As physical AI enters our homes, workspaces and public infrastructure, it will have a transformative effect. Autonomous vehicles will become the norm on our streets, factories and warehouses will move to full automation, AI-enabled devices will assist in surgeries and medical procedures, and greater intelligence will be embedded into domestic devices.

Such is the emerging significance of physical AI, Gartner has identified it as a top strategic trend that will shape enterprise priorities over the next five years. There is no doubt the opportunities are great. But are organizations ready to roll out autonomous robots and drones, self-driving vehicles and industrial automation at scale?

Project leaders are finding that the deployment of AI in physical spaces, where they will coexist with humans, is very different from deployment of AI in an abstract cloud computing environment. Physical AI is requiring machines and systems to perceive what’s happening around them, interpret context and act autonomously in the real world.

For obvious reasons, these deployments must be proven safe and reliable. To successfully achieve this, leaders are required to overcome numerous practical complications, such as the constraints on edge devices, regulatory compliance and environmental considerations.

In addition to this, project leaders also need to convince their senior leadership teams that physical AI can be scaled across operations.

This will require them to show that the ongoing operational costs are manageable – and that a clear return on investment, be that through improved uptime, energy optimization or workforce efficiency, is evident. If they fail to demonstrate this, projects will never get past the pilot phase.

Embrace AI from the outset

To address these challenges, the first step for leaders is to ensure physical AI solutions and their benefits are factored in at the outset of any project. When organizations fail to include AI at the earliest stage – during the design and development of any product or operational environment – it creates challenges.

This typically results in fragmentation across hardware, firmware, applications and cloud computing – and results in a build-up of technical debt and diminishing returns. Siloed operational assets also result in disjointed workflows, operational bottlenecks and suboptimal performance.

Where this is the case, we often see organizations struggle to innovate and pivot whenever new commercial opportunities arise, such as through new smart consumer devices, factory robotics or in-vehicle infotainment.

Gartner estimates that the organizations taking a proactive approach in reducing, what it refers to as, “AI debt” will mature up to 500% faster over the next three years.

Enable edge inference

In contrast to cloud AI deployments, physical AI requires organizations to integrate real-time edge inference with several computing layers. Specific solutions will need to be engineered to compensate for the numerous hard constraints encountered on edge devices, including compute capacity, memory, power consumption, thermal limits and form factor.

These constraints typically force deliberate trade-offs in model size, update frequency, hardware selection and inference strategy. As edge capabilities continue to advance, these constraints can increasingly be addressed. Low power GPUs and specialized AI accelerators are expanding the range of workloads that can be executed locally.

Techniques such as model compression and quantization also help reduce computational demand while maintaining acceptable performance.

In particularly constrained environments, distributed edge architectures can be used to offload specific tasks to nearby devices. With these advances, what matters less is where intelligence runs, and more how deliberately edge constraints are engineered from the outset.

This will increase reliability, reduce reliance on cloud computing and lower the ongoing operational costs.

Run simulations

These edge engineering solutions will provide organizations with a proof of concept. But, to enable these to scale, project leaders also need to test scenarios and understand second-order impacts across operations. They will want to do this without disrupting production, compromising safety or committing capital prematurely.

Project leaders can derisk investments and validate their decisions, however, by leveraging advanced simulation platforms, such as NVIDIA’s Omniverse. This enables them to create digital twins of factories, assets and workflows, and allows teams to explore “what-if” scenarios.

Simulations allow teams to assess performance and identify constraints early. In energy intensive environments, for example, teams can assess power usage and sustainability trade-offs. This enables leaders to evaluate costs, right size capital investment, accelerate planning cycles and align stakeholders around a shared view of the future.

Build confidence

The use of simulations also helps to identify quick wins that will help leaders to demonstrate early success. This will provide crucial evidence that the technology is safe and reliable, but also that it can provide a clear return on investment.

This should act as the first phase of a staged rollout program. With physical AI, it is advisable that organizations take an incremental approach, as it will help to build confidence in the project among the senior leadership team – and remove the hesitancy that can hold projects back and prevent them from scaling.

To further instill confidence, project leaders should simultaneously roll out a structured organizational change management project too. This will prepare stakeholders and the workforce for the impact of physical AI within their operations.

Lead organizational change

The skill sets required in a physical AI project are different to those needed in a cloud AI deployment. Organizations need deeper expertise in embedded systems, real-time software and lower-level programming languages. As a result, there may be a need to augment workforces and evolve organizational structures.

To encourage acceptance of the technology, a clear communication strategy will also be necessary - one that explains how physical AI will provide value, and how the deployment will impact individual roles and processes. It may also be necessary to provide additional training and ongoing support throughout the roll out process.

Physical AI can no longer be considered a futuristic concept – it’s already transforming the world around us. It’s enabling organizations to innovate, go to market faster and seize commercial opportunities. It is also helping to optimize operational workflows, increase productivity and reduce costs.

If organizations want to take advantage and accelerate adoption, however, they must develop the solutions that work for their specific needs and derisk their deployment strategies. When they do this, organizations typically find they can scale physical AI quickly and reap the benefits sooner.

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