Why 2026 is the Year for Industrial AI
The substation fire at Heathrow and the Iberian Peninsula blackout both laid bare the vulnerabilities of large, complex industrial operations. Rising instability, brittle supply chains, and the impact of accelerating climate change, show that many industries remain unprepared. Organisations that act now to capitalise on AI opportunity in industry stand to gain a decisive edge.
The Accelerating Shift Towards Industrial AI
Data-rich industries like insurance and finance are no strangers to AI and its benefits. The digitally native operations, the structured format of data, and the fungibility of processes make these sectors natural proving grounds. Early investment has sharpened operations, reduced costs, and powered competitiveness in an increasingly saturated market.
Industries like defence, infrastructure, manufacturing, energy, and logistics, however, have struggled to translate AI’s potential beyond isolated pilot projects. It’s estimated that the failure rate of AI projects in these sectors may be as high as 80%. Unlike digital-first sectors, these operations are predominantly in the physical world, where progress traditionally relied less on software and data, and more on people, machinery, and complex systems. Their operational complexity and fragmented data environments make AI harder to implement.
Enter, Industrial AI; the application of artificial intelligence to optimise and support real-world operations, leveraging data from machines, sensors and physical assets to deliver smarter, more efficient outcomes. The barrier to entry is significant, requiring integration with messy, legacy systems and workflows, as well as work to unify disparate, fragmented data sources. Though the challenges are real, the opportunity to boost resilience, target decisions, and find efficiency headroom is both large and increasingly pressing.
When Change Meets Opportunity
Recent years have delivered a series of destabilising events that continue to reshape the geopolitical and industrial landscape. The COVID pandemic was swiftly followed by Russia's invasion of Ukraine, and the outbreak of further conflict in the Middle East. There has also been a swathe of seismic political shifts in Europe, whilst the re-election of Donald Trump and his aggressive protectionist economic policies have caused significant repercussions worldwide.
This occurs against the backdrop of numerous climate emergencies, while global temperatures continue to rise. Wherever you look, the world is changing, dramatically, and at an accelerated rate.
Volatility has exposed a level of fragility in many industries in the physical world. The COVID pandemic tested supply chains around the world to breaking point andthe Ukraine-Russia war drove oil prices skyward, stoking a global inflationary crisis and sharp increases in the cost of living. In the UK, a series of substation fires recently caused widespread disruption at Heathrow, while unpredictability in power supplies in Spain caused a blackout which has been attributed to at least 8 deaths on top of the hefty financial toll.
Volatility lays bare fragility, but also creates openings for bold, strategic adaptation. Schneider Electric, for example, has partnered with NVIDIA to deploy AI-driven digital twin technology to improve its energy consumption monitoring and forecasting. This enables more intelligent balancing of grid demand, reducing energy waste during peak usage and contribute to a more stable energy ecosystem. Similarly, Hitachi uses cyclone forecasting from ClimateAi to anticipate disruptions and rebalance supply chains before extreme weather strikes. The financial benefits of this forward thinking are clear, as studies have revealed that $1 invested in resilience and disaster preparedness saves $13 in economic impact, damage, and cleanup costs after the event.
Against the backdrop of a growing necessity for resilience and agility, there is also a rapid commoditisation of data hygiene and processing. Principally, this is driven by increasing capability of off-the-shelf data-processing solutions using foundational text and image models, together with decreasing costs for industry-ready sensing, compute, and networking. This enables many digitally non-native industries to begin to ask deeper questions and extract information from their data. This would have been impossible even just a few years ago due to the historic cost of structuring data and integrating AI but is now flipping volatility from risk to opportunity.
Resilience Is the New Competitive Advantage
Operational resilience is no longer a bonus for the already well-resourced; it's fast becoming the differentiator between thriving and failing for all organisations in complex, volatile environments.
AI offers the potential to identify and understand sources of risk, as well as refine and optimise operations to mitigate them, and AI-powered risk detection, quantification, and tracking are now critical capabilities, not optional additions. Firms without data-driven support will tend to overestimate risk in areas lacking visibility, which is inefficient and drives costs. Worse still, a lack of visibility can lead to myriad unknowns, resulting in blindness and absence of contingency planning for unseen catastrophic scenarios.
Climate change transition planning is no longer merely a question of compliance and arguably superficial outputs. It is now a core element of long-term strategizing. As insurers increasingly demand credible plans to mitigate environmental and systemic shocks, the ability to model, quantify, and manage these risks has become a marker of operational maturity, not just a regulatory tick-box.
Organisations that rely on complex supply chains must also accommodate this risk, as even the slightest disruption can cause far-reaching consequences. If companies can effectively anticipate and plan for these events, then they can insulate themselves from their financial impact.
Turning Opportunity Into Reality
The commoditisation of data, increasing global volatility, and the rising imperative of resilience have laid the groundwork for a shift that goes beyond tools or models. Industrial AI reflects a broader evolution where industrial strategy must adapt to a world characterised more by agility than stability.
The critical step now is for organisations to identify rigorously their most consequential risks and opportunities and deploy investment and technology where the strategic value is highest - not where the hype is loudest. While advanced models, new technology, and innovation programmes promise excitement and novelty, they are a distraction to the pursuit of real impact. Only through resolute focus, and the disciplined pursuit of operational value will the winners realise the full potential of Industrial AI.
Sprint Campaign: Industrial AI
From predictive maintenance to advanced process automation and smarter supply chain management, Industrial AI is the application of AI technologies in industrial settings to transform operations across various sectors—including manufacturing, and robotics. This campaign will showcase the game-changing potential of Industrial AI, and how it can solve industry challenges, drive efficiency, increase productivity, boost innovation, and redefine the future of industrial operations.
Event Round-ups
Authors
Alistair Garfoot
Director of Strategic Services at Mind Foundry, Mind Foundry
Alistair Garfoot is Director of Strategic Services at Mind Foundry, where he works to deploy AI in industrial contexts. With nearly a decade of experience at the forefront of AI, Alistair uses his technical background to ensure that developed AI solutions align with business requirements and drive operational impact at scale.
