In today’s hospitals, the environment is one of constant activity: devices beep, phones buzz, computers whir, and machines ring. In a system that never sleeps, 24-hour connectivity is a necessity. For Andrew Smith, VP of digital services at West Suffolk NHS Trust, the priorities are evident: speed, clarity, and efficiency. “We like AI because it […]
In today’s hospitals, the environment is one of constant activity: devices beep, phones buzz, computers whir, and machines ring. In a system that never sleeps, 24-hour connectivity is a necessity.
For Andrew Smith, VP of digital services at West Suffolk NHS Trust, the priorities are evident: speed, clarity, and efficiency. “We like AI because it does things straight away,” he says. “It’s like it just makes the magic happen.”
Smith’s team recently upgraded their long-standing partnership with network, hardware and software firm, Extreme Networks, to include its new AI-driven platform, Platform One. He says that adding the platform has allowed him to have a “police helicopter view” of the network.
“We like AI because it does things straight away,” he says. Smith points to staffing limitations: there are fewer engineers than needed – a problem common in the NHS – and more technology is used by clinicians. “We’ve got the source here,” he says. “We just need to empower our teams to fix things quicker for our clinicians.”
More views, less noise
A key benefit of Platform One, Smith explains, is its ability to visualise complex systems in an accessible format. The platform provides visibility into network assets, alerts, lifecycle stages, and end-of-support timelines – information that allows teams to act quickly and effectively.
“Each morning, my engineers begin their shifts by logging into the dashboard over a coffee,” Smith said. “We can immediately zoom in and see what’s going on—like you would scroll your phone.”
This level of accessibility benefits non-technical teams as well. The finance department, for example, can review colour-coded diagrams that clearly outline the “three Ws”: “what, where and why,” allowing for faster approvals and more informed purchasing decisions.
Behind the interface lies a new approach to enterprise AI. Markus Nispel, CTO EMEA and head of AI engineering at Extreme Networks, says the goal was to avoid the staged, superficial AI demos common in the industry. “We didn’t want a mock-up,” he explains. “Those are actual agents doing something in real time.”
He adds that feedback from employees, partners and early customers like Smith helped shape the product. “We gamified the engagement during development,” Nispel said. “We had over 70,000 engagements – that helped us improve the accuracy and product maturity.”
Security by design
Smith is also concerned with cybersecurity As threats grow more complex, built-in protections are becoming increasingly essential. “The things that keep you up at night are the things that might happen,” he says. “This helps us visualise it, bring it home, and share with the rest of the team.”
Rather than leaving potential threats obscured, Smith says the platform helps bring vulnerabilities into view, making them easier to understand and address.
Nispel claims that this kind of visibility is a leap forward, particularly for existing wired and wireless infrastructure customers. “They lacked visibility into the entirety of the fabric,” he says. “This is something that really helps them understand what’s happening, especially in fabric and wireless environments where troubleshooting is traditionally complex.”
Plus, the platform consolidates key functions, WiFi, LAN, and fabric data, into a single interface, giving users complete control. “You can look at the colours, see what’s important, and drill down,” Smith says. “We’re changing from hours to minutes, minutes to seconds.”
The potential of self-healing networks
The platform’s AI-driven self-healing capabilities are another area of interest for Smith. These features not only help identify issues but can sometimes resolve them automatically. For teams under pressure, this provides additional support.
Smith also sees potential for AI to function as a training aid. By guiding junior engineers through troubleshooting in real time, the platform could help accelerate their development. “It becomes like a trusted advisor,” he says.
He sees a future where AI reduces onboarding costs, training overhead, and reliance on separate learning systems. “It can help junior engineers stop being junior a little bit quicker,” he adds.
According to Nispel, this is part of a broader trend in AI usage. “There are studies showing that even junior help desk personnel make a huge leap using AI in their first days and weeks,” he says. “And the good engineers? They get even better because they know how to leverage the system.”
A 47-minute migration
Smith’s experience with the system migration was particularly notable for its speed. “It could’ve been even quicker,” he says, pointing to a few data formatting issues, “but it was seamless.”
While similar migrations often take weeks, this transition was completed in just 47 minutes. Extreme Networks explains that early deployments like Smith’s have helped them optimise the process further. “The goal,” says Nabil Bukhari, CTPO, Extreme Networks, “is to get from 47 minutes to 47 seconds.”
The future, says Nispel, lies in agent collaboration – both within the platform and across other systems. “Next year we’ll see multi-agent architectures not only automating tasks but working across platforms – ticketing, security, customer service,” he says.
