Five industrial AI trends that will actually matter in 2026 - and what leaders should do now

THE ADOPTION of AI in industrial contexts is still recent, with limited pilot implementations and proofs of concept despite high expectations. As AI systems move from experimentation to practical application, leaders are under pressure to demonstrate value beyond technical capabilities.

"AI will stop behaving like a side project and start behaving as infrastructure," said Giedrė Rajuncė, CEO and co-founder of GREÏ, an AI-powered process intelligence platform for large physical sites. "But the uncomfortable truth is that most organisations are still applying AI to broken processes. In 2026, success will come from redesigning operations first, then using AI as a force multiplier."

According to Rajuncė, leaders preparing for the next phase of AI-driven operations must start by fixing the fundamentals - simplifying workflows before introducing automation and treating AI as a co-worker rather than a replacement. This also means investing in strong data foundations, security, and governance, while favouring interoperable platforms over fragmented tool stacks.

In this context, several AI and deep-tech trends stand out as genuinely transformative for industrial operations in 2026.

1. Agentic AI systems replace dashboards with action

One of the biggest shifts in 2026 will be the rise of agentic AI systems, which autonomously execute workflows across operations. “One example that illustrates the potential of these agents is an on-site accident scenario. Rather than alerting a manager, an AI agent can initiate a work order, contact the relevant vendor, check parts availability, and coordinate scheduling, handling the operational response with minimal human involvement,” explained Rajuncė.

However, these capabilities also expose weak foundations. Gartner predicts that 40% of agentic AI projects will fail by 2027 because they are misapplied and lack a singular business issue to address. In practice, this means agentic AI amplifies both strengths and inefficiencies, forcing leaders to confront operational reality faster than ever before.

2. Physical AI and industrial robotics move to orchestration

AI is rapidly moving from software into the physical world through intelligent robotics. The World Economic Forum identifies three systems that will increasingly coexist: rule-based robotics for predictable tasks, training-based robotics for variable environments, and context-based robotics for unpredictable conditions. This shift is already delivering measurable results, with AI-orchestrated fulfilment centres achieving faster delivery times and more skilled operational roles.

While specialised systems drive current gains, humanoid robots are expected to scale rapidly over the next decade, with projections of 13 million units by 2035, as organisations begin orchestrating physical AI across machines, people, and processes in real time.

3. Domain-specific AI models overtake generic generative AI

While generic large language models continue to dominate headlines, their limitations in industrial environments are becoming increasingly clear. Lacking context about specific facilities, equipment, and safety constraints, generic models risk producing inaccurate or even dangerous outputs. By contrast, domain-specific language models tailored for manufacturing, logistics, and construction are proving far more effective. Some estimates predict that by 2028, more than half of enterprise GenAI deployments will rely on such specialised models.

"Generic GenAI is overhyped for industrial use," Rajuncė continued. "A model that doesn’t understand your HVAC system, wiring, asset history, or safety thresholds is a liability. In physical environments, context isn’t a nice-to-have; it’s the difference between insight and risk. That’s why domain-specific models are overtaking generic ones in serious industrial deployments."

4. Multimodal AI unlocks real-time operational intelligence

By 2026, multimodal AI systems capable of processing text, images, video, sound, and sensor data simultaneously will become mainstream. For industrial operations, this enables entirely new forms of situational awareness.

For example, in manufacturing, vision systems can correlate visual defects with vibration patterns, acoustic signals, and thermal data. In construction and logistics, drone footage can be analysed alongside equipment telematics and structural sensor inputs. The result is significantly higher accuracy and faster response times than any single data source can provide.

5. Predictive maintenance and frontline usability become decisive

Predictive maintenance continues to deliver some of the clearest returns in industrial AI, with organisations reporting maintenance cost reductions of up to 40% by shifting from time‑based preventive models to data‑driven prediction. At the same time, frontline usability is emerging as a critical differentiator. World Economic Forum research shows that AI initiatives fail when frontline workers are treated as passive users rather than active knowledge contributors.

"If an AI system can’t explain why it’s recommending an action, trust disappears," explained Rajuncė. "Frontline teams want clarity. If an AI agent suggests shutting down a machine or flagging a safety issue, people need to see the reasoning behind that decision."

For more information:

www.grei.ai