While Dutch industry is increasingly applying artificial intelligence in its business processes, it lags behind the pace needed to remain internationally competitive. By 2025, adoption in industry will have increased faster than in other sectors. Yet a sector analysis by ING Research shows that only one in five manufacturers is actively using artificial intelligence, while software investment lags behind overall industry investment growth.
Investment
The real value of industrial software capital has declined by 7.5% over the past five years. This compares with 8.5% growth nationwide. With this, the sector risks missing opportunities to become more productive and competitive. In the medium term, industrial companies that implement artificial intelligence tend to outperform competitors that do not.
Much to be gained from successful deployment of artificial intelligence
ING Sector Banker Industry, Gert Jan Braam: "Manufacturing companies have a lot to gain from the successful deployment of artificial intelligence. They would do well to take a pragmatic approach. Start small and get outside expertise where necessary. Getting the data housekeeping in order deserves priority in order to feed AI applications with the right data. Then you can scale up AI responsibly step by step."
Industry risks not realising potential productivity gains
Company-level academic studies show that adopting artificial intelligence can increase annual employee productivity growth by up to 3 percentage points. Moreover, in the medium term, industrial companies that implement artificial intelligence tend to outperform competitors that do not, both in terms of productivity and market share. Although the benefits vary by task and thus by company, the industry risks being left behind by an important new technology with potentially significant productivity gains.
Industrial software investment needs to rise
Productive deployment of artificial intelligence requires greater investment in software. While industry has increased investment in software over the past five years, on average, at 2% a year, growth has been only half as high as overall industry investment growth. Compared with economy-wide investment in software, industrial growth in software investment lags even further behind. While the nominal value of software ownership still increased, its inflation-adjusted (real) value fell by 7.5% between 2019 and 2024. A difference of 16 percentage points from the 8.5% growth in software capital nationwide. Therefore, to realise substantial productivity gains with AI, a reversal of that downward trend is needed.
Artificial intelligence in the Netherlands compared to Northwest Europe
Compared to other EU countries, the Netherlands' industrial AI adoption presents a two-fold picture. Manufacturing companies in the Netherlands are more likely to deploy artificial intelligence than the EU average, but lag behind European leaders. Measured among companies with 10 or more employees, 29% of manufacturing companies in the Netherlands use AI, compared to an average of 17% in the EU as a whole. At the same time, Dutch manufacturing companies are lagging behind compared to their counterparts in most surrounding countries in north-western Europe. In Belgium and Denmark, for instance, almost 40% of manufacturing companies report using artificial intelligence by 2025. The share of large industrial users (deploying three or more AI applications), at 17% versus 11%, is also a lot higher in those countries than in the Netherlands.
Manufacturing companies have much to gain from the successful deployment of artificial intelligence. They would do well to take a pragmatic approach. Start small and get outside expertise where necessary.ING Industry Sector Banker, Gert Jan Braam
Making production processes more efficient
Artificial intelligence can make manufacturing companies more efficient. It is crucial for more efficient design of production processes, thus addressing labour shortages. Business processes still require numerous manual operations at most manufacturing companies. Much of that work can be made more efficient with further automation and AI, for instance by deploying self-learning robots. Certainly the trend towards smaller and more diverse orders ("high-mix, low-volume") requires IT solutions that enable more autonomous production processes. The smart application of AI also makes industrial processes faster, better quality and more reliable.
Much added value for core industrial processes
The number of successful industrial AI use cases is growing by the day. Manufacturers are also increasingly deploying machine learning and deep learning models in core processes. For example, for AI-assisted quality checks in the production line or reducing changeover times through AI-driven machine setting and automatic tool selection.
Biggest gains to be made outside the core process
The greatest potential of (Gen)AI is not in the core process of manufacturing itself. That is already highly automated with machines and robots. It is precisely in processes around that that there is still much to be gained, such as in logistics processes and service provision, but also content creation and analysis applications for departments such as marketing and sales, HR, IT, finance and legal. Concrete examples are:
- Making production planning more efficient
- Translating a product design into machining steps faster and more accurately
- Better predict machine wear and failures
- Optimise logistics and inventory management and pricing
- Autonomous transport systems between warehouse and production line
- Automated ordering and quotation processes for standard products
- AR applications supporting mechanics in real time
- Automatic documentation via speech-to-text for work instructions and training
- Automated answering of customer queries with chatbots
- Automated code writing for drivers of machines and robots
- Supporting the development of new products and processes
This increases productivity, while giving employees more time for work where their knowledge adds most value.
Good data infrastructure key prerequisite for success
Finally, a good data infrastructure is the basis for success. Many manufacturing companies would therefore do well to make their processes more transparent, clean up and make data more consistent, and link IT and operational technologies (OT) to ensure reliability. The better data is unlocked and structured, the more powerful AI can make connections and create value.
Strategic vision drivers and knowledge building needed
Artificial intelligence requires a clear strategic vision from corporate executives, as the technology involves a systemic change that may cause resistance from employees. This is borne out by research showing that 95% of GenAI pilots fail because organisations try to avoid that very friction. Forerunners, however, show that with vision and management commitment, friction can be overcome, especially when AI is not deployed as a generic tool but is deeply embedded in work processes.
Staff
Staff are mostly not eager to adapt existing ways of working, do not always have experience with artificial intelligence and there are often concerns about (future) job loss. However, the latter risk seems limited in the industry. Due to continuous staff shortages, large-scale layoffs are not obvious. Also, automation is largely at the expense of less competitive activities and less popular work. Thereby, employees focus more and more on the development of products and manufacturing processes and less and less on the manufacturing process itself.
Build knowledge, start small and create scale
Knowledge building is also crucial: employees need to be introduced to artificial intelligence step by step to see the added value for their work and become more proficient with it. By starting small, low-threshold knowledge can be gained, such as with prompting for office processes. In addition, collaborating or merging helps to create sufficient scale. Not only to enable digital transformation, but also to cope with the increasing competition and complexity in a general sense - of manufacturing processes and due to staff shortages and regulatory pressure.
