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A couple of months ago I was at Siemens Realize LIVE in Amsterdam, and over dinner at the media and analyst reception, a conversation with Tony Hemmelgarn, CEO of Siemens Digital Industries, took a turn I did not expect. From industrial AI to why a Delhi intersection, and a UK roundabout arrive at the same result through completely opposite means.

Somewhere in there the UK driving exam came up too (that thing is brutal!). Tony had lived in the UK for a while himself, so he knew exactly what that experience is like. Stay with me, it made more sense than it sounds.

I grew up in India and have lived in the UK for several years now, which means I have spent enough time driving in both places to appreciate just how different they really are. I also happened to be back home in Delhi this summer for the holidays.

If you have driven in Delhi, you know exactly what I mean. There is a kind of ‘beautiful chaos’ to it. Cars, bikes, buses, auto-rickshaws, pedestrians, delivery guys on scooters, all making their own decisions at the same time, somehow without a plan. There are traffic lights and lane markings, sure, but anyone who has actually spent time on those roads knows the rulebook is only half the story. You are reading the situation constantly. A gap opens, someone takes it. Someone else has already anticipated that. A scooter appears out of nowhere on your left. You adjust. And somehow, everyone just keeps moving.

Now put that next to driving in the UK. Almost comically different. Everything is explicit here. Lane discipline. Signalling. Right of way. Mirrors, always the mirrors. Anyone who has sat the UK driving test knows that knowing how to drive and proving you can drive by the book are two very different things. It took me more than one test (meh!) to pass the exam. Unlearning Delhi’s driving and then learning the UK’s is not a joke.

The UK runs on predictability and rules. Delhi has rules too, but what really keeps things moving is interpretation, anticipation, and a kind of collective improvisation. Being neck deep in all things ‘industrial AI’, I couldn’t stop seeing the parallel, because the real question underneath both is who’s in charge when the rulebook and the improvisation are happening at the same time. That’s basically the orchestration problem, just with more scooters!

What Happens When Rules Fail: The Real-World Orchestration Problem

My last blog was about why the context layer is becoming such a big battleground in industrial AI. The next part to this is what happens after a system actually has that context. Knowing what is going on is one thing. Deciding what to do about it, especially when nobody has seen the situation before, is a different problem entirely.

For decades, industrial automation has been really good at that second part, as long as we can write the rules down. If X happens, do Y. Temperature crosses a threshold, trigger the alert. Machine stops, stop the line. Inventory drops below a level, reorder. That is honestly why automation has worked so well for so long. We take messy physical processes and turn them into decisions that repeat the same way every time. Nothing wrong with that at all, when the world is predictable, rules are genuinely powerful.

But what happens when the world is not predictable?

A machine starts behaving differently because the material batch changed. A supplier is late. Energy prices spike overnight. A production order gets rewritten at the last minute. An operator does something slightly different because they know something the system was never told. Or, my personal favourite, three unrelated things go wrong at exactly the same moment, and now it is not one system’s problem to solve. It is a question of who is in charge, and who decides.

Usually, the problem is not that the system does not have enough data. It might be drowning in it. The problem is that nobody wrote this particular situation into the rulebook, because nobody thought to.

Scaling AI Adoption: Why Most Pilots Don’t Translate to Production

Scaling AI, automation, and digital transformation is now the single biggest business priority for manufacturers globally, ahead of cutting costs, ahead of innovation, ahead of supply chain resilience from IDC’s own Worldwide Manufacturing 2026 Survey. And yet only around one in five manufacturers have actually gotten AI to scaled production use across multiple workflows or sites. Most are still piloting, or stuck running it in a limited corner somewhere. That gap between what people say they want and what is actually running on the floor is, in my mind, that same problem playing out at scale.

Blending Rules and Improvisation: A New Model for Industrial AI

We tend to talk about AI in manufacturing through use cases. Predictive maintenance. Quality inspection. Scheduling. Forecasting. All fine, all useful, but underneath all of that sits a much simpler question: can a system make a good call when the situation was never anticipated in the first place? That, to me, is really the line between automation and autonomy. Automation runs a known response. Autonomy has to read a situation, weigh a few options, and decide what happens next.

This is where the traffic thing earns its place, I think. Delhi has not beaten the UK at this. The UK has not beaten Delhi either. Both systems genuinely work, they just carry complexity differently. The UK gives industrial AI something it cannot do without: rules, constraints, predictability. You do not want an agent treating a safety requirement as optional, or skipping a maintenance step because it spotted a quicker path. Delhi gives you the other half of the lesson, which is that the real world does not always follow the plan, and when it does not, something needs to be able to interpret and adapt in the moment.

Traditional automation asks what should happen when a condition occurs. AI lets us ask something harder: given everything happening right now, what should happen? The first question can be programmed in an afternoon. The second needs judgement, including knowing when to ask a human. It also needs an understanding that the locally smart move can quietly create a bottleneck or a maintenance headache somewhere else entirely.

The decision stops being about one machine or one KPI. It becomes about the whole system, which is probably why I think the next real fight after the context layer is orchestration, agents working across ERP, MES, EAM, supply chain and energy together, not just sitting inside one application doing their own thing.

So maybe the future of industrial AI needs a bit of both: the UK’s rules and Delhi’s improvisation, with AI doing the very unglamorous job of figuring out which one a moment is asking for, across every system involved, not just the one closest to the problem.

The Path Forward

Underneath all of this sits a broader challenge: how intelligence works across systems, processes, people, and increasingly other agents. That’s orchestration. And as industrial AI moves from individual use cases into day-to-day operations, it is becoming harder to ignore.

For manufacturers, I think this changes the discussion slightly. Most organisations already have plenty of AI ideas, pilots, and use cases. What feels less clear is what happens when those systems need to work together. How does an AI-driven decision in one part of the operation affect everything else that sits downstream? Who is coordinating across ERP, MES, EAM, supply chain, and energy systems? And when things do not go according to plan, how does the system decide whether to act on its own or pull a human into the loop?

Those feel like the more interesting questions because they are not really about AI models at all. They are about how intelligence, whether human or machine, is orchestrated across the operation. And that may be one reason why so many organisations still struggle to move from promising pilots to something that operates at scale.

Contact our experts to explore how industrial AI orchestration applies to your manufacturing strategy.

Gunjan Bassi

Gunjan Bassi - Research Manager

Analyst Profile
Gunjan Bassi has more than 14 years' experience working in the logistics and transportation sector. Before joining IDC, she worked with Transport Intelligence (Ti), a transportation and logistics research firm based in Bath, England, where she was responsible for vertical…

On September 9th, at Connected Britain 2026 in London, I moderated a panel discussion that highlighted a clear evolution in the industrial 5G market, with enterprise interest moving beyond validation toward a more disciplined assessment of where 5G drives meaningful operational performance.

As panelists discussed deployment economics, and what’s needed to achieve scalability beyond individual use cases, three key themes emerged:

#1 – The business requirement is what determines the connectivity architecture

Industrial environments have a growing range of connectivity options; therefore, the appropriate model depends on the operational characteristics of the use case.

During the panel, mobility, predictable performance, security, resilience, coverage, and low latency emerged as important factors strengthening the case for private 5G. Mike Lewis, Market Advisor at Enterprise Ireland, emphasized the importance of starting with the business context and identifying where these characteristics address a specific operational requirement. Manufacturing, transport, and logistics, where mobility and automation are premiums, can provide a stronger rationale where network performance directly affects operational processes.

At the same time, the market is moving beyond a binary distinction between public and private networks. Alessandro Bovone, Chief Information & Technology Officer at JT Global linked this evolution to the broader issue of enterprise sovereignty. In this context, sovereignty extends beyond ownership of dedicated infrastructure to encompass control over connectivity, security, resilience, data flows, and the delivery of network capabilities across enterprise operating environments.

The strategic implication is that enterprises should define the operational outcome, sovereignty requirements, and required service characteristics first, and subsequently determine whether private, public, hybrid, or multi-technology connectivity provides the appropriate architecture.

#2 – Operational outcomes are becoming the basis of the business case

Damian Cross, Head of Technology Automation at Peel Ports Group provided a strong example of how private 5G can translate network performance into measurable operational value. Under its previous Wi-Fi architecture, connectivity interruptions as equipment moved between access points could affect up to 15% of operations. The organization therefore established a private 5G proof of concept covering approximately 80 acres and initially connected four types of operational equipment with different throughput and latency requirements.

The trial separated technical validation from business-value assessment. Within two weeks, network and equipment KPIs showed that the required connectivity performance had been achieved. Over the subsequent three-month analysis period, Peel Ports identified that, under its previous Wi-Fi environment, approximately 49% of equipment movements were unproductive. Improved connectivity then supported its “global pooling” operating model, enabling container movements to be assigned dynamically to reduce unnecessary travel and improve equipment productivity.

The infrastructure economics were also significant. Peel Ports reported that nearly 80 Wi-Fi access points could be replaced by four 5G radios across the operational environment, with a payback period of less than eight months. Once the network was established, it also supported additional applications including body-worn cameras, mobile CCTV, IoT devices, AI-enabled video analytics, and safety-related alerts.

For IDC, the Peel Ports example reinforces an important principle. The return on private 5G should not be assessed primarily through network metrics. It should be evaluated through the operational KPIs that the network enables. These can include equipment productivity, unproductive movement, process availability, downtime, safety, asset utilization, automation efficiency, and ultimately financial return.

For technology suppliers and service providers, this raises the commercial expectation. Connectivity capability needs to be translated into demonstrable operational and financial outcomes.

#3 – Scaling requires assurance, integration, and commercial flexibility

The discussion also highlighted why successful pilots do not automatically translate into large-scale deployments.

  • Technical assurance is becoming increasingly important. Enterprises need evidence that network performance can be sustained under real operating conditions, particularly where 5G supports operational technology or critical processes.
  • Legacy OT integration remains a material barrier. Industrial equipment was not necessarily designed for native 5G connectivity. Retrofitting machinery, integrating gateways, managing devices, and connecting existing applications can therefore represent a substantial part of the deployment effort and economics.
  • Commercial models need to reduce the risk of adoption. The growing interest in network-as-a-service, subscription-based, and lower-CapEx approaches that allow enterprises to begin with anchor use cases, demonstrate value, and subsequently expand.
  • Partnerships are equally important, particularly where delivering an end-to-end industrial solution requires connectivity, integration, devices, applications, and operational expertise from multiple ecosystem participants.

The industrial 5G market is entering a more mature phase in which technical capability alone will not determine adoption.

The panel discussion pointed to several requirements for broader scale including clearly defined business intent, measurable operational outcomes, technical assurance, suitable spectrum, integration with the existing operational environment, sovereignty requirements, and commercial models capable of supporting expansion.

The closing perspectives from the panel captured many of these priorities through a concise set of themes: outcomes, scalability, spectrum, transformation, intent, and assurance.

I see the next stage of industrial 5G will therefore be determined by the industry’s ability to translate successful implementations into repeatable, scalable, and commercially sustainable operating models.

Turning insight into action

For enterprises: Industrial 5G’s next phase is about measurable outcomes, not connectivity specs. If you’re planning a scaled deployment, whether you’re still building the business case, tackling legacy OT integration, or expanding beyond initial pilots, the operational context matters as much as the technology. We can help you define what success looks like and chart a path that’s right for your organisation.

For service providers and technology suppliers: This market shift toward outcomes-driven procurement is reshaping commercial expectations. Positioning your solution through operational KPIs rather than network performance alone is becoming the competitive imperative. Understanding how enterprises evaluate business value, structure commercial flexibility, and manage technical assurance can help you build stronger customer relationships and win larger deployments.

Want to discuss how this applies to your business? Contact our experts or get in touch directly.

Masarra Mohamed

Masarra Mohamed - Senior Research Analyst, Communications Platform as a Service

Analyst Profile
Masarra Mohamed is an expert in digital infrastructure, cloud, AI, and communications platform-as-a-service (CPaaS). She leads IDC’s global CPaaS research and advisory practice, shaping the firm’s perspective on API-driven communications, customer engagement platforms, and their convergence with contact centre and…

IT services buyers across the Middle East, Turkey and Africa (META region) are making big decisions right now about their technology partners. Their business models are under pressure to transform, their technology budgets are flowing toward AI and automation, and IT services vendor partnerships are being evaluated through a new lens. That lens is AI capability and it matters more than track record, security expertise, or cloud skills.

Here’s the problem: the IT services partners they’re evaluating are weakest on exactly this criterion.

AI capability gap: why IT services partners are falling behind

New research across the META region shows a sharp disconnect. When services buyers rank what matters most in choosing a partner for 2026 and beyond, AI and agentic AI capabilities land at the top. Ahead of innovation track record. Ahead of security credentials. Ahead of cloud-native skills.

When those same buyers score their current partners on that same criterion, the numbers drop significantly. Satisfaction gaps open up precisely where buyers have their highest expectations.

This isn’t a positioning problem. It isn’t a pricing problem. It’s a capability gap, and it’s affecting vendor retention and deal size over the next 18 months.

Why IT services buyers demand change in 2026

73% of organisations across the META region believe their business needs to reinvent within five years just to survive. That reinvention is already happening. Application modernisation, multicloud strategy, and AI/ML deployment are topping investment roadmaps through 2027.

Budgets are flowing. 56% of organisations across the META region are planning to increase IT services spending in 2026 versus 2025. That figure rises to 79% when we look ahead to 2027. As that investment accelerates, vendor conversations are shifting. Buyers used to ask ‘should we adopt AI?’ Now they ask ‘which partner has the depth and track record to execute this?’

What META services buyers are asking their IT services partners

When services vendors sit down with buyers across the region, three questions keep coming up.

The first is straightforward: where does your AI capability actually stand? Buyers are committing real budgets to public cloud, on-premises infrastructure, and AI/ML systems. They need confidence that their partner has the maturity and the delivery history to handle work at scale. When capability gaps exist, they show up fast under budget pressure.

The second question is about delivery models. Most vendors remain primarily human-centric in how they deliver services, while a few are experimenting with fully automated alternatives that remove humans entirely from the value chain. Vendors who can articulate a more nuanced approach to this tension are winning conversations.

The third question cuts deeper: are you helping us think, or are you executing for us? Technology consulting firms have already claimed 37% of business strategy conversations across the region (IDC EMEA IT Services 2026). If your firm competes primarily on delivery execution, that advisory seat gets harder to claim as months pass.

How IT services vendors are closing the AI capability gap

The pattern across the region is clear. Vendors who invest in AI capability first are seeing stronger client retention and larger deal sizes. Vendors who can explain how they layer AI into delivery while keeping human expertise in control are more credible to risk-averse buyers making large bets. And vendors who’ve moved from execution conversations to strategy conversations with their clients are building stickier partnerships.

Making these shifts takes work. It means restructuring how engagements are staffed, how delivery is orchestrated, and how partnerships develop over time. But the vendors doing this now are securing better positioning with their largest META clients.

These questions – where’s your AI capability, how do you want to deliver, can you help us think – are the ones IT services buyers across the META region are asking right now. On October 6, IDC analysts Matt Wilkins and Eric Samuel are bringing these questions directly to IT services partners and technology leaders across the EMEA region. Join us for the live webinar and bring your questions.

Eric Samuel

Eric Samuel - Associate Research Director, Services

Analyst Profile
Eric Samuel leads IDC’s Middle East and Africa IT Services Strategies program, delivering market intelligence, competitive analysis, and strategic guidance that help IT services providers strengthen their position and accelerate growth across the region. His recent work has focused on:…