Capgemini Puts AI in Automotive Engineering to the Test
Digital tools are already established in areas such as BMW maintenance. According to Capgemini, artificial intelligence is now also showing measurable effects in engineering.
BMW
Capgemini’s Automotive Engineering and R&D Pulse 2026 shows how far AI has moved beyond pilots. Executives report shorter concept phases, rising productivity gains, higher budget shares and pressure to reorganise engineering.
Artificial intelligence has long been seen as a possible accelerator for vehicle development. Until now, however, it has been difficult to assess how strongly its use already affects development times, productivity and value creation. A new Capgemini study now provides figures from practice. For its “Automotive Engineering and R&D Pulse 2026”, the consultancy surveyed 200 executives from carmakers, Tier-1 suppliers and mobility companies. According to Capgemini, 61 per cent of the respondents are based in Europe.
The results suggest that, in some companies, AI in automotive engineering has already moved well beyond pilot projects. The strongest effects are currently visible in early development phases. Sixty-eight per cent of respondents say the time from an idea to a developed concept can be reduced by more than 50 per cent through AI. The study therefore offers a more concrete indication of where the productivity gains long expected from artificial intelligence are already becoming visible.
Where is AI having the strongest effect?
In concept development, AI systems can support analysis, simulation, variant creation and the preparation of technical information. Capgemini itself interprets the findings as an indication that artificial intelligence is developing into an important productivity factor in automotive engineering. However, the data is based on assessments by the executives surveyed and does not allow general conclusions for the entire industry.
According to the study, the effects are not limited to shorter development times. Fifty-nine per cent of respondents see new innovation and efficiency potential through AI. Forty-seven per cent say they have increased the value of products or services by more than 50 per cent.
The findings indicate that companies are increasingly assessing AI on the basis of concrete results. The focus is no longer only on technological possibilities, but on measurable improvements within existing development and business processes. Respondents see particularly strong potential in maintenance and aftersales, named by 71 per cent. Research and concept development follow with 65 per cent. Compliance and documentation are cited by 63 per cent, production and manufacturing by 55 per cent.
Why are AI budgets increasing?
The growing operational use of AI is also reflected in budgets. Forty-six per cent of the companies surveyed already invest between 10 and 20 per cent of their engineering and R&D spending in AI initiatives. Eighty-seven per cent plan further increases.
These investments are being made in an industry that has to accelerate its development processes under high cost pressure. Eighty-nine per cent of respondents report rising costs over the past three years. Two thirds consider savings of up to 20 per cent over the next two to three years necessary in order to remain competitive. At the same time, 71 per cent expect product development times to be shortened by 10 to 15 per cent. Three quarters want to reduce the ramp-up times for new products by 5 to 10 per cent.
What is driving the productivity pressure?
The efficiency targets are also being driven by increasing competition from new market entrants. Sixty per cent of executives see new players as the biggest challenge for their business. Fifty-seven per cent fear losing relevant market share within the next five years if their companies cannot significantly reduce costs. Forty-six per cent see the same risk if innovations do not reach the market faster.
This gives the question of the actual contribution made by new technologies additional weight. For carmakers and suppliers, the issue is less whether AI can be used at all. The decisive question is increasingly whether it can reduce development effort and measurably increase speed. Technological change is therefore accompanied by organisational change. Twenty-four per cent of companies are already restructuring their engineering and research organisations.
A further 59 per cent want to restructure these organisations within the next two to three years. At the same time, Capgemini says 93 per cent are investing in digital tools for scenario and risk planning. Ninety-four per cent are working on more resilient product designs to protect supply chains more effectively. Internationally distributed engineering models are also gaining importance. Seventy per cent already operate so-called Centres of Excellence at offshore locations, while 71 per cent want to expand such structures further.
