Breaking down barriers: why traditional development structures are limiting Europe’s automotive industry.

The greatest barrier to realising deeptech's game-changing potential in the automotive sector isn't technical; it's organisational. While the automotive sector races towards electrification and autonomy, the most formidable obstacle to leveraging deeptech isn't a lack of innovation, but rather deeply ingrained organisational structures. Traditional automotive development has evolved around three interconnected constraints that all work against true system-level optimisation:

V-Cycle Process Limitations: The conventional V-cycle development approach requires rapid decomposition of vehicle-level targets into component specifications, to enable detailed simulation and design optimisation. This approach, however, breaks the crucial coupling between components within the system - like refining individual musicians without considering how they play together in an orchestra.

Specialist Team Development Structures: The V-cycle approach has led traditional organisations to form specialist departments where inverter, motor, and gearbox specialists can each optimise their components in isolation, and then pass data and specifications between them. This made sense when powertrain system architectures were extremely stable and sub-systems and component interactions were well defined. This fragmented approach prevents true system-level optimisation, where improvements in one component amplify gains across the entire powertrain. Achieving this true system-level optimisation demands increasingly sophisticated software design capabilities.

Internal Development Complexity: Perhaps most critically, while some forward-thinking organisations are attempting to build these capabilities in-house, developing AI-driven optimisation platforms requires deep expertise in machine learning, high-performance computing, and advanced simulation techniques; skills that are scarce and expensive to build internally. This domain is so specialist that most manufacturers are better served partnering with deeptech specialists rather than attempting to build these capabilities from scratch. Attempting to build these capabilities internally not only incurs significant cost and time, but also diverts critical resources from a manufacturer's core mission: designing and building exceptional vehicles.

The reality is that fragmented units each building their own tools both duplicate effort and miss the system-level optimisation opportunities that deliver the greatest competitive advantage. Just as you wouldn't develop your own CAD software, there's compelling logic in partnering with specialists who live and breathe this technology daily.

The development of next generation powertrains is different from their ICE predecessors. We predict that the companies that will be successful will be the ones that break down barriers between specialist groups, embrace integrated optimisation across the entire powertrain system, and partner with technology specialists to integrate ML and AI capabilities directly into their existing workflows. It’s the integrated solution, we feel, that will win out as it's the approach that allows engineers to focus on what they do best – to create exceptional products - while being helped by, and benefiting from, cutting-edge optimisation capabilities that would take years and millions of pounds to develop internally.

Monumo’s deeptech-enabled integration keeps components coupled throughout the development process; something we can achieve because of the enormous number of system configurations we can consider in very short time frames. We are no strangers to examining a million, fully worked, 3-in-1 optimised motor designs in under a week, and finding the absolute best for the criteria required, be that weight, cost, sustainability or performance. This capability allows these groups to work together and evolve system specifications in parallel, reducing concept design cycles from 16 weeks to 4 weeks, and is proven to work. In a recent project and working on a state-of-the-art motor as our baseline, we successfully reduced magnet mass by 24% - an improvement that was three times greater than had we just optimised at a component level.

The automotive industry stands at a critical juncture. Companies that partner with deeptech specialists are already reducing costs and development times, while their competitors struggle with traditional approaches. The companies that embrace deeptech integration will gain significant competitive advantages in cost, performance, and time-to-market. Those that continue to rely on traditional engineering approaches will find themselves increasingly disadvantaged.

The technology is maturing to a point that it is consistently delivering proven results with clear competitive advantages. The question isn't whether to adopt deeptech in powertrain engineering - it's how quickly you can.

Simon Shepherd is Head of eDrive and Chief Product Officer at Monumo, a deeptech startup based in Cambridge and Coventry, UK, specialising in AI-driven automotive engineering solutions. Ready to explore how deeptech can transform your powertrain development? Contact our team at monumo.com or reach out directly to discuss your specific challenges and opportunities.

In case you missed it - click to view Part 1: How deeptech is already revolutionising EV powertrain engineering