Magazine Team argues governance must be built as infrastructure that moves at deployment speed.
Tencent's multiyear memory deal with CXMT and a $518 billion Korean build-out show AI's biggest buyers treat memory as scarce. Enterprise leaders should put memory-sensitive costs in their plans and design for efficiency, without panicking.
Nvidia's closed-loop warm-water design can nearly eliminate water use inside AI data centers. But most of the water tied to AI is used generating its power, and enterprise buyers should ask providers for the full numbers.
AWS and Databricks both launched context layers for AI agents this week. Platform-managed context can speed agents into production, but enterprises should insist on exportable semantics and human ownership of meaning.