Manufacturers should begin their adoption of artificial intelligence by identifying the operational constraints that are holding them back, rather than diving into the technology because of a fear of missing out, according to the chief information officer of one of the UK’s leading strategic manufacturing outsourcing specialists.
Ian Knight, who has more than 30 years of experience in industry and began his career as an apprentice at PP Control & Automation, said his business deliberately ignored the “platform and technology noise” and instead focused on where its own operations were being constrained.
“The starting question should not be ‘how do we adopt AI’ – instead it should be ‘where are we constrained, and what is the most effective way to remove that constraint?'” Knight said. He argued that when manufacturers answer that question first, the role of AI becomes far easier to define, justify, implement and scale. “The companies who gain the most value from AI will not necessarily be those who invest the most, it will be the ones who understand their constraints the best.”
Identifying Constraints Before Technology
The company, which was founded in 1967 and operates from a state-of-the-art, award-winning facility spanning more than 5,500 square metres in the West Midlands, has grown its turnover from £10.1m in 2009 to a record £25.3m in recent years, with projected revenues of £40 million by 2026. It employs more than 200 people and works with over 20 of the world’s most successful machine builders and original equipment manufacturers (OEMs), providing solutions for sectors including aerospace, food processing, medical, packaging, printing and advanced technologies. Its work includes machines that robotically milk cows, protect mobile phones from water damage and make Formula 1 cars faster.
Knight emphasised that his team’s approach avoided what he called a common trap: “investing in capability without a clear path to value”. Instead, the company ensured every application of AI was directly linked to a measurable operational outcome. This philosophy is consistent with broader trends in UK manufacturing, where a recent Make UK report highlighted that while AI could unlock billions in productivity gains, a significant gap remains between experimentation and operational impact. Only 2% of manufacturers state AI is widely embedded across their operations, fewer than 40% use it in some areas, and nearly one in five have not adopted it at all.
Extracting Structured Data from Unstructured Documentation
The specific constraint that PP Control & Automation’s engineering team chose to address was one of the most time-intensive and resource-heavy activities in electrical engineering: extracting structured data from unstructured documentation. Technical PDFs supplied by customers, which can often exceed 1,600 pages, were previously processed manually, consuming a significant amount of engineering time. Knight described the old process as requiring “days” of effort.
The AI solution his team implemented now converts those same documents into structured, repeatable outputs in a matter of hours. The system can embed rules, provide full traceability, and automatically identify missing components and mismatches. “Previously, this process required significant engineering time and manual effort. Now, those same documents can be processed in hours, not days,” Knight said. “And this is just the starting point.”
The impact on headcount has been substantial. The company achieved a 36% recovery in headcount capacity by targeting the 60% of time that had previously been lost to manual parsing and interpretation of technical documentation. Beyond the internal efficiency gain, Knight noted that the new capability removes a key point of friction in the early stages of customer engagement, accelerating the transition from an initial enquiry to executable work.
The use of AI to tackle data extraction is particularly significant given the wider challenges manufacturers face with data readiness. Research indicates that 65% of manufacturers struggle with having the right data for AI applications, and over 60% report their data as unstructured or difficult to use. PP Control & Automation’s approach demonstrates how a targeted, constraint-focused application can overcome that obstacle directly.
Phased Implementation and Scaling Benefits
Knight stressed that the company moved in phases, creating a foundation for what follows. “We moved in phases, creating the foundation for what follows, with subsequent phases looking at enhancing the structural data, its validation and integration into business systems. Each stage builds on the last, moving from data extraction to decision support, and ultimately to execution.”
The potential does not stop at internal use. Knight said the AI system “has the potential to be deployed internally and also has a capability offered to customers and potentially the wider market”. This aligns with PP Control & Automation’s broader strategic direction. Under CEO Pinaki Banerjee, the company announced an aggressive international acquisition drive in February 2026, targeting complementary firms in Europe, the US and Asia, with the aim of reaching £100 million in sales within four years. The company has also invested heavily in automation, including over £2 million in advanced cable preparation and testing technology.
PP Control & Automation is a founding member of the Manufacturing Assembly Network (MAN Group), a collective of specialist manufacturers, and established the UKMfgUnite initiative during the COVID-19 pandemic. Its leadership sees the constraint-first approach as a model that can help other organisations move from experimentation to impact. “We’ve proven what you can achieve by embracing AI, not as a tool in isolation, but a system built around real manufacturing constraints,” Knight added. “When manufacturers answer that question first, the role of AI becomes far easier to define. More importantly, it becomes far easier to justify, implement and scale.”
