In our first “Cisco Unpacked” session, we explored three topics that keep showing up in real customer conversations: the acceleration of wireless refresh cycles, the emerging direction of quantum networking, and how Cisco is positioning networking as a foundational layer for AI – especially through practical, near-term use cases like AI-enabled contact centres.
1) Wireless is shifting from “commodity” to strategic capability
Wireless is no longer being treated as “just connectivity.” We’re seeing a clear shift toward wireless as a lever for employee productivity, operational efficiency, and better observability for IT teams.
- Device refresh is forcing the conversation. Windows 11-driven laptop upgrades and modern device design (fewer built-in Ethernet ports) are pushing more endpoints onto Wi‑Fi—often without organisations realising how much this changes demand on the WLAN.
- Workstyles have changed the coverage requirement. Hybrid work, hot-desking, and “work anywhere in the office” behaviour mean Wi‑Fi performance needs to be consistent across meeting rooms, quiet spaces, and shared areas—not just at fixed desks.
- Modern Wi‑Fi upgrades can trigger LAN readiness checks. Newer access points can be more power-hungry, so PoE capacity, switching uplinks, and internet breakout capacity often need validation alongside the WLAN design.
- Management and assurance expectations are rising. Beyond RF design and AP placement, organisations are increasingly focused on authentication, policy, and the day‑2 operational experience (visibility, troubleshooting, and user experience monitoring).
What good looks like: a wireless refresh plan that starts with outcomes (user experience, density, critical applications), validates the RF design, and confirms the wired underlay (power and uplinks) can support the new AP generation—rather than treating Wi‑Fi as a standalone swap-out.
2) Quantum networking: not immediate, but worth tracking now
Cisco recently discussed prototype work around a “universal” quantum switch. This is not something most organisations will deploy in the next 12–24 months—but it is a useful signal of where vendor R&D is heading, and what foundational challenges still need solving.
- Interoperability will be a defining problem. Today’s quantum systems can use different qubit technologies and encoding approaches. If quantum moves beyond single, isolated systems, we’ll need ways to connect heterogeneous environments.
- Networking must preserve the quantum state. In classical networking, packets can be buffered, copied, and retransmitted. Quantum networking introduces constraints where maintaining state fidelity becomes the core engineering challenge.
- Security timelines are tightening the conversation. Even before quantum is mainstream, organisations are already investing in post‑quantum thinking—particularly around cryptography resilience.
- Traditional infrastructure vendors are positioning early. Cisco’s involvement suggests quantum-era networking isn’t being left solely to compute vendors; networking innovation is part of the roadmap.
3) AI is a networking problem as much as a compute problem
We broke “AI” into three practical lenses: (1) AI-capable infrastructure across the campus/branch, (2) AI-focused data centre designs (built for dense GPU compute and power/cooling constraints), and (3) Cisco’s newer concept of a Secure AI Factory—developed in close alignment with NVIDIA to speed adoption through validated designs.
- GPU clusters demand “lossless” behaviour and extremely high throughput. When GPU time is expensive, the network’s job is to keep GPUs fed—minimising congestion and inefficiency between nodes.
- Network utilisation directly impacts AI job completion time. Even modest utilisation gains can translate into meaningful improvements in time-to-result and overall platform efficiency.
- Cisco is blending silicon strategies to meet AI needs. Alongside its own Silicon One roadmap, Cisco is also aligning with NVIDIA switching silicon in specific Secure AI Factory designs—prioritising outcomes and validated architectures over a one-size-fits-all hardware story.
- Validated designs reduce adoption friction. Pre‑approved reference architectures help organisations move from experimentation to repeatable deployments—especially where skills shortages exist.
- Edge AI is where many organisations will start. Hospitals, warehouses, and manufacturing sites often need low latency and local processing. Smaller AI-capable clusters at the edge can be a more realistic first step than building a dedicated “AI-only” data centre.
4) The most immediate AI ROI is showing up in customer experience (contact centre)
If there’s one AI area where organisations can trial, prove value, and scale quickly, it’s the contact centre. A useful distinction came up in our discussion: LLMs can improve performance, while agentic AI can change outcomes. Importantly, the direction of travel we see is augmentation—not wholesale replacement of human agents.
- “Intelligent front door” for simpler requests. Handle routine queries (status updates, bookings, rescheduling) via natural conversation across voice and digital channels—while enabling smooth handoff to a human agent when needed.
- Real-time agent assistance. Live transcription, recommended next actions, and knowledge surfacing based on the conversation context—reducing repetition and improving consistency.
- Better call handling through context awareness. If the system can detect what a customer has already tried, it can help agents avoid re-running basic scripts that frustrate customers.
- Automated after-call work. Summaries, action capture, and form population can reduce wrap-up time and free agents for higher-value interactions.
How Cisilion can help
- Wireless readiness & refresh: assess your current user experience, validate RF design, and confirm LAN/PoE and switching readiness – then design a right-sized upgrade path
- AI-ready networking & edge AI foundations: translate target AI use cases into infrastructure requirements (bandwidth, latency, segmentation, observability), and help you choose the right starting point – cloud experimentation, on‑prem/edge pilot, or validated reference architectures.
- Contact centre & customer experience with AI: identify high-value journeys, run a proof of value, and support rollout of AI augmentation (intelligent front door, real-time agent assist, and after-call automation) with clear success metrics.
- Roadmap & risk alignment: keep horizon topics on the agenda without distracting from near-term improvements – so today’s decisions don’t limit tomorrow’s options.
Whether you’re planning a wireless refresh, exploring AI infrastructure (from edge pilots to validated designs), or looking for the fastest path to measurable AI value in the contact centre, we can help you move from ideas to an actionable plan. If you’d like a workshop-style session, share your priorities (sites, timelines, constraints, and target outcomes) and we’ll propose next steps tailored to your environment.
Watch the full webinar on-demand here.