Trends 2026: Technology pivots from pilot to programme as manufacturing adapts to new reality

Simon Francis, group quality director at G&P, sees 2026 as a turning point for the manufacturing sectors. In this article, Simon looks at the trends set to dominate the new year and shape the future of industry.  
 
It could be argued that the past year was somewhat experimental in nature, with much anticipation about the potential use of artificial intelligence (AI) and ‘smart’ manufacturing. While the final technology is not yet in place, in 2025 many sought to break new ground and drive the industry forward. The potential for change over the coming years is significant, and it comes at a time when broader geopolitical uncertainty is at a recent high. With that in mind, 2026 is shaping up to be a critical turning point for the manufacturing sector, where those embracing AI and smart technologies establish these systems and leverage their advantage. 

 
AI: buzzword or beyond?  
 
There is a strong push towards digitalisation spurred by the rise of AI. In fact, around 80% of manufacturing executives plan to allocate at least 20% of their improvement budgets to smart manufacturing initiatives.1 It’s anticipated that smart manufacturing will transform how products are made, improve competitiveness, and increase agility for just-in-time production. This is supported by the view from 82% of manufacturing executives that AI will be a key growth driver for their businesses.
 
With its ability to independently set goals, plan, reason, and act with minimal human input or intervention, ‘agentic AI’ could completely alter the manufacturing and engineering sectors. This could then translate into automation with increased autonomy, known as ‘physical AI’. However, the majority of manufacturing AI currently in use is in vision systems and machine learning, particularly in process manufacturing, discrete manufacturing, engineering, and maintenance.  
 
A major beneficiary of advancing AI and machine learning is digital twin technology. With increasing amounts of data available, cloud storage, and the processing capabilities of AI, digital twins have become a true necessity.  
 

Reshoring and manufacturing agility 
 
The global trade environment and economic uncertainty are seeing many manufacturers seek greater agility and sovereignty. Agility is identified by many C-suite executives as a primary business challenge, with established or legacy operational models and systems generally unfit for modern manufacturing businesses. According to HSO, self-reported agility among manufacturers is at a five-year low, and 60% are looking to improve their agility in the next 12 to 24 months.  
 
This need for greater control and localisation also means more manufacturers are reshoring and nearshoring. It’s clear that manufacturers are seeking to regain control of variables and minimise the impact of external, uncontrollable factors. Given the political and economic climate, this will certainly continue throughout 2026. 
 

Quality and AI-enhanced inspection 
 
Cost competitiveness and the drive for greater efficiency have seen the need for quality management rise to unprecedented levels. With extended supply chains and complex data, more than a third of manufacturers are said to be unable to trace the root cause of quality issues. At the same time, 50% of manufacturers believe quality and reliability are significant challenges, and 40% believe it takes too long to improve product quality.
 
One route that G&P is taking to address clients’ quality concerns is through AI and automation. Using state-of-the-art AI-supported vision inspection system technology, we have been able to increase inspection speed, accuracy, and reliability throughout the quality management process. We’re already applying AI across manufacturing industries and have found that it can significantly reduce the risk of error associated with visual inspections. 
 

Is the AI bubble at breaking point? 
 
As the manufacturing sector launches into 2026, we’re expecting a shift from cautious investigation and experimentation to proactive integration of new technologies like AI. However, much of today’s AI valuation is driven more by speculative promise and inflated expectations than proven, scalable value. As organisations make the expected move from experimentation to integration,  many will discover that the costs, data quality issues, skills gaps, and governance risks outweigh the benefits. 
 
Perhaps as soon as within the next twelve months, we’re expecting to see a sharp divide between value-creating agents and those that ultimately fail to deliver. This would trigger a contraction in development, consolidation among AI suppliers, and a more streamlined reassessment of where AI truly delivers advantage. The result will not be the end of AI, but the bursting of its bubble, clearing the ground for more disciplined, outcome-driven adoption. 
 
However, while there are undoubtedly challenges posed by AI and finding the resources needed to successfully adopt them, the benefits and competitive advantages to be gained by separating and sourcing the wheat from the chaff are extremely attractive. 

 
Looking ahead… 
 
Convergent trends, external forces, and market competition will continue to drive innovation, while instability in the economic outlook and global trade will see increased reshoring at a global level, particularly in the West. This presents a new set of challenges as local supply chains become burdened and the skills gap widens.  
 
Throughout, end-user expectations remain the same, meaning that manufacturers must maintain or improve quality management throughout the entire manufacturing supply chain to keep their competitive edge.