Enterprise networking has hit a turning point — it’s no longer judged by port speeds but by how well it keeps artificial intelligence running across clouds, data centers and the edge. As MWC Barcelona 2026 approaches under the banner of “The IQ Era,” the transition is unmistakable: Intelligence is no longer layered on top of ...

The hyperconverged edge is where AI factories collide with wireless networks. That shift is forcing telecom infrastructure, enterprise networking and compute architecture to converge in ways the industry has long discussed but rarely executed. Nvidia’s push to extend artificial intelligence beyond centralized data centers is accelerating that change, and companies such as Veea Inc. are ...

The Nvidia ecosystem is quickly becoming the control plane for AI infrastructure. The shift isn’t just about GPUs anymore. As enterprises move from experimentation to scaled deployment, the market is consolidating around a standardized stack where Linux and Kubernetes integrate tightly with Nvidia hardware, and the Red Hat–Nvidia partnership reflects customers’ push for repeatable AI ...

The autonomous enterprise is taking shape as companies move from AI that informs to systems that actually act. Across the enterprise market, the question around AI has shifted from “does this work?” to “does this actually move the business forward?” That evolution is playing out in partnerships throughout the enterprise, such as the one between ...

Across large enterprises, AI adoption is shifting from experimentation to operational pressure, with customer service often emerging as the first system that must work at scale. The difference between pilots and production is no longer theoretical — it shows up in resolution rates, satisfaction scores and revenue impact. Zendesk Inc., a customer service software company, sits ...

Metadata management has become the practical dividing line between AI systems that scale and those that stall. As organizations push AI from experimentation into sustained production, the limiting factor is no longer models, but visibility into sprawling data estates. Survey results and field experience from companies such as HighFens Inc. show that without usable metadata, ...

Unstructured data is now the constraint shaping how far artificial intelligence platforms can realistically scale. Enterprises are struggling to scale AI because unstructured data pipelines can’t deliver the latency, throughput and consistency inference workloads demand. Hewlett Packard Enterprise Co. is seeing this pressure firsthand as customers push to keep GPUs busy and simplify how unstructured ...

Hybrid cloud is no longer just an infrastructure compromise — it’s increasingly the execution layer that determines whether enterprise artificial intelligence can move from promise to production. As AI moves into production, hybrid cloud strategies are being reshaped by the realities of inference, distributed data and where GPUs actually live. Software solutions providers such as ...

Ernst & Young Global LLP is seeing a clear split emerge as artificial intelligence moves deeper into the enterprise, with organizations either bolting AI onto existing processes or committing to an AI-native rethink of how work and decisions get done. Instead of layering intelligence onto legacy workflows, companies are beginning to pull those workflows apart ...

The conversation around enterprise transformation is shifting as organizations reckon with what it takes to turn artificial intelligence into something that delivers lasting, operational impact, with trusted AI emerging as the real dividing line between experimentation and scale. What’s changing isn’t the technology itself, but the expectations around confidence, accountability and everyday use. As leaders ...