The gap between intention and action
There is a figure that sums up the state of the manufacturing industry in 2026 quite well: 93% of operations directors at large manufacturing companies plan to increase their investments in AI and digital technologies over the next five years. Yet, less than a third of maintenance and operations teams have actually implemented — even partially — an artificial intelligence-based solution.
This gap is not a technological problem. The platforms exist, sensors cost a fraction of what they did five years ago, and the cloud has removed infrastructure barriers. The problem is that most organizations are trying to integrate AI into organizational structures that are not designed to exploit it.
Traditional ERP systems were built for an era of sequential processes, rigid hierarchies, and department-by-department optimization. AI can connect planning, production, supply chain, maintenance, and workforce management in real time — but when the organization is still designed for linear work, the value stops at departmental boundaries. Intelligence remains trapped within functions.
What Industrial AI is (and what it isn't)
Industrial AI is not a chatbot to ask questions to. It is not a dashboard with prettier charts. Nor is it a machine learning model running in a separate environment that produces reports for someone to then interpret and translate into actions.
Industrial AI is artificial intelligence specialized for operational contexts, embedded directly into company workflows — from maintenance to production, from supply chain to field service. It doesn't just respond: it acts. It doesn't suggest: it executes, within the guardrails defined by the organization.
The difference is fundamental. A traditional ERP is a system of record: it captures transactions, archives data, produces reports. An ERP with integrated Industrial AI becomes a system of action: it anticipates problems, proposes solutions, and in many cases implements them autonomously.
Gartner predicts that by the end of 2026, 40% of enterprise applications will embed dedicated AI agents for their processes — up from less than 5% today.
From generic AI to AI that knows your factory
2026 marks a decisive turning point: the shift from generic AI to specialized industrial AI. IFS has taken this turn resolutely with IFS.ai — a set of features embedded directly at the heart of IFS Cloud. It is not an add-on or an external integration: the intelligence is woven into the application itself.
The architecture is built on three complementary layers:
- Data Foundation: The company's operational data is structured and made available to AI models in real time.
- Orchestration Layer: Machine learning algorithms analyze data to identify patterns, predict failures, optimize scheduling, and propose corrective actions.
- IFS.ai Copilot: A conversational interface that allows users to interact with data in natural language, obtain contextual insights, and trigger automated workflows.
What it changes concretely: five real scenarios
1. Maintenance: from failure analysis to automatic action
IFS.ai Copilot for FMEA transforms failure and maintenance data into actionable insights. Reliability engineers no longer need to spend days filling out matrices: the system identifies critical patterns and suggests intervention priorities. When a subcontractor sends a report in PDF format, the system reads it automatically, extracts the relevant information, and generates the corresponding work order.
2. Production: intelligent scheduling with finite capacity
Operation Time Prediction uses AI to improve production planning. MSO Enhanced Scheduling handles real-time rescheduling based on actual capacity constraints — and if a supplier communicates a delay, an AI agent can reschedule the next day's production without human intervention.
3. Supply chain: documents that process themselves
Customer orders, supplier quotes, delivery notes: IFS.ai can extract data from PDF documents and automatically create orders, quotes, and receipt notifications in the system. What previously required 20 to 30 minutes of data entry becomes a process of a few seconds with human supervision.
4. Field service: the technician arrives already prepared
With IFS.ai Copilot for service technicians, the worker receives real-time diagnostic suggestions based on the specific asset history and best practices. The result: more first-time-fix repairs and automatically generated service reports.
5. Finance and HR: the invisible that makes the difference
In finance, IFS.ai automates invoice matching, flags budget anomalies, and accelerates the procurement process. In HR, it simplifies document management and frees up time for strategic activities.
The role of FEKRA: from data to decision
As an IFS partner, Fekra operates in the most critical space of industrial digital transformation: the bridge between available technology and the organization's capacity to use it.
Our approach is structured in four concrete steps:
- Assessment: Identify critical assets and processes where industrial AI can generate measurable value within 6 months.
- Foundation: Build the data foundation — integrate maintenance history, operational procedures, and production data into IFS Cloud.
- Pilot: Activate IFS.ai on a controlled scope, typically 5 to 10 critical assets. Measure ROI rigorously.
- Scale: With concrete data on results, expand the scope. Expected payback within 12 to 18 months.
If you want to understand how Industrial AI can work in your specific context, contact us for a discussion without commitment. Sometimes, half an hour is enough to see things differently.