Not long ago, conversations about manufacturing were mostly about building new plants, buying equipment, and expanding production capacity. Today, the focus is shifting. According to the KPMG Global Tech Report 2026: Industrial Manufacturing, the sector is entering a new phase where competitiveness is increasingly determined not by the number of machines on the factory floor or even the size of investment budgets, but by how effectively companies collect data, analyze it, and turn it into decisions. For many industrial businesses, artificial intelligence is no longer an experimental tool sitting inside an innovation team. It is becoming part of everyday operations. Nearly half of executives surveyed report meaningful financial returns from digital investments, while 87% believe advanced technologies will be a key source of competitive advantage in the years ahead.
At first glance, this may look like another wave of digital transformation. The findings suggest something much bigger. For decades, industrial competition was built around capital, equipment, raw materials, and market access. A new factor is now joining that list. It cannot be stored in a warehouse or measured in tons. It is a company’s ability to turn information into action. In the past, production speed depended largely on the speed of machines. Increasingly, it depends on the speed at which data can be processed and converted into decisions.
That shift helps explain the growing interest in artificial intelligence. Nearly half of industrial companies are already deploying AI solutions that create measurable business value. The strongest demand comes from areas where the cost of mistakes is high. Predictive quality control ranks as the leading use case. More than half of executives identify it as a top priority. It is followed by reducing equipment downtime, improving production analytics, and using generative AI to tailor products to customer needs. Compared with many other industries, manufacturing is moving quickly on AI while remaining cautious. A chatbot error may frustrate a customer. A mistake on a production line can stop operations, damage product quality, and create major financial losses.
That is why some of the most important changes are happening around AI rather than inside the technology itself. The report shows that manufacturers are building a new digital environment where AI is just one part of a much larger system. Sensors, analytics platforms, equipment monitoring tools, digital twins, cloud services, and forecasting technologies are becoming increasingly connected. AI acts as the intelligence layer that links these different streams of information and helps managers make decisions faster. As a result, manufacturing is moving beyond the automation of individual tasks and toward the creation of intelligent production environments.
Digital twins offer one of the clearest examples of this trend. Not long ago, they were seen as a niche technology available only to the largest corporations. Today, interest is growing rapidly. Companies are using virtual models of factories and production assets to test changes before implementing them in the real world. Combined with AI, these models can predict the consequences of decisions, identify bottlenecks, and improve equipment performance. Interest is also rising in edge computing, where data processing takes place close to the production line rather than in distant cloud servers. This approach speeds up analysis, reduces infrastructure pressure, and allows businesses to respond more quickly to changing conditions.
Yet one theme runs through every part of the report: data. This is where both the opportunities and the risks of industrial transformation meet. More than 80% of executives say their organizations have built strong foundations for managing data. A similar share rate their data management capabilities positively. At the same time, 76% identify unreliable data as one of the biggest risks facing AI adoption. On the surface, that looks like a contradiction. In reality, it reflects how companies see the challenge ahead. Having data is not the same as being able to use it effectively.
A modern industrial company resembles a complex ecosystem. Production facilities, logistics operations, suppliers, ERP systems, and finance departments often operate in separate information environments. Data exists in abundance, but it does not always move smoothly across the organization. That is why the report places significant emphasis on new approaches that connect information into a unified knowledge structure. If a digital twin reflects the physical side of a business, emerging data management models are beginning to create a digital representation of the company’s entire information landscape. For many organizations, this challenge is just as important as implementing AI itself.
As data becomes more valuable, corporate management structures are changing as well. Seven out of ten companies use a centralized approach to AI implementation, with IT departments taking the lead. At the same time, there is growing recognition that technology projects can no longer be treated as the responsibility of engineers and software developers alone. Nearly nine out of ten executives see close collaboration between IT teams, risk specialists, and cybersecurity professionals as essential. The deeper technology becomes embedded in operations, the greater the cost of mistakes.
This is one reason cybersecurity spending is rising alongside investment in AI. Nearly half of industrial companies plan to significantly increase cybersecurity budgets over the next year. For manufacturers, the issue carries particular weight. A factory cannot afford prolonged downtime. While a cyberattack may create temporary disruption in some industries, it can halt production, delay deliveries, and generate direct financial losses in manufacturing. It is no surprise that executives rank stronger cybersecurity among the most important benefits of technology investment, alongside improved operational efficiency.
The sector’s view of the external environment is changing as well. For years, supply chains were built around efficiency and cost optimization. Today, resilience has become a priority. Companies increasingly use digital tools to monitor risks, assess supplier dependencies, and model different scenarios. Improving data flows emerged as the most common response to economic uncertainty in the survey. In effect, data is becoming the nervous system of the industrial enterprise. It allows leaders to understand what is happening across the business in near real time rather than after the fact.
Attitudes toward the workforce are changing too. Nearly 90% of executives believe managing AI agents will become an important professional skill within the next five years. Companies are investing in training, creating new roles, and rethinking organizational structures. The report does not support the popular idea that machines will simply replace people. Instead, manufacturing appears to be moving toward a model where employees and intelligent systems work side by side. That is why workforce development is becoming just as important as technology deployment.
Ultimately, the report describes more than another stage of digital transformation. It captures the early signs of a broader shift. Industrial companies will continue building factories, purchasing equipment, and upgrading infrastructure. But the source of competitive advantage is gradually moving elsewhere. Increasingly, success belongs not to the company with the most advanced machinery, but to the company that can turn information into decisions faster than everyone else. That is the essence of industrial intelligence. It is becoming a new layer of industrial management, where data, analytics, and artificial intelligence are beginning to play the role that production capacity and capital once played.
Shyngys Yerbolat, expert at EconomyKZ.org
