4th July 2025
Welcome back to the Shock of the New campaign, which originally ran five stories from July – November 2023, concluding with a downloadable e-book comprising all stories and artwork.
Part II further investigates “The shock of the new” highlighting the dynamic relationship between innovation and the human response to change. It acknowledges that whilst change can be challenging, it can also lead to profound advancements and opportunities for societal and sustainable development.
Supporting artwork featured throughout this article is a continuation of the ai generated artwork featured in the original Shock of the New campaign. It uses ai prompts to depict emerging markets and technologies in different modern art styles – A nod to the inspiration of Robert Hughes’ documentary and book of the same name, along with the recent disruption and adoption of artificial intelligence technologies.
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The fourth industrial revolution has a new engine, with manufacturing on the cusp of its most transformative era since the invention of the assembly line.
Whilst steam, electricity, and digitisation each defined previous industrial revolutions, the defining force of the current age – the so-called Fourth Industrial Revolution – is artificial intelligence (AI).
No longer a futuristic concept, AI is already embedded in the industrial landscape, accelerating product development, enhancing operational efficiency, and reinventing how businesses manage people, processes, and performance.
This article explores the evolving role of AI in modern industry, the opportunities it presents, the barriers it must overcome, and why machine builders and OEMs cannot afford to view it as optional.
AI in manufacturing spans a wide spectrum, from narrow machine learning algorithms to full cognitive automation. While the term “AI” often evokes ideas of sentient robots, most industrial applications today fall under the umbrella of narrow AI – systems designed to perform specific tasks using logic, pattern recognition, and data analysis.
According to a 2023 report by Capgemini, over 60% of large manufacturers worldwide have adopted AI use cases in production environments, with adoption rates accelerating fastest in predictive maintenance, quality inspection, and supply chain optimisation.
Using sensor data to predict when a machine will fail, enabling proactive repairs and reducing downtime.
Computer vision systems detect defects on production lines with far higher accuracy and speed than manual inspection.
Reinforcement learning algorithms adjust production parameters in real-time to maximise throughput and minimise waste.
AI models crunch vast datasets to predict inventory needs, supplier risks, and delivery delays.
AI-driven CAD tools create hundreds of optimised product designs based on performance parameters and material constraints.
Several converging factors are making AI an urgent priority rather than a distant aspiration:
Manufacturers now generate more data than ever before—from IoT sensors, ERP systems, and machine logs. AI thrives on this data-rich environment.
Cloud computing and edge devices have drastically reduced the cost and complexity of deploying AI models, making it more accessible to mid-sized manufacturers.
With experienced operators retiring and younger workers in short supply, AI can act as an intelligent assistant—augmenting human capabilities, guiding workflows, and preserving institutional knowledge.
In a world of supply chain shocks, energy price volatility, and competitive uncertainty, AI offers greater agility and decision-making precision.
AI enables real-time energy monitoring, predictive resource usage, and circular economy innovations, aligning industrial performance with ESG commitments.
AI adoption in manufacturing represents a deep organisational shift. Success requires far more than choosing the right software. It demands a rethinking of processes, culture, and collaboration across the business. Companies that treat AI as a strategic enabler rather than a standalone tool are more likely to extract lasting value. This transformation typically unfolds through a sequence of interdependent phases, each critical to building momentum and trust in AI’s potential.
The first step is identifying high-impact use cases – specific operational pain points where AI can deliver meaningful, measurable improvement. Rather than chasing abstract innovation, manufacturers should focus their attention on problems that consistently affect performance: unplanned downtime, excessive scrap rates, or supply chain bottlenecks. These are the pressure points where AI has already proven its value through predictive maintenance, intelligent quality control, or advanced forecasting. Focusing on these areas creates a foundation for ROI and stakeholder buy-in early in the journey.
Once a use case is selected, the next priority is data readiness. AI systems rely entirely on data to learn, predict, and optimise. Yet in many industrial environments, that data is fragmented, outdated, or inaccessible. Companies must invest in the digital infrastructure that makes AI possible, from IoT-enabled machines and real-time sensors to Manufacturing Execution Systems (MES) that connect the shop floor to the cloud. Clean, structured, and timely data is the lifeblood of effective AI.
With data in place, the focus turns to platform selection. It’s essential to choose AI tools that can integrate with existing systems, support open standards, and scale across multiple production lines or facilities. Scalability ensures that early wins can be replicated without starting from scratch at each location. Modern AI platforms also allow for hybrid deployments (Some running in the cloud, others at the edge) supporting real-time performance whilst keeping control over sensitive data.
Critically, AI programmes must begin with focused, manageable pilots. Starting small allows organisations to test assumptions, iron out technical wrinkles, and evaluate outcomes before committing to full-scale transformation. These early pilots, when successful, become internal case studies and proof points that can rally wider support. Once a pilot has demonstrated value, a structured roadmap can guide its expansion across the business, with lessons learned feeding into more sophisticated, enterprise-wide deployment strategies.
Finally, none of this works without the right people. AI success is not solely the domain of data scientists or IT departments. It requires cross-functional collaboration. That means involving engineers, operators, process owners, and digital specialists in equal measure. Domain experts bring the contextual knowledge that gives AI models relevance. Frontline workers offer practical insight into real-world constraints. By uniting these voices, organisations ensure that AI systems reflect operational reality, and that the workforce feels invested, not sidelined, by the changes to come.
Siemens uses AI to power its Insights Hub platform, which enables predictive maintenance and energy optimisation across its global plants. According to the company, this has resulted in double-digit percentage savings in downtime-related costs.
BMW employs AI-based image recognition systems on its production lines. These systems identify deviations in real-time, flagging defects and reducing the need for human rechecks. The company reports significantly improved product consistency.
UK-based Ocado has developed a cutting-edge AI-based fulfilment system, using computer vision and deep reinforcement learning to control thousands of robots in real-time, optimising space, speed, and picking accuracy.
The growing integration of AI into industrial operations is not only transforming factories, it is also triggering a domino effect of infrastructure and supply chain demands across multiple sectors.
AI’s expansion is not a linear change—it’s exponential. Every layer of technology adoption creates cascading requirements across interconnected industries, reshaping the industrial economy as a whole.
A simple demand flow is illustrated below.
AI workloads require immense computational capacity. The rise of generative AI, natural language processing, and computer vision has caused a surge in demand for AI-optimised data centres. Hyperscalers like Microsoft, Google, and Amazon are investing billions in building new facilities, with McKinsey predicting that demand for data centre capacity will double by 2030.
Running AI models efficiently requires high-performance chips—particularly GPUs, TPUs, and custom silicon. This is creating unprecedented demand for semiconductor manufacturing, with a particular focus on advanced fabrication (5nm and below). It is also fueling investment in European and UK chip sovereignty strategies.
AI-ready data centres and high-speed processors generate significant heat. This has created a parallel surge in demand for advanced cooling systems—liquid cooling, immersion cooling, and heat recovery systems are rapidly becoming standard.
AI adoption is increasing energy consumption, both within factories and through supporting infrastructure. Power-hungry data centres, autonomous systems, and robotics require stable, high-capacity energy supplies. As a result, the energy sector is seeing pressure for more resilient, flexible, and sustainable power generation—including renewables, microgrids, and nuclear options.
Cooling systems, chip fabs, and battery production all require significant water input. AI’s rise is therefore impacting water infrastructure, requiring new investments in wastewater treatment, closed-loop systems, and water reuse technologies.
From smart warehouses to AI-integrated logistics hubs, the demand for physical infrastructure is also evolving. Modular, sensor-enabled spaces with high connectivity are becoming essential in manufacturing and distribution.
Whilst the promise of AI is huge, adoption isn’t without challenges:
Operators and engineers may be sceptical of AI decisions. Transparent, explainable AI is critical to building user trust.
Integrating AI with ageing machines or siloed IT systems can require significant adaptation.
Many organisations lack a single source of truth, making cross-functional AI deployment difficult.
AI introduces new cybersecurity vulnerabilities and raises concerns about algorithmic bias, requiring strong governance frameworks.
As adoption matures, AI will move from isolated use cases to a fully orchestrated system – the autonomous, self-optimising factory.
Here’s what the future holds:
AI enables mass customisation through real-time reconfiguration of lines based on customer demand.
Augmented reality and AI copilots will guide workers, reducing cognitive load and boosting training.
AI-driven networks will dynamically reroute shipments, adjust orders, and simulate disruptions.
AI will model carbon impact, suggest eco-friendly materials, and optimise energy usage in real time.
AI is no longer the exclusive preserve of Big Tech or advanced research labs. It is becoming the operating system of modern manufacturing. The question is not whether to adopt AI, but how quickly and strategically it can be deployed.
For machine builders and OEMs, the opportunity is clear: AI can reduce waste, accelerate innovation, improve quality, and future-proof operations. By partnering with manufacturing experts and embracing AI holistically, from shop floor to C-suite, industrial leaders can turn disruption into competitive advantage.
Those who fail to act may soon find themselves not just lagging but locked out of the next industrial revolution altogether.
The Shock of the New campaign comprises of several stories published since July 2023. Part I of the campaign included 5 stories and a downloadable e-book. Part II will consist of five more stories and another e-book collection, releasing in 2025. More stories can be found in the carousel below.
The entire collection can be found here.
“The shock of the new” highlights the dynamic relationship between innovation and the human response to change. It acknowledges that whilst change can be challenging, it can also lead to profound advancements and opportunities for societal and sustainable development.
The fourth industrial revolution has a new engine and manufacturing is on the cusp of its most transformative era since the invention of the assembly line.
Recent history has been defined by turbulence and supply chains have become the front line of disruption. These shocks have acted as both a warning and a wake-up call.
At the heart of the green transformation lies a somewhat critical element: skills. Shock of the New reacts to the 2024 Global Green Skills Report.
The Shock of the New campaign returns, as Tony Hague states that if the UK continues to outsource its green technology production overseas, it risks squandering a once-in-a-generation opportunity.
Over 70 pages, collecting every story from the Shock of the New campaign. Includes bonus content and available for instant free download.
What if by understanding what works across other regions, successful approaches can be combined to build a collaboration blueprint for UK manufacturers to call their own.
In an era where environmental concerns are at the forefront of global discourse, the UK manufacturing sector finds itself at a crossroads. To flourish, there is a compelling argument that it must undergo a profound transformation towards sustainability.
The Greentech landscape and opportunity for innovation and investment is driving the sustainability agenda and encompasses a wide range of new technologies.
There are various types of machines and devices that contribute to the circular economy by enabling resource conservation, waste reduction, recycling, and the reuse of materials. The demand increase in these varieties has been more progressive in recent years.
When adopting new technologies and exploring emerging industries, there is often a disruptive element involved. They can challenge established norms, disrupt traditional industries, and require individuals and organisations to adapt to new ways of doing things.
Ian Knight, Chief Information Officer at PP Control & Automation (PP C&A) challenges the prevailing, often vague narrative around AI adoption and reframes the conversation around a more practical starting point: operational constraints.
Artificial intelligence is no longer a distant concept for manufacturers. It is already being explored across quotation, production planning, engineering, quality, supply chain and customer service functions. Yet, for many organisations, the gap between experimentation and meaningful operational impact remains difficult to close.
A leading aquaculture specialist – that is changing the way fish are vaccinated safely – has signed a major manufacturing deal.
On paper, defined discipline-specific suppliers can look organised. However, every additional supplier introduces another handoff, and every handoff creates another point where time, quality, communication and accountability can be lost.
Very rarely does growth not surface because an OEM lacks ambition. Shortcomings arise because operating models built to support such ambition don’t evolve quickly enough.
Recent weeks have brought two important industry moments into sharp focus, concluding that demand for AI and automation is rising, but investment, skills, and long-term thinking must follow.
For decades, one question has sat at the heart of operational strategy for machine builders and OEMs: make or buy? It’s a familiar debate and it isn’t the wrong question by any means, but perhaps it is an incomplete one.
In most machine building businesses, change is still treated as an exception. A late-stage drawing revision, component substitution, or wiring tweak discovered during build. Each one is handled, resolved, and signed off. And then everyone moves on. But what if that’s the wrong way to think about it? What if change isn’t the disruption to the system but the system itself?
