Monthly Archives: July 2026

Oracle expands Cloud@Customer with new database service for mid-sized workloads

Oracle is expanding its Cloud@Customer on-premises portfolio with a managed database offering that it says will enable enterprises to run databases, applications, and AI agents in their own data centers, helping CIOs modernize mid-sized workloads while meeting data residency, regulatory, and low-latency requirements. The hybrid cloud offering, Base Database Cloud@Customer, combines existing database and infrastructure…

CyCognito Brings Always-On AI Pentesting to External Attack Surface Management

CyCognito, a leading exposure management platform, today introduced Continuous AI Pentesting. The new capability bakes AI-driven offensive pentesting directly into the platform, leveraging the rich context it already maintains for every exposed asset. This enables CyCognito to deliver AI pentesting as a continuous service, circumventing the cost and coverage constraints that confine comparable solutions to…

The $3 trillion assembly line: Why CIOs must industrialize the data center supply chain

You are one of the six billion people (75% of the world population) online today, and every click you make is routed through the data center. Data centers, whether knowingly or unknowingly, play a very critical role in your daily online activities. With an increasing population, increasing usage of online presence, and now omniscient AI,…

How a contextual AI fabric turns organizational memory into AI advantage

Across industries, a version of the same conversation is playing out in technology leadership meetings. Enterprises have deployed AI broadly, and foundation models keep getting more capable. Yet the outputs still feel generic, shaped by industry patterns rather than by the organization producing them. McKinsey’s AI Trust Maturity Survey found that while overall AI maturity…

The engineering bottleneck has changed. Is your org prepared?

AI agents can turn a clear description into working software, the engineer’s judgement is what makes the difference: deciding what to build, catching the tradeoff the agent didn’t know to weigh, and owning the call on whether the result is right. That judgement has always been the hard part of engineering. It just used to…

The compound effect your AI adoption strategy is missing

For many engineering teams, AI adoption means individual engineers write code faster while overall team velocity remains stagnant. Individual speed and team speed are produced by different things, and AI has mostly accelerated the first but not the second. The step from individual AI adoption to team advantage is one many organizations haven’t taken yet,…

Leadership bottlenecks slow AI adoption

At Cisco, VP of engineering Jason Andrews deals with all the same technical issues as every other company deploying AI, including ensuring it’s governed, secure, and integrating multiple data sources, legacy systems, and AI models. But these issues are relatively straightforward compared to the bigger challenges relating to the fast pace of change, specifically how…

The AI bill is the easy part. The hard part is everything it changed

Your CFO has a simple question. “We’re spending more on AI. What are we getting for it?” Most CIOs cannot answer it — not because AI isn’t creating value, but because the accounting systems we inherited were built before AI existed as a category of labor. This June, the conversation shifted from token maxing to…

4 recs for CIOs to optimize AI budgets and improve sustainability

In the client-server era, the penalty for inefficient programming, such as unoptimized database calls, was largely confined to application responsiveness. Today, in the AI era, code, architectural, and platform inefficiencies are no longer just a performance issue, they’re a financial and environmental liability. Left unchecked, poor code cascades into soaring token costs and spikes data…

From outsourcing to ownership: How we brought development in-house without breaking delivery

Outsourcing worked – until it didn’t. After Akirolabs achieved early market validation and onboarded its first enterprise customers, outsourcing began to create strategic limitations around scalability, intellectual property (IP) ownership, security and delivery execution. The challenges started after the first enterprise customers confirmed product-market fit. At that point, delivery speed became directly tied to business…