Smart manufacturing is no longer a future state. Most process and control operations already run IoT sensors, hybrid cloud platforms and AI-assisted analytics in production. What separates the plants that get value from those investments from the ones that stall is rarely the application layer. It is the network underneath.
The companion article in Process & Control magazine sets the broader context for why this matters. This piece is your operational checklist: the 10 things every manufacturer should know about real-time networks in 2026, with the data, decisions and definitions process and control leaders are asking about right now.
1. Real-time means deterministic, not just fast
Real-time in a manufacturing context does not mean low average latency. It means deterministic latency. Your network must guarantee a packet will arrive inside a known time window every time, not on average. A 5 ms link with occasional 200 ms jitter will break a closed-loop control system that a steady 10 ms link with no jitter would run cleanly.
For process and control engineers, this is the same principle as a deterministic fieldbus, applied to the wide-area network. It is also why best-effort internet, even fast best-effort internet, is the wrong substrate for industrial control traffic. Time-Sensitive Networking (TSN) standards under IEEE 802.1 are the formal framework vendors are now building toward.
2. Your plants are generating more data than your network was built to carry
IDC forecasts that connected IoT devices will generate 79.4 zettabytes of data globally in 2025, with the industrial and automotive category growing fastest at a 60% compound annual growth rate. Inside a single plant, that translates into individual machines producing gigabytes of telemetry every day, with a modern smart factory running hundreds of such systems.
AI workloads make the pressure worse. McKinsey’s State of AI 2025 reports that 88% of companies now use AI regularly in at least one function, up from 78% the year before. For manufacturers, that AI rides the same network as control traffic, telemetry, video analytics and security data. If your network was designed before that traffic existed, the bandwidth headroom you thought you had is already gone.
3. Hybrid and multi-cloud is the new default. Your network has to match.
The first wave of cloud adoption was about offloading everything possible to public providers. That has matured. Most manufacturers now run hybrid or multi-cloud, with workloads placed where they make operational and economic sense. Latency-sensitive control loops live at the edge. ERP and analytics sit in the cloud. Regulated data stays on-premises.
In Europe, GDPR and sovereignty initiatives like GAIA-X reinforce the case for keeping selected workloads inside national or regional boundaries. The implication for your network is direct. It has to span edge, on-premises and multiple clouds with consistent performance and a single security policy, not three different policy regimes glued together.
4. Edge AI has moved from pilot to production
The Siemens and NVIDIA partnership announced in 2025 is a useful marker. The two companies expanded their work to deliver AI-powered assistance for factory floor operations that runs on-premises, on industrial PCs built to withstand heat, dust and vibration while supporting AI-based robotics and predictive maintenance. Edge AI is no longer a slide. It is hardware on the floor.
“The future of smart manufacturing will be determined by who builds a secure, unified and resilient network fabric capable of enabling those innovations at scale.”
Richard Aspinall, SVP Enterprise Europe, GTT (Process & Control magazine, January 2026)
5. Legacy networks were not built for this level of interoperability
Most plant networks installed before 2020 were designed for a different traffic pattern: predictable flows between known endpoints inside the plant. Today the same network may need to carry video analytics, OPC UA traffic, ERP queries, security telemetry and cloud backup at once.
Three failure modes show up consistently. Incompatibility between OT and IT segments. Latency spikes under load. Bandwidth ceilings that throttle exactly the workloads your business is investing in. None of these are visible until the failure occurs in production.
6. Network performance is now a process metric
For the process and control community, this is the headline point. Network performance is no longer an IT metric in a separate dashboard. It is a process metric. If predictive maintenance data does not arrive in time, the maintenance window is missed. If a quality control video stream drops frames, the defect classifier under-reports. If a safety monitoring packet is delayed, your safety case is weakened.
Data integrity and output quality sit downstream of network determinism. Plants that treat the network as plant infrastructure, owned and reviewed alongside the PLCs, drives and DCS, see fewer of these surprises than plants that treat the network as IT overhead.
7. Security has to be designed in, not bolted on
Every new connected asset is a new attack surface. The current generation of attacks against manufacturing exploits the same convergence of IT and OT that enables smart manufacturing in the first place. Secure access service edge (SASE) architectures collapse networking and security into one policy fabric, which is the direction most large manufacturers are now moving.
The practical test is simple. Does your security policy follow the workload across edge, on-premises and cloud, or does each environment have its own rulebook? The latter is where most breaches start.
8. Network modernization pays for itself through avoided downtime
Siemens’ True Cost of Downtime 2024 report puts the cost of unplanned downtime in automotive at $2.3 million per hour, and finds that downtime now consumes 11% of annual revenue for the world’s 500 largest companies, totaling $1.4 trillion globally, up from $864 billion five years earlier. Across manufacturing sectors more broadly, Aberdeen Research puts the average cost of unplanned downtime at roughly $260,000 per hour.
Bring that math to your CFO. A network refresh that prevents even one major outage a year typically pays for itself inside 12 months. This is not a connectivity upgrade. It is an uptime investment.
9. Managed service partnerships are the default operating model
Few manufacturers carry the in-house network engineering depth to design, deploy and operate a globally consistent, low-latency, security-integrated fabric across 10 to 50 sites. That is why managed service providers have become the default operating model rather than the outsourced alternative. The right partner builds your system requirements, security posture and scalability into the design from day one, not as add-ons after deployment.
10. The competitive gap will widen through 2026 and 2027
Manufacturers that modernize their networks first will compound the advantage. Their AI initiatives ship faster because the data moves. Their predictive maintenance works because the telemetry is reliable. Their plants are easier to audit because security policy is consistent. Manufacturers that wait will spend the same money 18 months later under more pressure, while losing share to competitors who got there first.
This is the underlying message of the Process & Control article. The infrastructure gap is not a technical risk. It is a competitive one.
Summary: the 10 points at a glance
| # | What you should know | Why it matters in 2026 |
|---|---|---|
| 1 | Real-time means deterministic, not just fast | Closed-loop control needs guaranteed latency, not low-average latency |
| 2 | Your plants generate more data than your network was built to carry | IDC: 79.4 ZB of IoT data globally; industrial growing at 60% CAGR |
| 3 | Hybrid and multi-cloud is the new default | Your network must span edge, on-prem and cloud under one policy |
| 4 | Edge AI is in production, not pilot | Siemens and NVIDIA have moved AI inference onto the plant floor |
| 5 | Legacy networks were not built for current interoperability | Incompatibility, latency and bandwidth limits surface under load |
| 6 | Network performance is now a process metric | Data integrity and output quality sit downstream of network determinism |
| 7 | Security must be designed in, not bolted on | SASE collapses networking and security into one policy fabric |
| 8 | Modernization pays back through avoided downtime | Siemens: $260K/hour average; $2.3M/hour in automotive |
| 9 | Managed service partnerships are the default | Few manufacturers can run a global, low-latency fabric in-house |
| 10 | The competitive gap is widening | Early modernizers compound the advantage through 2026 and 2027 |
Who this article is for, and when it isn’t the right fit
Who this is for:
- Process and control engineers and managers responsible for plant uptime and data integrity
- Manufacturing CIOs and IT directors scoping a multi-site network refresh
- Operations leaders evaluating the business case for an SD-WAN, SASE or Cloud Connect program
- Plant managers planning IoT, edge AI or predictive maintenance rollouts
When this isn’t the right fit:
- Single-site, single-country operations with low data-volume workloads. A managed broadband service may be sufficient.
- Greenfield builds where the architecture decisions are still in the design phase. Start with a network architecture review rather than a procurement.
- Non-industrial environments. The determinism arguments here are specific to process and control workloads.
Frequently asked questions
What is a real-time manufacturing network?
A real-time manufacturing network is a low-latency, high-bandwidth, deterministic network fabric that connects industrial control systems, edge devices and cloud workloads with the predictability industrial processes require. It guarantees packet delivery inside a known time window every time, not just on average.
Why is deterministic latency more important than low latency in industrial networks?
Closed-loop control systems break under jitter, not under steady latency. A 10 ms link with no jitter is more useful in a process and control context than a 5 ms link with 200 ms spikes. Determinism, the guarantee that delay stays inside a known window, is what protects process stability.
What is the role of edge AI in smart manufacturing?
Edge AI runs inference close to the machine rather than in a remote cloud. In 2026, edge AI is in production for predictive maintenance, quality inspection and robotics. Partnerships such as Siemens with NVIDIA have moved AI-powered factory floor assistance onto purpose-built industrial PCs.
How does GDPR and GAIA-X affect manufacturing network design in Europe?
GDPR and initiatives like GAIA-X push European manufacturers toward sovereignty-aware architectures, where selected workloads stay inside national or regional boundaries. This rules out a pure public-cloud-only design and reinforces the case for hybrid and multi-cloud fabrics.
What does SASE add to a manufacturing network?
Secure Access Service Edge (SASE) collapses networking and security into one policy fabric that follows the workload across edge, on-premises and cloud. For manufacturers, that means consistent security policy across all sites, instead of separate rulesets for each environment.
What does a managed service provider do for a manufacturing network?
A managed service provider designs, deploys and operates the network on your behalf, including system requirements, security posture and scalability. For multi-site manufacturers, a managed service partner is now the default operating model rather than a fallback to in-house engineering.
How much data does a smart factory generate?
IDC forecasts 79.4 zettabytes of IoT data globally in 2025, with the industrial and automotive segment growing fastest at 60% CAGR. Inside a single plant, machines can each generate gigabytes of telemetry daily, and a smart factory may run hundreds of such systems concurrently.
What does manufacturing downtime cost?
Siemens’ True Cost of Downtime 2024 report puts the average cost at $260,000 per hour across manufacturing sectors, rising to $2.3 million per hour in automotive. Unplanned downtime now consumes 11% of annual revenue for the world’s 500 largest companies, totaling $1.4 trillion globally.
Where GTT fits
GTT helps global manufacturers connect, secure and simplify the network fabric that smart manufacturing depends on. Managed SD-WAN, SASE and Cloud Connect services run on the GTT Tier 1 backbone, with 75% of customer traffic staying on the GTT network for performance, control and security. The GTT Envision platform gives you predictive analytics and AI-driven orchestration across edge, on-premises and cloud, so you can see and act on what your network is doing in real time.
Ready to simplify your global manufacturing network? Talk to a GTT expert today.
Related GTT reading
- How GTT supports global manufacturers with managed SD-WAN, SASE and Cloud Connect.
- Customer story: Röchling Automotive. Global connectivity across 40 sites.
