Why AI success starts before AI
New applications continue to emerge across planning, maintenance, quality control, and pre-production overall. On the shop floor, however, the questions tend to be more practical. Can AI reduce downtime? Catch problems before they escalate? Help operators make better decisions?
Readiness may ultimately determine success here.
The manufacturers getting the most from AI are often not the fastest adopters, but those with the data, processes, skills, and organizational foundations to use it effectively. So, how ready is the industry to make the best use of AI? And how do we measure this readiness?
Not everyone is measuring the same thing
Technology providers and investors tend to envision AI adoption as a fast track to market growth, while business leaders might look for productivity gains, cost reductions, or faster decision-making. Manufacturers, however, operate in a slightly different reality. Success often comes down to the millimetre and safety is of utmost importance. Fewer disruptions across the value chain translate not only in costs, but also in injury prevention and saved human labour that can go to more important tasks.
That distinction is key because AI is a great tool to have on your side, but what constitutes its successful implementation remains unclear; what creates value in one operation may have little relevance in another. A predictive maintenance solution that delivers measurable results in one facility may offer little benefit in a factory facing entirely different constraints.
As AI becomes more deeply embedded across industry, manufacturers may find increasing value in establishing their own principles for adoption. Where does AI create value? How should success be measured? And what role should it play within the wider operation?
The loudest voices are not always the closest to the problem
Every new technology must fit into existing operations, integrate with established systems, support production goals, and deliver measurable results. Reliability, traceability, and consistency often matter more than novelty.
This is one reason why AI adoption in manufacturing looks different — and maybe less glamorous — than it does for other industries. Manufacturers are evaluating what innovations the technology can spearhead in the future, but ultimately what they need to know is: can it solve the problems they already have?
Value depends on more than the technology itself
According to the research we did for our manufacturing wellness report, many manufacturers are still building the foundations that allow new technologies to create value. While AI continues to attract attention, readiness remains uneven.
Only 30% of manufacturers report having a seamless flow of manufacturing and process data across their organization, while 22% say they have no access to manufacturing-related data at all. A further 29% struggle to interpret the data available to them. At the same time, 26% do not formally review their use of technology, making it harder to evaluate whether new investments are delivering the results they were intended to achieve.
The picture is similar when it comes to automation. While many manufacturers have made progress on the shop floor, planning, design, and logistics often remain far less connected and automated. AI-driven decision-making, meanwhile, remains at an earlier stage of adoption than many other digital technologies.
The foundations still matter
The same pattern is visible beyond manufacturing. Kyndryl's 2025 People Readiness Report found that while AI has become widely available across organizations, many businesses continue to struggle to translate investment into measurable value. Two-thirds of CEOs surveyed identified skills shortages as a major barrier, while nearly half reported that employees remain uneasy about the technology.
The organizations making the strongest progress were distinguished less by the speed of adoption and more by the way they invested in leadership, training, and trust.
Those findings resonate in manufacturing environments, where technology performs best when the surrounding systems and people are ready to support it. A connected machine delivers greater value when the data behind it is reliable. Digital tools become more useful when information can move across departments. Automation becomes easier to scale when employees understand how to use it, challenge it, and improve it.
Different manufacturers start in different places
One of the principles behind manufacturing wellness is that lasting improvement rarely comes from focusing on a single challenge in isolation. Manufacturers may prioritize different areas depending on their circumstances, whether that's developing their people, improving sustainability, unlocking the potential of their data, or embracing new technology.
In the case of AI, the relationship between technology and readiness is difficult to ignore. New tools tend to deliver greater value when the surrounding conditions support them, from reliable data and connected systems to clear processes and workforce confidence.
The manufacturers making the greatest strides build systems capable of evaluating new opportunities, integrating them effectively, and creating value from them long after the initial excitement has passed.
That challenge extends well beyond AI. Nearly 1,000 manufacturers have completed the manufacturing wellness assessment to date, with an average score of 49 out of 100. The result highlights an industry with significant opportunities still ahead of it, not in any single area, but across the wider habits that support long-term resilience and performance.
Our manufacturing wellness report explores how manufacturers around the world are approaching technology, automation, sustainability, skills development, data, and the other habits shaping the future of the industry.
Download the full report to see how nearly 1,000 manufacturers scored across the eight habits of manufacturing wellness and discover where the greatest opportunities for improvement remain.
