The next phase of manufacturing AI depends on the network

Manufacturers need resilient connectivity to turn early AI momentum into lasting advantage.

The manufacturing sector is advancing rapidly. Sensors track the condition of equipment, computer vision helps identify quality issues, and AI analyses patterns from operational data that would be difficult for people to spot. These capabilities are moving beyond isolated trials and becoming part of how plants are monitored, maintained, and improved.

According to Expereo’s Enterprise Horizons 2026 data, 54% of manufacturing businesses in APAC said AI or machine learning would be a technology investment priority over the next 12 months, whilst 36% said their organisations already use AI extensively or in transformative ways.

This shows clear ambition, but investment alone will not create a long-term advantage. The infrastructure needed to prove an AI use case in one location is very different from what is required to operate it consistently across plants, countries, and cloud environments. As AI adoption expands across multiple plants, the quality of the network increasingly shapes the quality of the outcome.

Scaling changes the infrastructure question

Scaling AI in manufacturing is not simply a matter of deploying the same model at more sites. Its value depends on bringing together signals from machinery, production systems, quality processes, and supply-chain operations to create a dependable picture of what is happening across the business. If that information is delayed, fragmented, or inconsistent between plants, AI can end up responding to yesterday’s conditions rather than in real time.

The network connecting plants, data centres, and cloud platforms is separate from the control systems that run machinery on the factory floor. The challenge is ensuring this wider network can move operational data reliably, without delays, losses, or blind spots.

Once operational data leaves the factory floor, network inconsistency can affect everything built around production. Information may reach central systems late or incomplete. Comparisons between sites become less dependable, whilst remote operations and expert support can deteriorate when they are needed most.

The effect is rarely a dramatic outage. The plant continues to operate, but models work from a less complete picture and automated recommendations become less reliable. The value of AI gradually declines without the network necessarily being recognised as the cause.

Manufacturing AI rests on uneven foundations

That is where network readiness becomes critical. Expereo's research points to a gap that should concern manufacturers seeking to scale AI across APAC. Only 14% of the region's manufacturing and distribution respondents described their network as fully ready to support new technology initiatives. A further 42% said it was mostly ready but had some gaps, whilst 44% said it was either not ready or could support current requirements but would soon need to be upgraded or replaced.

Growing interest in edge computing makes this more urgent. Edge computing allows manufacturers to process data closer to the machinery producing it, but it does not remove the need for the wider network. Models, updates, and operational insights still need to move reliably between plants, data centres, and cloud platforms. Forty-six percent of respondents said they would prioritise edge computing over the next 12 months, compared with 30% across all APAC industries and 29% amongst manufacturing and distribution organisations globally. 

Manufacturing networks have usually developed in step with the business. New plants, acquisitions, and markets bring additional carriers, contracts, and architectures. Over time, this creates an estate in which performance, visibility, and support vary by location. When a problem crosses several provider networks, each party may see only its own part of the journey.

That challenge is amplified by the region's diversity. Levels of digital maturity differ, as do requirements around data localisation and cross-border flows. Supply chains are also under pressure: 64% of the manufacturing and distribution technology leaders surveyed in APAC rate supply-chain disruption as a big or huge threat to business strategy over the next 12 months, compared with 39% of APAC technology leaders across all industries. Manufacturers must be able to adjust where workloads run and how data moves without losing visibility or control.

AI performance depends on the infrastructure beneath it

For manufacturers, these readiness gaps matter because AI applications do not operate independently of the infrastructure beneath them. As more processing moves to the edge and more operational data travels between plants and platforms, inconsistent connectivity can translate directly into incomplete information, slower decisions, and less dependable outcomes.

Addressing them takes more than adding bandwidth. Manufacturers need predictable performance between sites and cloud platforms, resilience designed around the importance of each workload, and visibility that reveals degradation before it begins to affect operations. They also need to decide which applications require low latency, where edge processing is appropriate, and how traffic should be rerouted when a path fails.

This calls for connectivity to be treated as part of the production environment, with clear accountability for end-to-end performance across locations and providers. Expereo helps multinational organisations create that consistency across their global estates, giving technology teams greater visibility and control over the networks supporting their operations.

Manufacturers now have an opportunity to convert this early AI momentum into a sustainable advantage. Doing so will depend on their ability to move from promising individual deployments to repeatable capabilities across every plant. AI may create the intelligence, but the network determines whether it can be used wherever the business needs it.