Every organisation wants to make the most of its data in ways that work to their advantage, but the ongoing, interconnected challenges they face make this difficult.
The global economy is relatively resilient – Caxia Bank indicates that growth is around three per cent – but, as the International Monetary Fund says, uncertainty is the new normal. Geopolitical shocks, tariff changes and unpredictable policies are here to stay. Therefore, the best way to predict the future is to create it. In practice, that means using existing technologies – data, cloud and industrial AI – to support industrial growth and competitiveness.
Each of these technologies is evolving: internet of things (IoT) devices are increasingly being pushed to the edge, even as operational value chains shift to hybrid clouds. This is setting the stage for AI analytics to also be delivered at the edge. What’s changed is how these technologies are coming together and helping industrial workers to do their jobs better. An example is CONNECT, AVEVA’s industrial intelligence platform, which is built on Microsoft Azure. It incorporates our Industrial AI Assistant and will soon be integrated with Microsoft Fabric, bridging operational technology data with IT analytics to deliver advanced, AI-driven industrial intelligence in real time. Industrial teams, whether local or remote, are able to access and act on contextualised industrial insights in near-real time. Over time, this access enables the radical collaboration needed to unlock mutual value for the entire business ecosystem, whether internally within an organisation’s network or externally across the ecosystem. This trend will support five key technology shifts this year.
Connected ecosystems will drive innovation at scale
Perhaps unsurprisingly, hyperconnectivity could soon be the new standard. Silos still exist but the integration of technologies like edge-to-cloud systems, industrial AI and DevOps are bringing teams closer, enabling them to collaborate in radical new ways.
Ecosystem approaches – orchestrating networks across organisational boundaries – are now a strategic necessity for enabling value networks to tackle complex business problems no single organisation could handle alone. AVEVA’s research shows 43 per cent of executives at industrial companies are prioritising secure collaboration platforms, which means companies can share valuable operational data – like delivery schedules or asset performance – while protecting proprietary information.
AI is set to move from insight to execution
AI will remain a key focus this year. In fact, data from McKinsey indicates most companies (88 per cent) already use AI in at least one business function. Companies note its real-world impact: generative AI alone delivers up to 30 per cent productivity improvements for early integrators, says PwC. As AI evolves, agentic applications now use operations data and analytics to observe, plan and execute tasks autonomously in real time, although success depends on the underlying data integration platforms. Gartner predicts around 40 per cent of enterprise applications will feature task-specific AI agents this year, suggesting companies have a window of just three to six months to develop agentic capabilities to avoid falling behind.
Industrial AI already answers queries like ‘What was the average power usage in this area over the past 24 hours?’ Agentic applications will perform deeper analytics. For example, feed composition changes in chemical distillation processes typically require operator intervention. But process simulation software paired with a deep reinforcement learning agent can stabilise the system twice as fast as manual control. With agentic AI, manual rework goes the way of typewriter correction fluid.
Physical AI will power real-world automation
Agentic AI is just one sub-segment of physical AI, which refers to systems that interact with their physical environment, often automatically. Increasingly, we will see these systems integrated within a physical machine, such as a robot or vehicle. Together, data analytics, machine learning and robotics will change how industrial facilities operate, including in complex environments like factories.
Taiwanese pilot runs have shown how automating high-precision tasks such as screw tightening and cable insertion can cut deployment time by 40 per cent and reduce electronic assembly costs by 15 per cent. In the USA, drones have improved delivery times by a quarter and boosted efficiency by a similar 25 per cent, according to a joint report from the World Economic Forum and Boston Consulting Group. Expect the trend to develop rapidly, but again, deployment speed will depend on data quality. That’s equally true for embodied AI, the next horizon technology.
Living digital twins will become operational decision engines
By next year, digital twins will evolve from isolated replicas of discrete assets and processes into live and continuously updated simulation systems that reflect real-time changes across the entire value chain – thanks in part to better integration with data visualisation tools. Designed to deliver more than just insight into a single process, the technology is set to enable a 360-degree visualisation of complex processes, aiding budgeting, product development and resilience decisions.
Living digital twins integrate sensor feeds, operational data and supply-chain context, supporting key performance indicators ranging from less unplanned downtime, lower capital expenditure and lifecycle operating expenses, as well as faster product ramps. For energy- or resource-intensive industries, this spells lower energy and maintenance costs as well as a reduced environmental footprint. It also explains why analyst firm Markets and Markets expect the digital twin market to grow by 47 per cent annually to 2030.
Software-enabled workers will lead the way to Industry 5.0
None of these developments will be possible without people. We’re now facing a future where connected humans work alongside intelligent machines, called Industry 5.0.
Workforce redesign is now a major priority and leaders will need to plan for various human-AI scenarios, so organisations are prepared for the future of work. And this is occurring while labour markets are tightening across advanced economies. Teams will increasingly rely on AI-enabled tools to access industrial intelligence to perform better: connected worker wearables, augmented reality maintenance support, real-time collaboration across sites, and AI copilots.
These trends aren’t isolated. As we’ve seen, they’re converging into an interoperable technology stack that makes the most of industrial data, including from legacy systems. Hybrid industrial IoT supplies the connected data; AI-infused digital twins and physical AI make that information executable; teams and agents act on the resulting insights across partners and plants.
When networked together and governed by auditable software, these technologies will provide industries with a practical hedge against geopolitical shocks and improve supply chain resilience, giving teams the real-time industrial intelligence they need to keep assembly lines humming while helping address the broader challenges facing our planet.
Learn more about CONNECT at: www.aveva.com/en/solutions/connect
Rob McGreevy is chief product officer at AVEVA
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