Engineering is in the AI era. Across discovery, design, testing, manufacturing and in-service support, AI is reshaping how engineering and R&D companies innovate and operate.

AI is reshaping engineering at every level. It can accelerate individual tasks or transform entire engineering workflows through agent ecosystems. It reduces complexity through intent-driven interactions, enabling engineers to focus on outcomes rather than implementation details. Beyond productivity, AI powers intelligent products and physical systems that sense, predict, adapt and act autonomously, accelerating engineering innovation and creating new sources of value.

The promise of AI, when scaled across engineering organisations, is shorter development cycles and more differentiated products and services.

From AI ambition to engineering at scale

Access to AI tools is not enough. Competitive advantage comes when AI is embedded across engineering processes and products at scale. While nearly 80% of engineering companies have deployed generative AI, few have achieved meaningful bottom-line impact.

The barriers are structural. Fragmented product data, toolchains, and infrastructure make AI difficult to integrate and scale. Reliability concerns and weak governance limit trust in AI outputs, particularly in regulated and safety-critical environments. A shortage of expertise spanning both AI and engineering makes specialist capabilities difficult to build and sustain. As AI expands from digital assistants to agentic systems and intelligent products that interact with the physical world, organizations must also overcome challenges around integration, observability, safety and lifecycle management. Meanwhile disconnected pilots duplicate effort and struggle to deliver value.