Driven by changing customers demands that increase complexity, stricter regulatory requirements, and a fundamental shift from products to heterogeneous solutions built on diverse technological domains, development and engineering must change.
AI is central to this transformation, yet many engineering organisations struggle to move beyond theoretical discussions to real, actionable implementation due to the range of options and choices they face.
Research from Arthur D. Little (ADL), conducted with leading Dutch and Swedish engineering institutions NAE, IVA, and KIVI, demonstrates the benefits of AI and outlines a concrete roadmap for its successful adoption at scale. The study finds that AI can contribute to 25 per cent growth and 60 per cent productivity gains by 2030, but success depends on deploying AI alongside new ways of working and focusing on people and their needs.
Taking a people-centric approach
The path forward toward AI maturity requires addressing the most impactful challenges, including developing capabilities, encouraging appropriate mindsets, and enhancing trust in AI’s reliability, explainability, and security. These challenges are predominantly people-based. Solving them requires going back to basics on how engineers and developers create value: both through knowledge expansion and, increasingly, through knowledge integration, forming a T-shaped profile.
AI can be especially powerful in enhancing engineers’ capabilities in the knowledge integration dimension, for example, through knowledge management, project/portfolio management, scouting, intelligence, and collaboration tools. At the same time, via the knowledge expansion dimension, AI tools can also be extremely valuable in critical engineering disciplines, such as simulation, modelling, analytics, and problem solving.
Understanding the AI landscape
The first step in scaling AI deployment is to understand the current ecosystem. Demonstrating the possibilities, based on ADL’s research there are over 3,500 AI solution providers currently providing relevant tools for development and engineering, covering over 900 different use cases.
Achieving success therefore requires balancing strategic priorities with user-specific requirements when evaluating the AI options available. Organisations need to adopt a balanced portfolio approach, based on three layers:
· Strategic – selecting AI use cases that are important for must-win battles for maintaining and improving competitive advantage. These are typically specialised and domain/discipline specific and will have a range of specific benefits beyond productivity and efficiency improvement.
· User – choosing baseline AI applications that respond directly to the needs of key user personas and their jobs to be done. Taking a people-centric approach helps ensure that AI applications are actually adopted by users and is key to moving beyond pilots and trials to full integration.
· Balancing – this is where the portfolio of AI use cases is put together, creating a mix that responds both to baseline improvement and must-win battles, bridging gaps in requirements and leveraging strengths to exploit new opportunities.
This exercise results in a balanced portfolio of AI use cases that aligns with the broader technology strategy, combines leading-edge and mature AI technologies, targets user needs and allocates implementation priorities based on impact, adoption, and ROI.
Making AI happen
Identifying a balanced portfolio of AI use cases is the starting point, but merely plugging in new AI tools to the existing organisation is unlikely to deliver the anticipated benefits. Development and engineering functions must transform themselves toward an AI-enabled future model - the Networked Lab of the Future. This refers to the entire end-to-end cycle for development and engineering, involving a network of collaborators and partners, not a physical lab.
· Democratisation — making AI available, customised for everyone, and adopted as a natural way of working to help engineers and innovators further improve their mastery of data and knowledge
· Ambidexterity — setting up a development and engineering organisational structure, governance, and culture to remove barriers to AI-enabled breakthroughs, ensuring productivity and efficiency
· Data collaboration —using AI and other digital tools to build and strengthen data and knowledge ecosystems, helping to deploy the best skills and capabilities across domains and disciplines
· Enablement — modifying infrastructures, in particular the technology stack, enabling people to innovate at scale
For development and engineering organisations, doing nothing is not an option given the huge value at stake. The potential of AI is vast, but its true impact often lies not in the technology itself, but in the people who wield it. Success requires balancing technical mastery with a broader ability to integrate knowledge and systems. It also calls for a cultural shift - one that fosters collaboration, trust, and a willingness to reimagine traditional approaches.
By empowering people to innovate, changing ways of working to make them AI/digital-first, leveraging data and delivering technology and tools at scale, engineering and development teams can deliver concrete benefits from AI, now and in the future.
Michaël Kolk, managing partner and global innovation practice leader at Arthur D. Little
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