In 2026, industry is moving from assistants that answer to AI that acts. According to Gartner, 64% of technology leaders plan to deploy agentic AI within twelve to twenty-four months [Source: Gartner, 2026 CIO Agenda Preview]. The same firm expects more than 40% of agentic AI projects to be abandoned by 2027 [Source: Gartner, forecast of 25 June 2025]. What separates a project that holds from one that gets dropped comes down to execution and to trust.
Agentic AI is AI that can observe a situation, decide and act, where generative AI simply produces text or an answer. On a plant floor, the promise is concrete: a system that goes beyond explaining a fault, guiding the diagnosis, updating the documentation and preparing the action. Mimorian is an industrial intelligence platform that models equipment, structures fault diagnosis and captures the know-how of maintenance teams through a multi-agent AI architecture. That is exactly the ground these figures describe.
What does agentic AI change for industry?
Two notions are worth separating, because the market often conflates them. Automation runs predefined tasks along fixed rules, with human supervision handling the exceptions. Agentic AI goes further: it adapts to its context, ranks hypotheses, and proposes the next action [Source: Gartner, Business Quarterly 2Q26].
For a maintenance manager, the gap is clear. A CMMS plans and records interventions. It looks in the rear-view mirror. An agentic AI reasons about the machine in real time and walks the technician towards the root cause. It looks through the windscreen.
The key point: agentic AI keeps the human in the loop. Gartner describes a model where teams set the direction and grant the machine autonomy where it is useful, rather than a plant running without people. Human oversight remains the condition of use in critical environments.
Why is the shift accelerating despite tight budgets?
The budget constraint is real. IT budgets for 2026 are up 2.79% on average, and technology headcount up 1.33%, with a median of zero [Source: Gartner, 2026 CIO Agenda Preview]. In other words, teams have to do more with the same.
AI investment nevertheless holds up against that austerity. Companies cutting their IT budget still raise AI spending by around 34%, and 91% of respondents are increasing their generative AI funding [Source: Gartner, 2026 CIO Agenda Preview]. AI sits among the lines that boards protect first.
The priorities executives report for 2026 and 2027 explain why this subject lands directly on maintenance.
| Stated priority | Share of executives |
|---|---|
| Improve workforce productivity | 57% |
| Reduce costs | 52% |
| Improve operational resilience | 35% |
| Improve asset utilisation | 21% |
Source: Gartner, 2026 CIO Agenda Preview.
Productivity, costs, operational resilience, asset utilisation: that is the exact remit of industrial maintenance. The pressure pushing executives towards agentic AI is the same pressure that weighs on the shop floor.
Why will 40% of projects be abandoned?
Here is the paradox. The same firm that measures the enthusiasm forecasts failure for a large share of the projects. 64% are pressing ahead, and more than 40% of agentic AI projects will be abandoned by 2027 [Source: Gartner, forecast of 25 June 2025]. Enthusiasm runs ahead of maturity.
⚠️ The trap: stacking up AI pilots creates no value. An MIT study estimates that 95% of generative AI pilots deliver no measurable return [Source: MIT, NANDA project, The GenAI Divide, 2025]. What industrial projects that hold up show is that the blockage sits on the execution side rather than the technology side.
Industry already knows this gap. A survey of 2,234 maintenance leaders across the United States and Canada shows that 58% of teams use AI, and that 75% of users see a return in under six months [Source: MaintainX, 2026]. Yet despite that adoption, close to eight teams in ten have not reduced their unplanned downtime over a year. The tool alone is not enough.
The causes of abandonment look alike from one project to the next: too broad a scope, unreliable data, recommendations nobody can explain, and no clear oversight. When an AI acts without the team understanding why, trust collapses, and the project dies.
How do you end up among the projects that last?
What separates the projects that hold from those that get dropped comes down to four points. They are also the conditions of a trustworthy AI in the sense of the European AI Act.
- A bounded scope rather than a general ambition. Value comes from a precise, measurable business problem: a few critical assets, one family of recurring faults. Not the whole plant at once.
- Reliable data in and out. An agentic AI is only worth the quality of what it consumes, and the record it produces. Every diagnosis must leave a usable intervention report.
- Explainability of every recommendation. The technician sees why the AI proposes a hypothesis, and traces it back to the source: a drawing, a history, a rule. An opaque recommendation does not get deployed in a critical environment.
- Human oversight at every step. People validate, correct and enrich. They keep the final decision.
That is the logic behind Mimorian. Rather than training a model on the history of past failures, the platform models the machine itself: electrical, pneumatic and hydraulic drawings are broken down and cross-referenced with the documentation to produce a relational graph, a functional digital twin of the asset. Specialised agents reason on that basis, propose ranked hypotheses, and guide the technician towards the right test. Every recommendation stays traceable, and every intervention enriches the plant's memory.
Agentic AI holds a real promise in maintenance. It still has to be deployed with a clear scope, clean data, transparency and a human at the controls. That is what separates a pilot that becomes a standard from a pilot dropped after a year.
Frequently asked questions
What is the difference between generative AI and agentic AI in maintenance?
Generative AI answers a question or drafts a text. Agentic AI observes a situation, decides and acts: it guides a diagnosis, updates documentation, prepares a maintenance plan. On the shop floor, the second one accompanies the intervention while the first only explains it.
Does agentic AI replace the technician?
No. Gartner describes a model where people set the direction and supervise. A trustworthy agentic AI proposes hypotheses and guides the tests, and the technician keeps the final decision. It makes the team stronger.
How do you avoid being part of the 40% of abandoned projects?
By starting small. A bounded scope, reliable data, explainable recommendations and clear human oversight. Projects that fail almost always start from too broad an ambition deployed without trust or traceability.
Do you need a large budget to start?
No. A serious pilot runs on a few assets, alongside the information system, with a regular export rather than a heavy integration. The first value arrives within a few weeks, on real faults.
Conclusion
Three points to remember.
- Agentic AI is becoming industry's investment priority. 64% of executives are deploying it within two years, and AI holds up even against budget cuts.
- Mass adoption guarantees no result. More than 40% of projects will be abandoned by 2027, because the gap sits on execution far more than on technology.
- Trust is the condition for success. A bounded scope, reliable data, explainability and human oversight separate the projects that hold from those that die.
Next step for a maintenance manager: pick a single critical scope, list the recurring faults that cost the most, and test a trustworthy agentic AI on that bounded ground before any wider rollout.
To go further, read our guide What is a trustworthy AI in industry? and the article Trust and control: the real levers of ROI in industrial AI.
Sources
- Gartner, 2026 CIO Agenda Preview : Succeeding when plans change again (survey of CIOs and executives, more than 2,500 respondents: 64% deploying agentic AI within twelve to twenty-four months, rising AI investment even under budget pressure, priorities for 2026-2027).
- Gartner, communiqué « Autonomous Business » (5 mai 2026) (distinction between automation and autonomy; developed further in the Gartner Business Quarterly 2Q26).
- Gartner, prévision du 25 juin 2025: more than 40% of agentic AI projects abandoned by the end of 2027.
- MaintainX, State of Industrial Maintenance, 2026 (2,234 respondents in the United States and Canada: 58% using AI, 75% seeing a return in under six months, 79% of teams with no drop in unplanned downtime). Published by a vendor with an interest in the subject, to be read as such.
- MIT, projet NANDA, The GenAI Divide, 2025: 95% of generative AI pilots with no measurable return (reported by Fortune).