Today: there were already 10,000 vendors trying to help companies build AI agents and enterprise-grade coding assistants, and now there are 10,001, Oracle's cloud AI growth has come at a price, and the latest funding rounds in enterprise tech.
Any business that survived the digital revolution is very familiar with how quickly networking requirements can shift. But few are prepared for the speed with which emerging technologies today are challenging existing environments.
Today on Product Saturday: former OpenAI CTO Mira Murati's new company introduces its first product, DeepSeek drops another low-cost model on the market, and the quote of the week.
Any business that survived the digital revolution is very familiar with how quickly networking requirements can shift. But few are prepared for the speed with which emerging technologies today are challenging existing environments.
The demands on enterprise infrastructure are rapidly changing. Networks today were built with different requirements in mind, certainly not anticipating the wave of AI adoption we’re currently seeing, with AI agents contributing to explosive network traffic and strict requirements for low latency. In the near future, these agents will run at the edge and even on personal devices, meaning the stress on the network will be felt acutely in campus networks. The increase in traffic will in turn lead to intensifying security threats. So as companies quickly rebuild their IT foundations for the AI era, they need to start where it matters most: the network.
Any business that survived the digital revolution is very familiar with how quickly networking requirements can shift. But even with the crash course most got as the internet, mobile computing, and the cloud emerged, few are prepared for the speed with which emerging technologies today are challenging existing environments.
That’s because AI applications require high-performance throughput and low latency. Today’s networks can’t support these data-heavy workloads at the speed businesses need: across wired and wireless networks in offices, on factory floors, and other workplaces. In fact, 71% of organizations believe their current IT environments are not equipped for AI and machine learning workloads, according to an S&P Global survey. Now, under pressure from customers, investors, and employees to stay at tech’s cutting edge, businesses are moving with an unprecedented sense of urgency to update their IT landscapes.
From switches and routers to the software delivering a secure network, the agentic AI stack is emerging in real time around three fundamental principles: hardware designed for high-throughput AI workloads; security embedded into everything; and AI to simplify complex IT operations for internal teams. Without more capable infrastructure, businesses will struggle with speed, security, and performance. It’s why a whopping 97% of companies said a modern network was critical to AI, cloud, and IoT adoption, per a Cisco survey.
And as the use of AI agents grows, businesses can no longer expect to rely solely on their human talent. Increasingly, AI-powered capabilities like autonomous diagnosis and resolution across domains are table stakes. This pivot beyond AIOps to the emerging practice of AgenticOps will unlock greater speed and precision across networking, security, and application domains.
“In a world where adoption of generative AI is mainstream, human interaction becomes even more critical,” Cisco Senior Vice President Anurag Dhingra wrote in a recent blog. “AI not only reinforces the importance of collaboration, it also puts huge demands on networks. Taken together, this requires a fundamental rethinking of infrastructure to build a future-proofed workplace.”
As an integral part of many of the IT transformations that have taken place over the past 40 years, Cisco knows better than most vendors the challenges enterprises face in keeping up with emerging technology. And as organizations rush to upgrade their infrastructure, Cisco, which already powers 95% of Fortune 500 networks and serving every major industry, has rolled out an entirely new secure network architecture that is purpose-built to meet the urgent needs of businesses today:
By now, it’s a familiar pattern for enterprises: New technology emerges, requiring more powerful, resilient, and secure infrastructure. But unlike past overhauls that pushed IT teams to the brink, AI can also dramatically simplify the management of these increasingly complex networks.
The speed with which Cisco and others are rolling out new hardware and implanting the technology within their ecosystems is evidence of AI’s early impact in the enterprise. And the demand from businesses indicates that turning AI hype into sustainable, long-term value starts at the infrastructure layer.
Increasingly, in-house technology leaders are tasked with the seemingly impossible mandate of reaping the benefits of next-generation systems while simultaneously reducing legacy technical debt and costs and managing risk.
