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FEATURE AI IN DESIGN & MANUFACTURING HOW TO REALISE THE


POTENTIAL OF INDUSTRIAL AI Charlotte Smith,


production systems. Putting this foundation in place


technical manager at


SolutionsPT, examines how AI is shifting


to a scalable solution that is reshaping


industrial operations W


hile generative tools and AI chatbots have captured public attention, artificial intelligence has evolved more quietly for


industrial applications, but far more profoundly. Across every sector, AI software is already influencing how assets are maintained, processes are optimised, and how workforce planning is transformed. The market is saturated with AI solutions that


promise value but may only work in isolation. Without cohesion, cutting-edge pilot projects typically do not translate well into industrial reality. To overcome this, operators need a clear view of how AI is maturing, what defines a scalable project, and how to make multiple initiatives work together. One example is a new breed of dark factories


in China that feature fully automated systems that optimise themselves without human input. This therefore shows how industrial AI can apply to enterprises in the UK and Ireland that want to improve efficiency across previously unrelated systems.


SCALABLE SOLUTIONS These dark factories were built from the ground up under controlled conditions and designed around a specific manufacturing model. However, most engineering and industrial leaders are working with brownfield and legacy sites, so the success of next-generation industrial AI will be defined by their readiness to scale. AI readiness needs to be viewed not as a single


step but as its own process that requires: • Structured data as AI depends on consistent and contextualised information. The context is important as it reflects how processes operate together rather than in single applications.


• Ownership of systems for defined accountability across IT and OT, ensuring that any AI insights are acted on by the right people at the right time.


• Alignment between digital strategies and OT realities. AI initiatives must be grounded in real operational constraints so that insights are relevant to the day-to-day needs of operators.


• Strong Guardrails to ensure that any AI hallucinations do not impact critical


2 DESIGN SOLUTIONS JUNE 2026 2


means that AI will be able to operate across fragmented environments, accounting for legacy systems working alongside new assets, as well as data and workforce silos, and complicated supply chains. Achieving this requires confidence that models can be refined to meet changing needs. Next-generation solutions will build on small and discrete pilot projects and integrate with strategy to apply enterprise-wide.


SEMI-AUTONOMOUS AI OPERATIONS While fully autonomous operations are only possible under specific conditions, semi-autonomous operations are within reach using the building blocks that companies have already created with modernisation projects. In semi-autonomous operations, an AI agent


will be deployed to monitor conditions, identify optimisation, and recommend actions in real time. Here, AI systems support human decisions rather than taking over responsibility. The important distinction between semi-


autonomous AI and a traditional pilot project is that the AI agent is not solving a specific problem in a controlled environment. Instead, it can have a wider impact, for 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. Semi-autonomous AI operations may start small but, as confidence grows, it can become a trusted member of the team.


AI SUCCESS 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 with 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 their 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 solutions 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 RESPONSIBILITIES Even the most advanced cutting-edge AI and robotics can only deliver value because of the professionals who design systems, define parameters, then monitor and interpret progress. Human expertise is needed to apply insights and take judgements that affect production. As AI is adopted in industry, it is 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 solutions better, however they need confidence when using and validating AI’s recommendations. The future of industrial AI will be driven by people and AI agents working together in partnership. Building confidence in AI is not just technical but also human, and relies on alignment between capabilities and industrial realities.


DEPLOYING AI AT SCALE 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. In turn, these companies can 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.


SolutionsPT www.solutionspt.com


www.designsolutionsmag.co.uk


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