AI Special Focus August/September 2026 www.pwemag.co.uk Plant & Works Engineering | 39 value added chain. At this stage many organisations do not have the proven capabilities inhouse that enable them to drive AI value. This is where proven holistic digitalisation partners can support: from initial strategy workshops, through to developing industrialised AI operation models. They often have the expertise and experience in developing effective integrated methodologies that combine sector-specific domain knowledge with highly developed technological expertise —and always with the aim of establishing AI not as an isolated technology but as a driver of innovation within the system. In a world where 74% of AI pilots never make production, a holistic approach towards AI deployments is becoming a critical competitive advantage for manufacturing and engineering organisations; and it can truly shift the needle for these companies, including within automotive and aerospace. With that in mind, the companies that succeed in understanding the five phases: not just as a linear process – but as an iterative learning cycle and that activate cultural, process-related and technological levers in a synchronised manner – they will be in a strong position to not only be AI users, but position their organisation as AI innovators that drive value and results. development, modular entry points and opt-in or opt-out scenarios facilitate tailored adaptations for the use of AI across organisations, which will be different for everyone. For example, an engineering company with a developed data infrastructure will likely start immediately with advanced analytics use cases, while a logistics provider would probably initially invest in data lakes and literacy programs. This adaptability transforms the process from a rigid framework into a living organism that adapts to the maturity level of the company. If you consider this further from a purely ‘technological perspective’, the coherent integration and efficient adaptation of various existing solutions becomes a critical enabler too. For instance, the seamless connection to ERP/MES systems via OPC UA interfaces; the use of established DevOps pipelines for MLOps; the embedded architecture of AI models in edge device clusters, and so on. These are all more than just technical details. They are vital requirements to be considered that deliver fast, predictable and viable success for companies. Driving AI value across organisations requires a strong combination of culture, process and technology, and aligning it into a coherent Step 4: Proof of Concept (PoC) Development – Agile implementation is the next step. This enables organisations to transform their concepts into validated prototypes under real-life conditions with a focus on their performance and user acceptance. Step 5: Use Case Review & Scaling – The last step involves a final evaluation to review the use case and how to scale it – if that is appropriate for the organisation (e.g. manufacturer). All this is made possible by developing and tracking various kinds of quantitative KPIs and qualitative factors. This will then result in well-informed decision-making for AI projects. Organisations can then decide on scaling, optimisation or discontinuing a particular project. Systematic frameworks for AI drive success The five-step process from use case ideation to PoC review forms the systematic framework for ensuring successful identification, evaluation and implementation of AI use cases. Additionally, following this kind of framework enables organisations to thoroughly and consistently evaluate AI applications and their quality at all stages. Following a structured approach also helps to create clearly defined handover points within organisations and enables companies to manage AI initiatives more transparently, effectively, efficiently, and to generate quantifiable business value. It means companies won’t flirt with failure as they deploy AI. Instead, the real success, comes from delivering projects that drive organisational value, and which are sustainable long-term – while also offering greater organisational agility. AI culture, AI translator, AI transformation Further, for many organisations adopting AI means that they need to develop a sense of open-mindedness about AI, as it will likely require a fundamental transformation in terms of culture. However, if AI succeeds, this will be worth it. Additionally, a clearly communicated AI strategy has the potential to become an organisational compass. It can set and synchronise resource allocation, talent acquisition and innovation culture. What is more, as AI strategies are deployed, it’s worth establishing ‘AI translator roles’ across the organisation. ‘Translators’ bridge the gap between data scientists and departments, while targeted upskill programs enable employees to actively shape the digital transition. In terms of process development, a balance between standardisation and flexibility needs to be achieved too. While the end-to-end process serves as a guiding principle for use case Bodo Philipp, CEO of MHP Consulting UK
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