AI in manufacturing is no longer an experiment in marginal gains. It is rapidly becoming the core architecture of competitive industrial strategy, enabling cost control, resilience, and supply chain localisation at scale.
Despite a growing catalogue of AI tools built for industrial use, manufacturing remains slower than most sectors to adopt and scale them. This lag is often attributed to technological limitations, including legacy equipment, fragmented data environments, and operational complexity. Yet beneath these practical challenges lies a deeper issue: mindset.
Manufacturing leadership has traditionally leaned towards caution, with investment cycles favouring incremental gains over systemic transformation. This aversion has, in many cases, been reinforced by a misreading of what AI requires to deliver value. “Until recently, there was no dramatic motivation to change how things were done,” Karim Saleh, CEO at Cerrion, says. “Many organisations lacked digitisation, and the pandemic exposed just how vulnerable those models were. Executives often misunderstand the complexity of AI implementation or underestimate the speed at which it can deliver value. The result is a hesitancy to commit beyond pilot projects, especially when leadership continues to view AI as an experimental innovation rather than essential operational infrastructure.”
According to Saleh, what is needed is not a wholesale reinvention of operations but a strategic reset. “Leadership needs to clearly understand and embrace AI’s tangible benefits, the ROI, minimal disruption, and significantly reduced production losses and costs,” he adds. “The shift has to be in perspective. AI is not a special project—it is part of the base layer of how manufacturing will run going forward.”
AI is not waiting for the manufacturing industry to feel ready. Start-ups and fast-moving firms are already proving that agentic AI can be integrated quickly, deliver returns almost immediately, and adapt to the unique operational rhythms of factory environments. When AI is deployed not as a solution looking for a problem but as a platform aligned with strategic goals, its momentum carries forward across teams, departments, and sites.
Automation is redefining the geography of manufacturing
As geopolitical tensions rise and global supply chains face renewed pressure from protectionist tariffs, climate shocks, and transportation bottlenecks, manufacturers are once again reevaluating where and how they produce their products. In this recalibration, AI is emerging as a key enabler of localisation.
While traditional cost arbitrage will always favour specific offshore models, AI is starting to level the playing field by reducing reliance on labour-intensive processes. “Agentic AI can dramatically reduce inefficiencies, production losses, and reliance on manual labour,” Saleh explains. “When you factor in the potential to lower wastage, improve quality control, and optimise labour deployment, the case for local manufacturing becomes significantly stronger.”
This has material implications. Local production becomes more feasible when AI ensures consistency, reduces downtime, and enhances traceability without requiring additional headcount. In one deployment example, manufacturers in the food sector utilised existing video infrastructure to detect contamination or packaging anomalies in real time, eliminating entire layers of manual inspection while enhancing quality assurance. The knock-on effects are both economic and strategic, reduced loss, improved safety, and a simplified compliance landscape.
Moreover, AI supports agility. Instead of building scale for efficiency, manufacturers can now distribute operations more broadly and control them with digital coordination. This shift toward regionalised, tech-enabled production is allowing businesses to absorb shocks, reduce emissions from long-haul transport, and get closer to customer demand, all while staying competitive.
The competitive logic of low-cost labour alone no longer holds. “We are already seeing reshoring strategies being seriously evaluated,” Saleh says. “AI does not erase cost advantages overseas, but it changes the calculus. The gap is closing, and for many, the trade-offs are finally worth it.”
AI must integrate seamlessly into existing systems
Despite the narrative that AI is futuristic or disruptive, its most powerful implementations often come from technologies that blend quietly into existing workflows. One of the most consistent mistakes manufacturing executives make is assuming that AI requires wholesale digital transformation before it can be deployed. In many cases, the opposite is true.
“Executives consistently miss the low-hanging fruit,” Saleh says. “There is huge value in agentic AI applications that do not require new infrastructure. Video AI that uses existing CCTV to detect quality defects, predictive maintenance that flags early anomalies, or automated safety checks are areas where the return is not theoretical. It is already happening.”
By targeting use cases with immediate, measurable benefits, manufacturers can create a virtuous cycle: deliver quick wins, build confidence, and then scale with purpose. Cerrion’s work in sectors such as glass, packaging, and food production illustrates this well. In one European glass plant, the use of AI-driven monitoring on legacy CCTV systems cut incident response times by more than 36 per cent. That not only prevented scrap but triggered a shift in how teams approached shift coordination and incident reviews.
In another deployment, video AI helped a global packaging manufacturer reduce workplace risks by flagging dangerous anomalies on high-speed production lines. Rather than requiring real-time human monitoring, the system issued automatic alerts, enabling faster and safer intervention. The outcomes were not just performance gains; they were cultural. Fact-based decision-making improved, and teams began to shift from reactive troubleshooting to proactive optimisation.
Crucially, these gains were achieved without the need for new sensors, costly retrofitting, or rewiring data pipelines. “The assumption that AI needs massive digital maturity is just wrong,” Saleh says. “What matters is reliable connectivity, the right input signals, and a system designed to plug into reality. That is the foundation for scale.”
Workforce transformation requires transparency, not platitudes
The notion of a hybrid AI workforce is often presented as a win-win: machines take over routine tasks while humans transition into more fulfilling, strategic roles. But in practice, the shift is more nuanced. Not every worker displaced from a repetitive task is instantly repositioned as a data analyst or creative problem solver.
“AI introduces new roles and new tensions,” Saleh continues. “But it also injects energy into an industry that many had written off as outdated. We are now seeing manufacturing attract data scientists, systems engineers, and process experts who want to work on real-world challenges. AI is turning the factory into a frontier of technical innovation.”
Buy-in, however, depends on trust. Cerrion’s approach to labour transformation focuses heavily on communication and training. Operators are shown how AI helps them, not replaces them, by taking over repetitive monitoring, catching issues they may miss, and reducing stress in high-pressure environments.
In low digital literacy contexts, the priority is usability. Interfaces must be designed with the operator in mind, with clear visuals, low cognitive load, and rapid feedback loops. “You cannot just drop AI into the shop floor and hope for acceptance,” Saleh says. “When operators see how AI improves safety, reduces stress, and keeps lines moving, trust builds organically.”
Skills development is also central to long-term success. As factories become increasingly data-centric, there is a growing demand for new roles that sit at the intersection of digital and physical systems, where individuals can interpret outputs, refine inputs, and work with AI as a collaborative system rather than a black box. Those roles do not replace the existing workforce; they evolve it.
Sustainability and compliance are now inseparable from performance
In the past, ESG initiatives and productivity programmes were often managed separately. Now, AI is demonstrating that sustainability and efficiency are not only compatible but also interdependent. When used for production monitoring, predictive analytics, and quality control, AI reduces waste, saves energy, and mitigates environmental risk, all while lowering costs.
Saleh offers the example of glass manufacturing, where modest changes in yield or scrap rates can translate into significant carbon savings. “A one per cent yield improvement on a line producing 100 tonnes per day equates to more than 130 tonnes of carbon savings per year,” he explains. “Multiply that across multiple lines, and the numbers are significant. And because it saves money too, this is not a trade-off; it is a strategic advantage.”
In the context of emerging AI regulation, particularly in food, healthcare, and high-risk environments, compliance must be embedded into the system rather than being added as an afterthought. Cerrion aligns its deployments with European standards, ensuring explainability, traceability, and privacy from day one. This enables smooth cross-border operation while giving manufacturers confidence that they are audit-ready.
“Transparency is not just for regulators; it is for operators and stakeholders too,” Saleh adds. “Our AI governance module is designed to provide visibility into how decisions are made. That is critical for building trust in high-stakes environments.”
Health and safety also benefit. In several of Cerrion’s industrial deployments, AI has helped teams identify the root causes of recurring incidents, redesign workflows, and enforce safety routines. By monitoring environments continuously and flagging outliers in real-time, AI systems become part of the factory’s immune system, detecting threats before they escalate.
The broader point is clear: AI in manufacturing is no longer about nice-to-have insights or future-facing strategy. It is a live system for managing cost, risk, workforce, and environmental impact. When integrated with discipline, it becomes the architecture for how factories will think, respond, and compete in the decade ahead.
