ARTIFICIAL INTELLIGENCE n example, by triggering maintenance activities, balancing energy loads, or adjusting controls while human operators can schedule higher-value tasks. At the same time, the agent AI is collecting and analysing data to refine recommendations, creating a feedback loop over time to become more accurate. Semiautonomous AI operations may start small with minor process adjustments, but as confidence grows, they can become a trusted member of the team. The success of AI depends on quality, continuity, and contextualised streams of data. Traditional analytics relied on historical datasets to identify trends and generate reports. The shift to AI is interpreting live conditions as they evolve. The challenge many organisations face is not a lack of data, but a lack of accessibility and contextualisation. AI requires a unified data environment where data from previously unconnected silos is collected, sorted, structured, and accessed. In turn, an AI algorithm can make connections between previously unconnected inputs and outputs – for example, how one particular asset influences overall production. Investing in data management will put a business in a better position to scale as its AI model can use the same structure. New lines, machines, or components can be integrated into the unified data model without rebuilding it. Crucially, the unified data environment is what enables multiple AI systems to work together. Successful scalability of AI is about cohesion. Rather than investing in multiple tools that all operate on single points in the operation, businesses can get better results by creating a unified data environment to underpin AI. Human operators We’ve covered how AI can evolve into a collaborative member of the team, working in tandem with human expertise for the best possible result. The same can also be said for the example of China’s dark factories. Even the most advanced cutting-edge AI and robots can only deliver value because of the professionals who design systems, define parameters, then monitor and interpret progress. AI might have the power to analyse vast volumes of data, but it needs human expertise to apply insights and take judgements that affect production. As industrial companies adopt AI, it’s important to evolve and update the roles and responsibilities of operators along with the technology. People need to provide feedback, intervention, and guidance to make AI systems better. When hiring and training operators, it’s important for them to have confidence in using and validating AI’s recommendations. The future of industrial AI will be driven by people and AI agents working together in partnership. When systems are introduced with the right digital foundation in place, operators can quickly learn to trust that the outputs of the algorithm and that they reflect the whole business. So, building confidence in AI is not just technical but also human, and relies on alignment between capabilities and industrial realities. Industrial AI is no longer just for experimental pilot projects or limited by technology. As the technology continues to evolve, the real challenge for UK businesses is not access to AI, but readiness for it. The successful businesses will be the ones that focus on getting the foundations right: ensuring good quality data that is in the correct context and easily accessible, deploying cyber-resilient architectures and building operational alignment before scaling AI initiatives. These companies can, in turn, build on isolated pilots to realise the full potential of industrial AI, creating an environment defined by bringing together data and tools into a cohesive intelligent system. n Follow us on LinkedIn @Drives & Controls Follow us X Drives&C Controls & rives Join us Facebo Drives & C on X @Drivesn Forthe D on ok Controls Controls latest news visit Controls the Driv www.driv ves & Controls we vesncontrols.com
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