The true promise of artificial intelligence (AI) is being revealed as autonomous agents transition from theoretical constructs to practical applications across various industries. It’s like having self-directed co-pilots deep inside factories, refineries and grids that pay for themselves from launch day by delivering cost savings, efficiency gains and improved operational resilience.
AI insights already support empirical decisions in business. Agentic AI turns those insights into execution in real time. And as more elements of industrial workflows are automated, they become faster, smarter and more innovative.
With applications across the industries, Agentic AI can increase uptime, accelerate time to market for product design and reduce operational waste, helping companies achieve sustainable growth with lower resource investment.
Getting to grips with Agentic AI
An AI agent is a kind of software application that sits and works behind the industrial operating technology platform. It runs autonomously, often 24×7, looking for relationships between processes and controlling operations or making suggestions to operators for investigation to further improve outcomes.
One way to think of it is as an AI colleague that performs a task by accessing and interpreting system data such as operating temperatures, pressure specifics or safety metrics. For example, operators are concerned over the performance of a feed pump and ask the system to automatically create an AI model to monitor the pump 24×7, alerting them when there is something unusual detected.
When Agentic AI is combined with generative AI and large language models, the barrier to entry is lowered to where you no longer need to be a data scientist or AI expert to be able to get full value from the solution. You can use natural language to tell it what you want, and the system uses the same language in response. The result is a powerful, humanized system that offers the ability to be adopted by a much broader audience.
That wider adoption could see the global AI market add a staggering $15.7 trillion to the global economy within just five years. That includes generative, agentic and other types of AI, PWC data estimates. More than a third of that total will likely come from productivity improvements.
Agentic AI at work in industry
Agentic AI brings value across industrial applications. The technology will function as an extension of existing industrial tech stacks, making it easy to use in augmenting human output.
- Faster ROI and more scalable digital infrastructure: A major application is in developing digital twins. These virtual models of real-world systems are crucial to optimizing supply chains, understanding and predicting asset performance, and improving shop floor outputs. But they need clean, continuous data to work and mapping and aligning this data across systems is a tedious manual process at present. For example, if one system identifies a piece of equipment as Pump 101 and another labels it PMP 101, a worker must manually establish that connection. Agentic AI can automate most of this work, resolving asset identities with up to 80% accuracy, meaning that engineering teams spend less time reconciling spreadsheets and more time using digital twins for bigger questions.
- Better risk visibility and shorter decision cycles: Today’s AI assistants can handle basic queries like showing average power use over the past 24 hours. Agentic AI takes this a step further by performing deeper analytics: collecting data, running calculations, determining patterns and relationships, and generating insights autonomously. It’s the difference between getting a temperature reading and being told your system is trending toward a failure due to multiple symptoms. With a more intelligent operating model, industrial teams have better insights into risks and can faster.
- No-code solutions and intuitive recommendations: Agentic AI could similarly transform vexing issues around asset management by addressing data fragmentation and complexity issues. A power plant operator can use AI to quickly create a monitoring agent for an underperforming condenser. The AI collects data from various systems, evaluates performance and creates a detailed, transparent dashboard for teams to track issues such as fouling and their associated costs, all while continuing to monitor using AI round the clock. Here, automation makes complex equipment simple and intuitive.
- Less downtime and more resilience: We see many monitoring and troubleshooting applications. Across many industries, Agentic AI can monitor critical infrastructure in real time. By analyzing sensor data from internet-of-things devices, the system can flag risks, isolate issues or even act autonomously. With less manual micromanagement, businesses can clock higher uptime with smarter asset protection.
Why Agentic AI matters now
With such a broad range of use cases, the operational upside of Agentic AI is difficult to ignore. These systems identify risks and correct course in real-time, significantly reducing costs and speeding up industrial workflows.
In the near future, industries can expect to scale their operations faster. We will soon see Agentic AI monitoring systems that identify issues and send alerts when something goes out of tolerance, adding immense productivity and efficiency benefits. As AI evolves, it will take on many more increasingly complex tasks without requiring major infrastructure changes. And as we know from other types of AI, these systems learn and adapt over time, becoming more effective the longer they are deployed.
Despite these advantages, adopting Agentic AI requires addressing several challenges, including building robust data infrastructure and smooth integration with existing systems. Clean, bias-free data is essential for reliable AI decision-making, while transparency in autonomous decisions builds trust.
Strategic investments in data frameworks and continuous AI learning will mitigate risks and drive long-term returns on investment. Most crucially, human oversight will always be essential to clarify, correct and refine AI inferences and outputs.
Humans are already working more closely with intelligent machines. Agentic AI is poised to be the next major technological shift in industry, elevating this collaboration into an intuitive give-and-take partnership. When the grunt work is taken care of, humans are free to focus on high-level strategy and innovation.
AI is humanity’s digital twin. Balancing AI automation with human judgment will ensure safer, more effective decision-making, enabling us to do more with less while meeting our sustainability goals.
