Only 3% of industrial companies have no plans to use AI in their operations, new research shows
In a new research report by Hexagon and Foundry, over 70% of industrial decision-makers in France, Germany and the UK say they already use AI in their operations. While classic uses such as predictive maintenance dominate, sustainability and asset performance management (APM) are rapidly gaining ground.
In the past two years, generative artificial intelligence (AI) has sparked intense interest among decision-makers: at the end of 2023, Gartner found that 45% of organizations said they had a generative AI proof-of-concept underway, and 10% used it in production.
However, new research by Hexagon and Foundry shows that more established and mature use cases have become much more widespread in industrial operations. It also reports that industrial decision-makers are enthusiastic about AI’s potential and only a tiny fraction have no plan to implement it.
Foundry surveyed 223 IT decision-makers in France, Germany and the UK, working at organizations with between 1,000 and 10,000 employees and within a wide range of industrial sectors.
AI applications abound, with a strong focus on optimization
The first finding of this new research is that the enthusiasm for AI has not cooled down: only 3% say they have no plans to use AI. “The incorporation of AI in maintenance is widely accepted, with a large majority currently using or planning to use AI for various maintenance applications in 2024,” the survey notes.
This high level of AI usage is primarily driven by mature applications, such as work management assistance (73%), inventory optimization (71%), and predictive maintenance implementation (68%)—three key features that may come out of the box with a modern maintenance solution, such as an Enterprise Asset Management (EAM) platform. 
Source: Foundry / Hexagon
These mature uses of AI present a common advantage: they offer the level of reliability that industrial organizations expect to be used in production at a large scale. For example, Kal Tire, a tire manufacturer, is using AI-assisted computer vision to inspect one million tires and handle 100,000 tire changes—a result that can be achieved thanks to the maturity of computer vision for this type of use.
Energy Efficiency as a Rapidly Growing Use Case
The survey shows that industrial companies currently prioritize automation and efficiency gains over other considerations. “Markets are receptive to cloud-based, AI-enhanced asset management systems that can drive efficiency and sustainability,” the survey notes.
Among use cases that are planned in the future, energy efficiency analysis leads, with 35% of decision-makers saying they plan to use AI for that purpose.
Cloud and Enterprise Asset Management (EAM) Adoption Power New Uses of AI
A second finding is that this widespread usage of AI has been made possible by the large adoption of the cloud, helping companies leverage large volumes of data to power AI models. “A striking 80% of respondents have fully implemented cloud-based EAM systems. SaaS is almost universally adopted, with IaaS and PaaS lagging somewhat,” the survey notes. EAM adoption helps power new approaches, such as asset performance management (APM), which helps companies optimize the use of their assets based on real-time assessment of cost, risk and performance.
Bas Beemsterboer, Strategy Director for Hexagon, said: “Many proof of concepts (PoCs) focus on ChatGPT-style interfaces, with significant hurdles. However, we currently see productive use cases in fields like image recognition, where AI can do much better than humans. Optimizing asset management and investments based on different scenarios is also a key use of AI, with transformative business outcomes.”
Beemsterboer provides advice to industrial companies looking to expand the role of AI in their operations: “We opened our AI frameworks within our HxGN EAM solution last year. It lets our clients run their own PoCs and use cases with the help of a library of algorithms to save time and effort. We see three factors that unite PoCs that make their way to operations: executive support, strong alignment with business needs, and good salesmanship. PoCs should not be left to engineers, they need good partners and involved executives.”
Read the full report to uncover detailed insights on AI trends in industrial operations.