38 | Plant & Works Engineering www.pwemag.co.uk August/September 2026 Special Focus AI Artificial intelligence (AI) is currently at a crossroads between hype and actual value-adding implementations. According to Gartner studies, while 92% of companies are increasing their AI investments, 74% of initiatives fail before they are ready for production. This discrepancy entails a number of fundamental problems, ranging from personal sensitivities, to operational feasibility, and the strategic viability of the projects. Critical errors made along the way are almost always due to poor methodology right at the start of development. The problem: most organisations treat AI implementation as a technological experiment and not as a strategic value driver. Unstructured approaches lead to the typical pitfalls: solutions looking for a problem, use cases without business objectives; and pilot projects becoming stranded in the “proof-of-concept desert” due to insufficient consideration of measurability and subsequent scalability. However, corporate practice shows things can be done differently. The reason for this is down to the ability of many companies in identifying use cases that make a clear business impact, instead of utilising purely technology-driven solutions focused approach. Starting here enables them to eliminate poor investments at an early stage. It also incorporates a structured evaluation system that maintains, from the outset, the scalability of pilot projects in a way that moves them towards true industrial value creation long term. Five steps towards driving AI value Step 1: Use Case Ideation – Many organisations have a difficult time with knowing where to start with AI. To change this, manufacturers would do well to carry out a systematic pain-point analysis. This includes various explorative methods to identify appropriate business-relevant AI use cases for their organisation. These use cases should offer clear economic value and strategic business relevance not just for the ‘now’, but for the future. Step 2: Use Case Evaluation & Prioritisation – This step usually involves considering multidimensional evaluation criteria (e.g. strategic fit, ROI, feasibility) to help with identifying the use cases that offer a high potential. At this stage, it’s also worth creating a value–effort matrix to ensure maximum transparency that supports ongoing decisionmaking around AI. Step 3: Use Case Refinement – The next step is to develop a detailed catalogue of the various requirements that will make suggested use cases operable and feasible. This also helps with use case refinement as it identifies gaps between existing and required resources. Bodo Philipp, CEO of MHP Consulting UK shares a step-by-step approach that all manufacturers, especially those within the automotive and aerospace, can take towards building a successful AI business case, starting with the creative ideation and multi-stage quality gates, that is followed by the implementation and review of the proof of concept (PoC). Each phase serves as a strategic filter that checks for relevance, feasibility and scalability. Taking AI from pilot to production
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