Britain’s smaller manufacturers are at a crossroads. The UK trails its global peers in robotics and AI adoption, ranking just 24th in robot density worldwide with around half the automation of the European average. At the same time, output values are nudging upward while productivity is slipping, with output per hour worked falling 0.8% year-on-year in the second quarter of 2025. For SMEs, which form the backbone of the industrial sector, the choice is stark: take the first steps into automation now or risk being left further behind.
The obstacles are well known. Cost, skills, and confidence weigh most heavily on smaller firms. Simon Eggleton, Divisional Sales Manager – EURAF at TNA Solutions, explains: “For smaller manufacturers, the main barriers are the cost and complexity of installing multiple standalone machines, as well as the need for skilled operators to run them. Many SMEs also struggle with limited floor space, which makes adding new equipment difficult.
“Larger businesses tend to face different challenges — often around scale and integration — such as ensuring consistency across multiple high-capacity sites or introducing new SKUs without disrupting existing high-volume operations. In contrast, smaller players may hesitate because they fear investing in systems that could quickly become obsolete or require more expertise than their workforce can provide.”
But even when financial and physical constraints can be managed, there are still digital hurdles. Claire Biggerstaff, Manufacturing Market Lead, EMEA at Zebra Technologies, says: “Factories of all sizes share similar challenges when it comes to implementing AI solutions. These include knowing where to begin, finding the right partners and resources, keeping pace with new technologies, and having appropriate goals and return on investment metrics.”
She points to data from Zebra’s Manufacturing Vision Study, which found that 54% of manufacturers in Europe (and 61% globally) expect AI to drive growth by 2029 — a sign of the urgency but also the uncertainty surrounding adoption.
One of the biggest sticking points is data. “SME and mid-cap factories need to make sure they have good data quality and management, and can remove obsolete, inaccurate, and duplicate data,” Biggerstaff adds. “Data owners and storage locations also need to be aligned and accessible for AI models to train and test on and access in real-life.” Without such preparation, even promising projects can falter.
If barriers are clear, so too are the pathways forward. Eggleton points to the power of simplification: “One of the most effective approaches is to simplify operations by reducing the number of separate machines and interfaces SMEs need to manage. For example, in the food processing sector, integrating distribution, seasoning, and packaging makes it much easier for smaller teams to operate lines efficiently, with fewer training demands and less scope for human error.”
Automation in practice
The story of Burts Snacks illustrates this approach. Working with TNA Solutions during its early growth phase, the company invested in a single integrated seasoning and packaging line. That gave the Devon-based manufacturer the efficiency and reliability to scale quickly. Over time, further systems were added step by step, including on-head seasoning, high-speed packaging, and integrated distribution. Each stage simplified operations, reduced manual intervention, and allowed rapid product changeovers. Today Burts runs continuous operations across more than 100 SKUs. By working with TNA as a long-term partner, it has shown how even modest initial investments in automation can evolve into a platform for round-the-clock production.
Other SMEs are pushing further into advanced automation, often with the support of specialist integrators. In one example cited by Zebra Technologies’ Biggerstaff, a European automotive supplier introduced a vision-guided robotics system to inspect battery caps for electric vehicles. Built on deep learning software, the solution constantly improves its ability to detect microscopic defects, providing an edge in precision manufacturing that traditional inspection tools cannot match. Similar approaches are helping OEMs cut defect rates in complex assemblies like car doors, and are even migrating into sectors such as pharmaceuticals and food.
For those not ready to make such leaps, incremental steps can still deliver tangible benefits. Eggleton notes: “Full-scale automation doesn’t have to happen overnight. Smaller manufacturers can start by introducing smarter, integrated systems in stages. For example, making sure equipment communicates so that lines can adjust automatically, or adding gentle product handling to cut waste. These incremental steps can deliver immediate gains in efficiency and consistency, while giving businesses the confidence and flexibility to grow at their own pace. To call a cliché, it’s a marathon, not a sprint — and even modest automation moves can make SMEs more competitive without overextending resources.”
Crucially, technology is only part of the answer. Skills and people remain central. Biggerstaff emphasises: “Manufacturing leaders understand how labour initiatives must extend beyond improving worker efficiency and productivity with technology. Six in 10 leaders rank ongoing development (61% in Europe, 65% globally), retraining/upskilling (66% in Europe, 65% globally), and career path development (63% in Europe, 62% globally) to attract future talent as a high priority for their organisations.”
The UK’s SMEs, then, face a moment of decision. The barriers are real: high costs, tight spaces, limited skills, uncertain data foundations. But the examples show these obstacles can be overcome — whether by starting small with integrated systems, partnering with trusted suppliers, or steadily building skills alongside technology.
The crossroads is here and now. Delay risks locking in the productivity gap; acting, even cautiously, opens a path to resilience and competitiveness. Automation may be a marathon rather than a sprint — but for Britain’s SMEs, the choice to start running cannot wait.
