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By Kasey Best, senior director of threat hunting, DNSFilter Rapid enterprise AI adoption has outpaced operational maturity. Many organizations are deploying AI because of market pressure rather than clearly defined business outcomes. There’s a growing proliferation of “AI products” that are little more than thin wrappers around existing large language models.

Workday, Inc. announced Workday AI Research, a dedicated technical research team focused on advancing reliable, trustworthy, and efficient AI for the enterprise. Workday AI Research findings help shape AI development at Workday and contribute new evidence, methods, and evaluation practices to the broader research community.

GitLab Inc. announced updates that give enterprises more control as they scale agentic software development. GitLab Dedicated customers, who already run their most sensitive software delivery workloads on GitLab, can now run GitLab Duo Agent Platform inside that same single tenant environment and region, connect their own models for inference, and keep AI-processed data inside their existing security boundary.

By Tina Martin of Ideaspired Small business owners often know their offers are strong, but their visuals don’t keep up with the pace of running the business. The tension is real: hiring a designer for every promo can feel out of reach, yet inconsistent graphics can dilute brand visual identity and weaken a business marketing strategy. AI promotional image creation changes that by turning modern digital design tools into a practical way to produce polished, recognizable promo images on demand.

Flexential, a leading provider of secure and flexible data center solutions, has established an $800 million credit facility to accelerate the development of new data center capacity in markets where the company’s customers are growing and demand continues to build. The financing will support more than 130 megawatts (MW) of new capacity across four key markets in Flexential’s national platform, providing a scalable source of capital to build ahead of enterprise and AI-driven infrastructure needs.

AI can move fast. Your infrastructure needs to be ready to move with it. As organizations push AI from pilots toward production, infrastructure decisions become increasingly strategic. AI workloads can demand high-performance compute, low-latency access, secure data handling, and rapid scalability. But not every workload has the same requirements, and not every environment is the right fit. That makes one question increasingly important: What should run where, and why?

Tricentis announced at its annual industry conference, Tricentis Transform, a lineup of new AI-powered innovations designed to help enterprises build, validate, and deliver high-quality software at scale.

NeuBird AI published the Earned Autonomy Framework, a set of open architectural principles defining how autonomous agents should earn, scope and operate within write access permissions in production environments. The framework is open for adoption, revision and co-signature by any operator, security leader or independent engineer who builds, buys or runs autonomous software that touches production systems.

NTT DATA and Palo Alto Networks announced a multi-year strategic alliance designed to help organizations securely adopt AI, modernize cybersecurity, simplify complex technology environments and build cyber resilience for the AI era.

Run a noisy-neighbor test sometime. Spin up a workload on shared cloud infrastructure, benchmark it at 2am, then benchmark the same workload at 2pm on a weekday. The gap between those two numbers, on identical specs, is the whole argument for bare metal in one experiment. Shared cloud environments run a hypervisor layer between your workload and the physical hardware underneath, and that layer isn’t free.