Three winners, and what others can take from them
The IFS Awards winners 2026 went to Alliander, SPIE Nederland and Dixstone. Three Benelux organisations that moved Industrial AI from pilot into their work process.
Industrial AI in IFS Cloud, where the system prepares and people decide
IFS.ai brings Industrial AI into daily operations: assets, service, planning and maintenance. We help your team define what AI may prepare, what it may execute and where a person signs off.
Autonomous, with your people in control
Industrial AI only pays off when it lands in assets, service, planning and maintenance. For a COO or operations director the first question is authority: who may act on an AI recommendation, and which decisions need approval? We help define those rules before automation scales, so IFS.ai signals that may start work automatically are separated from those that need a signature. Eqeep does this as IFS Specialized Partner in the Benelux. See our analysis and design approach.
Planners and purchasers need what-if planning as a weekly habit, not a quarterly exercise. They see the options before the line feels the impact; the aim is a decision you can check, not a perfect forecast. Maintenance and asset managers face the opposite risk: analyses with no follow-through. IFS Cloud connects maintenance, planning, parts and asset history, so a predictive signal becomes scheduled work. Read more on demand forecasting and asset availability.
Service managers want expertise that stays available when the experienced colleague is not in the room. CIOs and IT leads fight data quality, and the ERP underneath is not always IFS. Composable architecture and open APIs turn AI capabilities into modular building blocks, and IFS reports that integrations shift from months to weeks. In each case people remain accountable for the decision, with an audit trail they can explain. Start with composable ERP.
Six AI capabilities, one platform
IFS Industrial AI works inside operational workflows. It can draft, rank, surface context, forecast, optimise and flag exceptions. A person remains responsible for each decision.
Drafts that start from IFS data
Summaries, work-order drafts and service notes come from trusted IFS context.
Ranked options for the next action
Signals from assets, service, projects and supply chain arrive as ranked options.
Knowledge at the moment of work
Contextual knowledge: manuals, asset history, agreements and earlier interventions appear when the decision is made.
Test a change before it reaches operations
Model demand, capacity, maintenance windows and service risk first.
Plans that respect the constraints
Technicians, parts, SLAs, production windows and asset criticality in one plan.
Catch the exception while it is small
Anomaly detection flags abnormal readings, cost drift and late signals before they turn into downtime or backlog.
What we see in practice
Predictive maintenance and predictive asset management: one chain from signal to decision
Industrial AI only pays off when it lands in assets, service, planning and maintenance. Every step in the chain has an owner.
Signal
Industrial Intelligence in IFS Cloud
Anomaly detection flags abnormal readings, cost drift and late signals while they are still small.
People decide: which signals may start work automatically and which need a signature.
Prepared action
IFS.ai and IFS Loops
Drafts, ranked options and what-if scenarios start from trusted IFS data. Digital workers take over routine coordination.
People decide: what an agent may prepare and which actions need approval.
Decision
Your planners, technicians and managers
A predictive signal becomes scheduled work in IFS Cloud, with an audit trail people can explain.
People decide: people remain accountable for the decision.
IFS.ai: Industrial AI that acts in the flow of work
Autonomous manufacturing
Controlled autonomy: IFS Cloud, IFS.ai and your operating rules decide where a person must approve.
Predictive maintenance
Predicting is step one. Acting is step two. That is where the real value of AI lies.
Process insight
Process mining turns raw data from your IT systems into a view of how your processes actually run.
Getting AI-ready
Before you start with AI, examine your data: complete, reliable, current and consistent across systems.
Questions before you automate
What are the three layers of IFS Industrial AI?
IFS Industrial Intelligence runs inside IFS Cloud and uses operational data to support day-to-day decisions, without a separate deployment. IFS Loops adds governed digital workers that execute work end to end, can go live in weeks and remain auditable. IFS Nexus Black addresses problems that standard systems cannot solve; it is co-created with operators and can move from prototype to production in days.
How does IFS.ai support predictive maintenance and predictive asset management in IFS Cloud?
IFS.ai brings Industrial AI into maintenance and asset management. Anomaly detection flags abnormal readings, cost drift and late signals while they are still small, and IFS Cloud connects maintenance, planning, parts and asset history, so a predictive signal becomes scheduled work instead of another dashboard. Our solution architect explains what predictive maintenance needs from your data and processes.
How do humans stay in control?
Teams decide which decisions an agent may prepare, which actions need approval and how outcomes are measured. Digital workers remove routine coordination; planners, technicians and managers keep the accountable choices.
Where should an industrial AI journey start?
Start with one decision that repeats often, uses reliable IFS or asset data and has a measurable operational result. Examples are demand changes, field exceptions, asset risk, work order prioritisation or service margin leakage.
Can Industrial AI work with SAP or other ERP systems?
Yes. The route can coexist with the existing ERP environment. Eqeep maps the operating model, data flow and IFS capabilities first, then decides where IFS Cloud, IFS.ai or Nexus Black should carry the work.
Do we need perfect data before starting?
No. You need data that is good enough for the first decision. IFS and AI can help improve data quality over time, but the control model, exceptions and ownership must be clear from the start.
Who you will talk to
The colleague you speak to about AI in your IFS environment.
Robert Kool
Solution Architect
Utrecht, Netherlands
