Speaking to Intelligent CIO Middle Eastat LEAP 2026, Rahul Misra, SVP and Managing Director, MEA at IFS, explains how the company’s industrial AI strategy is bringing agentic AI into critical workflows across energy, utilities, manufacturing, aerospace and other asset-intensive sectors.
IFS has pivoted towards industrial AI over the past year. What does industrial AI mean in practice, and how does it build on the company’s traditional strengths?
IFS has pivoted towards industrial AI over the past year or so. Our core remains the asset and service industries we have always focused on. That does not change. What is important about this pivot is how we are helping customers in those industries apply AI to real operational challenges.
AI has been around for a long time, but it continues to evolve in different forms. Over the past couple of years, much of the conversation has been around generative AI, which can generate reports or bring information to you in a certain way. The question then becomes: what do you do with that information? That is where agentic AI comes in.
In our region, having the right resources and skills is a significant challenge. As Saudi Arabia and the wider region modernise their infrastructure, you cannot continue adding skills and resources at the same pace as technology and infrastructure are evolving.
Consider an energy or utilities company with thousands of assets. The same applies to manufacturing, defence and aerospace. Generative AI might identify a potential problem with an asset, but what happens next?
Agentic AI can decipher the problem, look at the asset, identify the resources and skills required and then help execute the appropriate action. IFS adds the industry context. If we know the asset is a turbine, for example, and understand its historical failure patterns, we can contextualise the data, identify the appropriate process and determine what needs to happen next.
That is industrial AI. We bring together contextual data, industry processes and AI-driven decision-making, with human oversight for operational input.
Our agentic AI Loops are designed to continuously learn and improve decision-making so that the operational impact gets better over time. Beyond the agentic AI layer, we are also using our Nexus Black approach to solve larger customer challenges, leveraging LLMs in an agnostic way across the asset and service side of the business.
Many companies have invested in AI but are struggling to demonstrate ROI or move beyond pilots. What is preventing them from scaling?
Data is the number one challenge. It sits across multiple environments and systems, and organisations often struggle to bring it together in a way that makes it usable.
This is where AI can play a significant role, whether through an agentic layer or LLMs. You need to ensure the data is usable for making informed decisions that have a positive operational impact. It has to be outcome-based.
When data sits across a heterogeneous environment, you also have governance, integration and cybersecurity considerations. There are many interconnected issues that need to be addressed.
Then there is trust. When AI tells you what you should do, human oversight becomes critical. Can I trust the information? Can I trust the decision the system is recommending? There is a cultural challenge associated with that.
The third issue is scalability. Many projects remain stuck at the pilot stage. Within a siloed pilot, you can ensure that all the necessary components and data are in place. At scale, however, you need to have meaningful data, the right human skills and confidence in the information being presented.
It is a journey. The technology is evolving and getting better over time, but organisations need to establish those foundations before they can scale successfully.
Where do you draw the line between AI assisting humans and AI making autonomous decisions?
We still have a long way to go. As the models continue to learn and improve, that line will become increasingly blurred.
There is no fixed answer because the business world continues to evolve and new variables are constantly emerging. The models need to continue learning and become more predictable in the decisions and outcomes they provide.
It comes back to the old principle of garbage in, garbage out. You have to ensure that what you feed into these models becomes cleaner and more reliable over time.
LLMs are also becoming better, the learning is improving and the information being fed into them is improving. I do not think the line between humans and AI will ever disappear completely. It will become blurred, but ultimately AI will help us make better-informed decisions that have a positive operational impact.
How is IFS leveraging AI within enterprise asset management and field service management?
We look at three layers within the IFS Cloud platform.
First, we have embedded AI capabilities. Second, agentic AI operates at the workflow level. Third, we have Nexus Black, which addresses specific and often complex business use cases that a generic platform or standard AI capability may not solve.
With Nexus Black, we can leverage different models, irrespective of where they come from, to address those business problems. Our aim is then to take those learnings and turn them into repeatable, usable and packaged offerings for customers, so they do not have to start from scratch.
Our industry knowledge is critical here. We bring around 30 years of industry know-how and intellectual property, combined with the ability to leverage new technologies. Bringing all those elements together is what differentiates the approach.
Nexus Black is a core part of IFS’ co-innovation strategy. Are you already working with customers in the region, and what have you learned from those engagements?
Yes. One of the biggest issues again comes back to trust.
Customers like what they see because we can develop prototypes very quickly. We can take a business use case and come back with a prototype within a matter of weeks. That means customers can see and experience the potential rather than viewing it as something that might materialise over a much longer period.
Once they can see it and understand what it can do, the conversation changes significantly. We expect to be in a position to announce some new customers on this journey in the coming weeks.
Industrial environments generate enormous volumes of operational data. How do you contextualise that data and turn it into something AI can use effectively?
This is where industry expertise becomes critical. You need to understand the data coming from different assets and operational environments, and you need the right subject matter experts to bring together technology, processes and industry knowledge.
There is no simple formula. Operating an oilfield rig is very different from operating a turbine or maintaining an aircraft engine. A manufacturing environment presents another completely different set of requirements.
You have to identify the common denominators, understand the outcome you are trying to achieve and then work backwards, combining human expertise with the appropriate technology.
That ability to contextualise operational data is fundamental to making industrial AI meaningful.
Data sovereignty is a major consideration in highly regulated industries. How is IFS approaching this requirement?
It is a journey, and this is where partnerships such as our relationship with Microsoft become important.
In Saudi Arabia, as hyperscalers expand their local cloud infrastructure, we will be able to help customers currently operating on-premises or through local cloud offerings transition into these environments.
The advantage is that customers can leverage cloud infrastructure and services alongside the stringent security capabilities required to meet regulatory and data sovereignty requirements in the Kingdom and the wider region.
That is how we are working with customers as they navigate the transition.
Can you tell us about IFS Zero and how it supports customers’ sustainability ambitions?
Sustainability is becoming increasingly important across growing economies, where conversations around carbon footprints are intensifying. It is also increasingly becoming a statutory or reporting requirement.
We have developed IFS Zero to help customers address these requirements. It enables organisations to understand their sustainability requirements, monitor their carbon footprint and support the reporting process.
As sustainability obligations continue to evolve, the objective is to give organisations greater visibility into their environmental impact and help them manage it more effectively.
Saudi Arabia is undertaking one of the world’s most ambitious transformation programmes through Vision 2030. What opportunities does this create for IFS?
Vision 2030 is fundamentally about building within the Kingdom and developing the economy, and much of that ultimately comes down to infrastructure.
As Saudi Arabia grows and its cities and communities become smarter, there is a significant need to modernise infrastructure and improve the lives of citizens and residents. The Kingdom is at an important point of inflection.
These are precisely the asset and service-intensive industries where IFS operates. Building infrastructure involves engineering, procurement and construction, which is a vertical industry for us. The machines used to build that infrastructure, the plants supporting it and the assets that ultimately need to be operated and maintained all sit within areas where we provide verticalised, AI-enabled solutions.
That puts us in a strong position to grow alongside the Saudi economy. The same opportunity exists beyond the Kingdom as infrastructure modernisation accelerates across the wider region.
