Artificial intelligence is already being used at every stage of the product life cycle. Here's how it's changing the work product engineers are doing, regardless of the sector.
From robots that can play the piano and self-driving planes and boats, to a spice machine offering a personalised cooking experience — nearly every stand at CES 2025 in Las Vegas had AI embedded in some way. Nvidia CEO Jensen Huang told his 6,000-strong audience that the innovation has happened at an "incredible pace": "It started with perception AI — understanding images, words and sounds. Then generative AI — creating text, images and sound. Now we're entering an era of physical AI that can perceive, reason, plan and act."
For hardware designers and engineers, AI is expanding the toolbox. It's creating new functionality, enabling greater personalisation, optimising material choices, and reducing the time, labour, and cost of new product design.
Making Next-Generation Hardware a Reality
**AI-assisted design stage:** AI systems are being used to improve products at the start of the design process. Engineers can brainstorm ideas, run thousands of simulations, and catch design errors earlier. McKinsey estimates GenAI could unlock $60bn in productivity gains in product research and design alone, with a 70% reduction in development cycle times already observed when AI tools are used.
**Hardware optimisation:** AI algorithms can optimise the allocation of resources such as memory and processing units, and identify issues early through simulations. NASA, for example, is using AI to create components that are significantly stronger than previous designs while saving two-thirds of the weight.
**Software-hardware co-development:** AI hardware and software are interdependent parts of any product. This is what makes products able to learn and adapt to individual user behaviour — smart watches tracking steps and sleep patterns, smart thermostats improving energy efficiency.
**Accelerated testing:** AI is helping engineers replace many physical tests with virtual assessments that are faster and cost less. It can also predict hardware failures and performance bottlenecks, enabling proactive maintenance and design improvements.
Not AI for AI's Sake
Not every project requires AI. When assessing whether AI is appropriate, several factors need consideration:
**Data availability:** AI algorithms need large, high-quality datasets for training and validation. Where this isn't already present, the data collection work required can add significant time and cost.
**Privacy and security:** Customising hardware based on user data raises privacy issues. AI algorithms need to be ethically designed to prevent biases, and cybersecurity needs to be built into any solution from the start.
**Power consumption and heat:** AI hardware can be power hungry and generate significant heat — a real constraint for products where engineers are already asked to scale down without compromising performance.
**Supply constraints:** Pressure on the global semiconductor market may lead to shortages. US export restrictions on AI chips to limit China's access to AI computing power add further complexity.
**Integration complexity:** Integrating AI into existing workflows can require significant changes to established processes, risking manufacturing bottlenecks and delivery delays.
Finding the Right Partner
The potential of AI to reshape products and services is becoming a reality. But it requires an experienced team to guide organisations through — one with the in-house expertise to manage the process end-to-end and find the optimum solution for the end customer.
