Making the case for what sets IFS apart: Focus, Breadth around an asset and AI built for production. November 3, 2026 | Posted by LG Content Team Gartner named IFS a Leader in its 2025 Magic Quadrant for Cloud ERP IFS was founded in 1983 in Linköping, Sweden, by university engineers, and its first product was a maintenance system for Swedish nuclear power plants. For 43 years, the company has built software for one kind of customer: organisations that design, build, operate and maintain physical assets. The last decade changed its scale. Since EQT took control in 2015, IFS has more than 15 times its valuation with significant growth. Today IFS Cloud brings ERP, asset management, field service and service management onto one platform, with Industrial AI built in. More than 6,000 customers in over 90 countries run on it, and annual recurring revenue has passed €1.28 billion. Suliman Gaouda, Regional Vice President, Industrial AI, APJMEA (Asia Pacific, Japan Middle East & Africa) IFS, recently responded to a questionnaire by LogisticsGulf.com. The wide-ranging interview covered multiple facets of the company - its evolution, accomplishments, its strengths and capabilities, service offerings, its regional expansion plans and its vision for the future. Below, are the transcripts of that exclusive interview. LogisticsGulf.com (LG): Given the present fluid geopolitical scenario and the disruption of supply chains in the Middle East and globally, how can Industrial AI provide better (proactive) outcomes with actionable capabilities that can help predict disruptions rather than just react to them? Suliman Gaouda (SG): Most supply chain systems were built to report what has already happened. Industrial AI changes the order of events. It watches the signals that come before a disruption, such as transit counts through a strait, supplier lead times, port congestion, fuel prices, and the condition of the assets that move goods, and it prepares a response before the shock lands. The need is real. Daily transits through the Strait of Hormuz fell from 85 to 130 before the crisis to six to ten between July and September, and Brent rose about 65% in a single month. A Gulf gas distributor saw the cost of moving one canister rise from US$2,800 to US$12,700. AI that can reroute, re-sequence and reprice in hours, not in the next planning cycle, turns a crisis into a decision. LG: How does the region's rapid digital transformation, infrastructure investments and industrial diversification create a strong opportunity for AI-led operational transformation? SG: The Gulf is building the next industrial estate at extraordinary speed. MEED counts around US$2 trillion of upcoming projects across the GCC, roughly half in Saudi Arabia and 27.5% in the UAE, and the UAE alone has about 700 execution-ready projects worth US$ 138 billion. Every port, plant, rail line and data centre becomes an asset someone must run for 30 to 50 years. That is the opportunity. Older markets are retrofitting decades of legacy systems. The region can capture asset data at handover, design maintenance around sensors from day one, and connect procurement, production and logistics before silos form. The cheapest moment to make an asset intelligent is before it is commissioned, and the organisations that use this window will run their estates with less downtime and lower cost. LG: How can AI help supply chain leaders work on models for different scenarios, identify constraints early and make faster decisions across the entire value chain? SG: A disruption rarely stays where it starts. A delayed vessel becomes a missing part, a missed production slot, an idle crew and a broken customer commitment. The answer is to model the chain as one system rather than as separate functions. AI planning engines can run hundreds of scenarios across transport modes, suppliers, inventory and production capacity, and show which constraint will bite first. McKinsey finds only 30% of companies see beyond their tier-one suppliers, which is where most cascades begin. Inventory matters too: a warehouse is cash on the balance sheet, and that cash loses value if it does not move. When AI connects stock, maintenance demand and supplier risk, leaders can decide in hours which orders to protect, which routes to switch and which parts to pre-position, with the cost and carbon trade-offs visible first. LG: How can moving from reactive to predictive maintenance reduce downtime and improve asset performance across factories, fleets, ports, airports and other critical infrastructure? SG: Unplanned downtime costs the world's 500 largest companies about $1.4 trillion a year, or 11% of revenue, according to Siemens. The same research shows a telling pattern: incidents per facility have fallen, but recovery per incident has stretched from 49 to 81 minutes as experienced people retire. Predictive maintenance addresses both. Sensor and operational data show how an asset is behaving, whether it is a crane, a fleet vehicle, a baggage system or a pump, and AI flags degradation before it becomes failure. The work is planned, the part is in stock, and the right technician arrives with the history in hand. Consultancy ranges put the result at 30 to 50% less unplanned downtime and 20 to 40% longer asset life. For ports and airports, where one failure stops everything behind it, that is the difference between a delay and a shutdown. LG: Why is generic AI not enough for complex manufacturing and transportation environments, and why does AI need to understand assets, processes, people and operational conditions to deliver meaningful results? SG: The first point to recognize is that Physical and Industrial AI and Enterprise AI are very different. Gartner expects Physical AI environments will generate 10X more data than all digital enterprise AI use cases combined. In the field a chatbot that is wrong writes a bad paragraph. An agent that is wrong in a substation causes an outage. Generic models are trained to be plausible. Industrial operations need AI that is correct, repeatable and auditable, because it acts on physical equipment, safety permits and regulated processes. It has to know what an asset is, how it fails, which part fits, who is certified to touch it, and the conditions it operates in. That context comes from decades of operational data, not from the internet. IFS separates planning from language: a domain-specific orchestrator decides what to do, and a general model explains it, with every action logged and traceable. Many customers also need AI that runs where their data must stay, including fully offline on a rig or in a plant. Generic tools were not designed for that. LG: Given the skills gap challenge, how can AI capture knowledge, support employees and improve productivity without simply replacing the workforce? SG: Roughly 70 to 80% of the world's workers do not sit at a desk, yet they have historically received a tiny share of enterprise software investment. Around 11,400 Americans reach retirement age every day, and 41% of US certified aircraft mechanics are already over 60. The real shortage is the knowledge walking out of the door. AI should capture that knowledge, not replace the people who hold it. At one airline, technicians spent one day in five finding manuals, part histories and spares. Putting that knowledge on a phone in minutes gives the day back to maintenance. At Kodiak Gas Services, a single IFS Loops Digital Worker took routine replenishment off its buyers, returning about 90,000 hours and US$3 million a year, and freed them for the judgement calls that need a human. LG: Why is IFS a standout in its genre? Make the case for your strengths, capabilities and USPs. SG: Three things set IFS apart. The first is focus. IFS works only in asset-intensive industries, so its software understands why an asset loses value, not just what it costs. A general ledger records depreciation; IFS explains it through failures, operating conditions and maintenance history. The second is breadth around the asset. From investment planning through design, construction, operation, maintenance and decommissioning, everything that touches an asset, whether a technician, an agent, a sensor, a warehouse or a truck, sits on one platform. The third is AI built for production. IFS Industrial Intelligence supports decisions, IFS Loops Digital Workers carry out daily work, and IFS Nexus Black handles high-stakes operations, all designed to be predictable and auditable. Gartner named IFS a Leader in its 2025 Magic Quadrant for Cloud ERP for Product-Centric Enterprises, and IDC recognises it as a Leader across asset management and field service.

IFS Industrial AI bookings triple in 2026 as companies move AI from pilots to daily operations. IFS has reported that annual recurring revenue passed €1.3 billion at the end of the third quarter, up 24% from a year earlier. The figure that explains it is smaller in dollar terms and larger in meaning: new Industrial AI bookings for the first nine months of 2026 already exceed three times those for all of 2025, with a quarter still to go. The story is not that AI is selling. It is that a particular kind of AI, the kind that can be trusted on a live job, has finally found its buyers. The pilot era is ending, and trust is the product. For two years, industrial companies treated artificial intelligence the way cautious homeowners treat a new contractor: a small job first, close supervision, no access to the keys. Pilots multiplied. Production deployments mostly did not. What has changed in 2026, according to IFS, is that customers in manufacturing, asset maintenance, field service and supply chain are moving AI out of the lab and into daily operations, and industry-specific use cases are now the leading driver of the company's growth. Recurring revenue makes up 85% of IFS's total revenue, which means this growth rests on subscriptions that customers renew rather than one-time projects that end. The Q3 customer list reads like a cross-section of the physical economy. Tesco, LATAM Airlines, Yeo Valley, Mitsubishi Electric and Eight Group selected IFS Industrial AI for asset lifecycle management, field service, aviation maintenance and manufacturing. None of those are workloads where an AI can improvise. Why a chatbot is not a coworker. The distinction that matters is between an AI agent and what the industry is now calling a digital worker. Eric Newmark, Group Vice President at IDC, drew it sharply: "Industrial organizations have moved past AI pilots toward operational deployment, but remain laser focused on ensuring it can be trusted in live operations. Building an AI agent is easy. Turning it into a digital worker you can trust in an industrial production environment is the hard part. Buyers want AI that acts on real operational data, while securely staying within the controls that their business sets. Vendors with deep industry specialization are best suited to meet that demand, and IFS' focus on asset-intensive industries positions them well for this transition to production-grade AI." That framing explains why a vendor with decades inside maintenance schedules, safety rules and field-service workflows may hold an advantage over a newcomer with a better demo. The hard part of industrial AI is not generating an answer. It is knowing which answers a technician is permitted to act on. IFS Intelligence puts the digital worker at the point of work. IFS's answer is IFS Intelligence, launched at IFS Unleashed in Orlando in October. Supported by what the company calls the Industrial AI Harness, it lets people ask questions, take action and delegate workflows to digital workers, all within the rules and permissions their business sets. More than 100 customers signed up during Q3 to pilot it and its digital workers before launch, which is an unusual number of early adopters for a product that had not yet shipped. The design choice is telling. The harness is less about making the AI more capable than about making it containable. In an industry where a wrong instruction can ground an aircraft or halt a production line, permission boundaries are a feature customers will pay for. The idle capacity argument. Mark Moffat, IFS's chief executive, built his explanation around a simple observation about unused potential: "Every industrial organization has capacity it isn't using, and the people running it know where it is. Our customers want AI that understands their equipment, their safety rules and how the work actually gets done, and AI that can act on a live job without putting anything at risk. That is what we have spent decades building, and it is why our AI bookings have tripled in just nine months." It is a quietly persuasive thesis. The productivity gains in industry are rarely hidden from the people closest to the machines. What has been missing is software that can act on that knowledge without creating new risk. What comes next. Ryan Courson, IFS's chief financial officer, pointed to where the company is heading: "Customers are moving beyond testing AI and buying our Industrial AI at scale. Bookings so far this year already exceed three times last year's full-year total. That demand is driving material growth for IFS, with ARR now above €1.3 billion, up 24% year on year. Our focus now is on expanding adoption of IFS Intelligence to help customers improve performance across more of their operations." The next test is whether those 100-plus pilots become standard practice across entire operations. If they do, 2026 may be remembered as the year industrial AI stopped asking for permission to try and started being given real work.