Digital technology has always supported engineering. What has changed is its reach. Simulation, digital twins, generative design and AI now shape not only how products are designed, but how quickly organisations learn and how they decide what to fund. For mission-driven innovation, that makes digital capability part of the core operating model, not an IT project on the side.
The seventh component of the Mission-Driven Innovation System treats digital enablers as the intelligent infrastructure of missions.
Key points
- Industry 5.0 uses AI and automation to amplify human judgement, not replace it.
- Organisational maturity — not software — decides the value of simulation.
- Digital twins keep learning after the experiment ends.
- In innovation management, AI should inform decisions, not make them.
From Industry 4.0 to Industry 5.0
Industry 4.0 connected machines, sensors and factories. Industry 5.0, as framed by the European Commission, is human-centric, sustainable and resilient. For innovation leaders the important shift is that production, design and R&D can now share data continuously, so every production run and every product in the field can feed evidence back into the next generation of designs.
Simulation: maturity beats software
Virtual experimentation lets teams test ideas in models before building them. But owning simulation software isn’t the same as benefiting from it. Research led by Anita Friis Sommer with the Innovation Research Interchange found that most R&D organisations used simulation, yet few had built it into an enterprise-wide capability — and that the highest-performing firms were those with the highest organisational maturity. The resulting framework describes four maturity levels, from Basic to Best-in-Class.
Digital twins and generative design
A conventional simulation answers a question and then stops. A digital twin stays connected to a physical product or process through sensors and operational data, and keeps improving its predictions as reality changes. Coupled with generative design — algorithms that explore thousands of options against constraints such as weight, cost and environmental impact — it lets each product generation start from better evidence. The creativity of these systems still depends on human clarity about constraints and intent.
AI for innovation management
AI is also starting to support the management of innovation itself: spotting emerging opportunities, comparing new concepts with past projects, and bringing more evidence into go/kill decisions. The principle is simple — AI is a collaborator, not the leader. It can inform, simulate and predict, but connecting decisions to purpose and values remains a human responsibility.
In the book
- The Virtual Experimentation and Simulation Maturity Framework — and how to use it as a roadmap
- Digital twin and generative design cases from Rolls-Royce, Dallara, Harley-Davidson and Jaguar Land Rover
- Where AI supports each stage of the innovation process, and principles for using it responsibly
Where this fits in the system
Digital enablers accelerate every other component: they shorten the learning loops in innovation processes, sharpen portfolio decisions and give ecosystems a shared source of truth.
Further reading
- Sommer, A. F. & Moskowitz, S. (2016). Leveraging virtual experimentation and simulation in R&D. Research-Technology Management.
- Sommer, A. F., Rao, A. & Koh, C. (2017). Leveraging virtual experimentation and simulation to improve R&D performance. Research-Technology Management.
- Cooper, R. G. (2024). The artificial intelligence revolution in new-product development. IEEE Engineering Management Review.