Rahul Misra, SVP & Managing Director – Middle East and Africa, IFS, talks about the next phase of enterprise AI, from moving beyond copilots to Digital Workers, the rise of Industrial AI, and how organizations can turn AI into real operational value. 

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Artificial intelligence is entering a new stage in the enterprise. After years of experimentation and pilot projects, businesses are having to make harder decisions about where the technology belongs, what it should change, and what value it should ultimately deliver. 

For Rahul Misra, who brings more than 25 years of experience across enterprise technology and business transformation, that shift is closely tied to the way organizations operate. As SVP & Managing Director – Middle East and Africa at IFS, he works with businesses across a region where technology, industrial growth and operational transformation are increasingly coming together. 

The UAE, in particular, has developed a fast-moving technology environment, with businesses and government pushing ahead with ambitious digital and industrial agendas. That makes the question of what comes next for AI especially relevant as organizations look beyond the initial excitement around the technology. 

In this exclusive interview with Tech Revolt, Misra shares his perspective on where enterprise AI is heading and what he believes businesses need to consider as that next phase takes shape.  

The Evolution of Enterprise AI 

Q1: What separates an AI use case that looks promising in a pilot from one that can deliver real value at scale? 

For me, the difference is quite simple: a successful pilot proves that the technology works. Scaling it proves that it works for the business. 

There are plenty of AI demonstrations that look impressive. But in an enterprise, particularly in industries such as energy, manufacturing, aviation, utilities or construction, AI has to work within the reality of the operation. It needs the right data and context. It needs to connect with existing systems and processes. It needs governance. And most importantly, there needs to be a clear outcome you are trying to improve. 

That outcome could be reducing equipment downtime, getting an engineer to the right job faster, improving asset availability, reducing inventory or making a supply chain more resilient. 

This is why, at IFS, we talk about Industrial AI. The starting point is not, “Where can we use AI?” It is, “What operational problem are we trying to solve?” 

When you start there, it becomes much easier to move from an interesting pilot to something that delivers measurable value across the organization. 

Q2: IFS talks about moving from copilot to coworker to Digital Worker. What changes for a business at each stage? 

I think about it quite simply: AI that answers, AI that assists, and ultimately AI that acts. 

But there is an important point behind that progression. For AI to assist or act effectively, it needs context. It needs to understand the business, the asset, the process and the operational consequence of the action it is taking. 

A copilot can give you information, summarize something or make a recommendation. A coworker goes further. It understands more of that operational context and works alongside people to help move the process forward. 

A Digital Worker takes the next step. It can actually execute a defined business process across different systems, within the right governance and guardrails. 

Take a maintenance issue. It is one thing for AI to tell you that an asset may fail. It is very different for AI to understand that asset's history, assess the operational impact, determine the right intervention, check whether the parts and skills are available, and then help coordinate the work. That is where context really matters. Because once AI starts to act, its decisions have operational consequences. And those consequences need to be positive — whether that means improving uptime, reducing cost, protecting safety or delivering a better service to the customer. 

The journey from copilot to Digital Worker isn't simply about giving AI more autonomy. It is about giving AI enough context to turn autonomy into the right operational outcome. 

Q3: What makes applying AI to the physical world fundamentally different from applying it to digital workflows? 

The physical world is much less forgiving. 

If AI gives you a poor recommendation when you are writing an email, you correct it. If AI is helping make a decision about an aircraft, a power network, a manufacturing line or a piece of critical infrastructure, the consequences can be very different. 

You are dealing with real assets, real people, safety requirements, maintenance history, engineering constraints and constantly changing operating conditions. That is why context becomes so important. 

AI needs to understand not only the data, but also what the asset is, how it behaves, what has happened to it previously, what other assets and processes it depends on, and what the operational consequence of a decision might be. 

This is where I believe the next big wave of AI value will emerge. Much of the AI conversation so far has focused on knowledge workers sitting behind a desk. But there is an enormous part of the global workforce that doesn't work that way. 

They are engineers, technicians, operators and field workers. Bringing AI into their working environment is a harder problem, because AI is no longer just generating an answer — it is influencing what happens in the physical world. 

But that is also what makes the opportunity so significant. 

Q4: What are UAE enterprises doing differently in their approach to AI compared with other markets you work across? 

What I find interesting about the UAE is the combination of ambition and speed of execution. 

AI here is not viewed simply as an IT initiative. It is connected to much bigger priorities around economic diversification, industrial growth, infrastructure, energy, aviation and the development of future industries. 

You can see that at a national level through the UAE's AI strategy, Operation 300bn and the focus on advanced technology across industry. 

That creates a different conversation with enterprises. Increasingly, the question is not, “Should we be using AI?” It is, “How do we put AI into the operation, and how quickly can we see a meaningful outcome?” 

There is also a willingness here to rethink existing operating models rather than simply adding AI on top of them. I think that is important, because real value will not come from adding another layer of technology. It will come from changing how work actually gets done. 

At the same time, as AI moves deeper into critical operations, governance, trust and context become even more important. The organizations that will get the most value will be those that can combine the UAE's appetite for innovation with strong data foundations, industry knowledge and clear governance. 

That combination of ambition and execution is what makes this market particularly exciting. 

Q5: What opportunities are we still missing by not designing AI around the realities of frontline and physical work? 

I think this is one of the biggest opportunities in AI today. We have spent a lot of time thinking about how AI can make office work more productive. But think about the engineer maintaining a power network, the technician servicing an aircraft, or the operator running a production line. Their problem is usually not that they need another dashboard; they need the right information and the right action at exactly the right moment. 

Imagine an engineer standing in front of a piece of equipment and AI can understand the asset, its history and the symptoms the engineer is seeing. It can draw on years of maintenance knowledge, help diagnose the issue and guide the next action. At the same time, the knowledge from that intervention can be captured and made available to the next engineer. 

That is particularly important because many industries are also facing shortages of experienced workers and the loss of decades of knowledge as people retire. 

So the opportunity is much bigger than productivity. It is about capturing industrial knowledge, improving safety, increasing asset availability and helping people make better decisions in the real world. 

For me, that is when AI becomes genuinely transformative: when it understands the context of the operation well enough to help people make the right decision, or take the right action, at the moment it matters. 

When AI Leaves the Screen 

Enterprise AI is entering a more consequential stage. Businesses are no longer looking only at what the technology can produce, but at how it can become part of the way work gets done. 

In industries where people, assets and physical environments are at the center of operations, that means proving its value beyond the screen. AI has to fit the realities of the work and become useful in situations where decisions have real consequences. 

Across the UAE, the pace of investment and ambition around technology creates room for that transition to happen quickly. As businesses continue to experiment, the next step will be turning that ambition into changes that can be seen and felt across their operations. 

What comes next will ultimately be shaped by how businesses choose to use it. AI has already shown what it can generate. The more interesting question now is what it can help people accomplish. 

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