Why AI pilots fail - and how manufacturers can break the cycle

Too many AI projects stall because organizations fall in love with the technology, not the outcome.

Real transformation only happens when projects are anchored in a clear business plan with measurable ROI - whether that’s increased throughput, lower energy consumption, improved yield, or reduced downtime.

However, with nearly 9-in-10 AI pilots across industries stalling before they can scale, how can manufacturers buck the trend?

Most pilots fail not because the algorithms don’t work, but because the data beneath them is fragmented, poor quality or locked away in silos.

On top of this, IT and OT teams often operate in isolation, making it hard to bridge the gap between innovation in the lab and execution on the factory floor.

Without scalable and trusted data foundations, AI risks remaining a proof-of-concept experiment that doesn’t have a huge impact on UK manufacturing production lines.

Establishing these foundations is now one of the biggest challenges, and opportunities, for the manufacturing sector. Manufacturers should treat AI investments like any other capital project: define expected returns upfront, align KPIs with operational goals, and track value creation over time.

By shifting from a “technology-first” to a “business impact-first” approach, manufacturers can prioritize the use cases that matter, secure executive buy-in, and ensure that investments in AI tools deliver sustainable, scalable value.

Unlocking AI value through trusted infrastructure

Through strong data foundations, not isolated pilots, manufacturers can turn AI into real gains.

Building unified, smarter data infrastructures that can absorb, integrate, and analyze data from all points throughout the value chain should be a top priority. Implementing these scalable data foundations ensures AI can adapt and develop as business operations expand.

Trusted IT infrastructure should become a key ingredient. In practice, this entails creating systems that are dependable, robust, and reliable enough to manage industrial data on a large scale. If you can trust your infrastructure, then it becomes an enabler of your data strategy.

If you can’t then it becomes a constraint, limiting the advantages AI can create. Importantly, trusted infrastructure not only supports AI, but it also helps cut down on wasteful spending and increases productivity, ensuring that projects yield tangible business value rather than remaining unfinished experiments.

Manufacturers should look to extract the ‘golden nuggets’ of information from unstructured data such as documents, presentations and emails to create actionable insights to maintain operational efficiency.

When digitized and stored, generative AI can process this information for troubleshooting and real-time optimization.

Bridging the IT-OT divide

Unlocking lasting transformation hinges on the successful integration of IT and OT teams. IT is the technology backbone of an organization, managing data and applications, whilst OT teams are focused on monitoring, managing and securing an organization's industrial operations.

Traditionally, these areas have functioned in silos, but today, this approach is no longer feasible. Manufacturers must form integrated teams that bridge the divide between IT and OT.

The success of Industry 4.0 relies on the convergence of IT and OT, enabling data flow and process optimization between production, automation and information systems throughout the entire value chain. The strategies and responsibilities of the IT and OT departments must be carefully unified to ensure a smooth transition.

Encouraging collaboration will enable a deeper understanding of factory-level challenges and needs. When combined, the teams can precisely handle supply chain optimization, predictive maintenance and real-time production insights.

Technology alone won’t achieve smart industry success. Instead, manufacturers must develop a collaborative culture, encourage innovation and adopt data-driven decision making to streamline processes and bring considerable efficiencies to businesses.

From pilots to proven impact

Competitiveness is seldom driven by isolated pilots. Manufacturers must commit to building trusted frameworks that make AI a cornerstone of their operations, building resilience and flexibility needed to adjust to shifting market needs.

However, this shift doesn’t just need new tools; it requires a change in mindset across the entire organization. Cross-functional ownership and the capacity to measure business results rather than merely technical ones are essential for successfully scaling AI.

Scaling AI to future-proofing manufacturing

To move from pilot to production, manufacturers must integrate AI, and data analytics, and ensure robust collaboration between IT and OT systems. With more collaboration, businesses can unlock smarter connectivity, streamline operations, and optimize their supply chains.

This transformation isn’t just about efficiency. Technology such as AI can build greater resilience, enhance security, and pave the way for sustainability and innovation. Those who lead with ROI and operational impact, not technology for its own sake, will be the ones who scale successfully.

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