Smart factories are here — but is your team ready to use them?

Since the emergence of Industry 4.0 in 2011, manufacturing has undergone a digital transformation. Industrial Internet of Things (IIoT) sensors now allow machines and assets to communicate seamlessly, while artificial intelligence has become a core business enabler. Cloud computing provides virtually limitless processing power and storage, and big data analytics has become essential for strategic decision-making. By integrating data from ERP systems with real-time machine data — via SCADA, PLCs, and other automated tools — manufacturing execution systems (MES) have paved the way for the modern smart factory.

Smart factories are not limited to MES alone but also cover other areas like energy management systems (EMS), video analytics-based plant safety, digital quality inspection using vision-based cameras, immersive technology-based shopfloor training, operational technology (OT) network, firewalls and other related tools.

If we go up the value chain, today factories are designed using digital twins with full process simulations and products are designed using product lifecycle management (PLM) platforms. Maturity of smart factories is an evolution, tightly linked with the digital transformation plan of the enterprise. Still 49% of the enterprises lack confidence in their future manufacturing strategy.

While visiting various plants, the disparity in digital maturity is often striking. In many business units, specific digital initiatives take precedence because they are driven by the immediate priorities or critical requirements of the end customer. In other instances, regulatory compliance dictates the roadmap. Ultimately, delaying a plant’s digital transformation can be a strategic choice; these are complex business decisions managed by CXOs based on broader organizational goals.

Having said this, based on Gartner’s Top 10 Strategic Technology Trends for 2026, digital and AI technologies will continue to be the fundamental for driving smart factories maturity. And according to IDC’s 2026 Manufacturing FutureScape, by 2027, 40% of factories’ operational data will be integrated across applications and platforms autonomously, due to increased standardization and the use of AI agents purpose-built for specific data.

In fact, I envision there will be AI agentic mesh in the smart factories, working under an AI orchestrator layer, either collecting or sharing data in a multi-agent AI environment, with human-in-the-loop (HITL) for critical business decisions.

Impact on the workforce skillset

In terms of coping with the impact of digital transformation, the world of the workforce on the shopfloor of factories is changing at a faster pace. The tasks and activities done by operators, supervisors, maintenance technicians, quality inspectors, material handlers and others need to be seen through digital, AI and smart factory lenses.

There is a growing realization within the workforce that the convergence of automation, AI, cloud/edge computing, and IIoT is fundamentally reshaping every manufacturing process. AI-driven shopfloor assistants have become increasingly common, guiding workers through machine maintenance, process automation, and quality checks. These digital tools are particularly vital during night shifts or off-hours, when fewer human experts are available on-site to provide support.

Over the last few years, I have observed manual quality inspections being steadily replaced or augmented by advanced vision systems. In fact, many modern machines now come with these cameras factory-installed. From robots performing thousands of precise welds on vehicle seating to the automated painting and injection molding of automotive parts, the shift is undeniable. Consequently, the workforce skillset required to drive digital transformation in these smart factories needs a comprehensive revisit. The sentiment of reskilling is well captured in the book “What Got You Here, Won’t Get You There,” though it’s more pertinent to managers or senior leaders.

Bridging the skillset gap

Through AI innovation, by 2031, over 30 million jobs per year will be redesigned – not eliminated. So, learning and development (L&D) leaders need to look at the talent development and retention strategies, which will stay relevant in the smart factories’ era and beyond.

Successful initiatives often involve learning and development (L&D) leaders collaborating with business unit heads and digital stakeholders to build a comprehensive transformation matrix. This matrix maps out the manufacturing processes most affected by AI and digital tools, identifies the relevant job roles, and aligns them with the necessary technologies—such as IIoT, cloud computing, Gen AI, agentic AI and computer vision.

From this matrix, the skillset gaps for the impacted roles because of process and technology changes are tracked and fed into the L&D talent development plan. This plan is developed at the BU/plant-level and the requisite investments on training and infrastructure are approved by the business head in conjunction with the digital head.

From my perspective, I feel immersive technology-based training is quite effective in smart factories. Virtual reality (VR)/augmented reality (AR) solutions have helped to cut down the training time by 20-50%, with full tracking of the talent proficiency. This information is fed into learning management system (LMS).

One of the most effective features is that the workforce skillset matrix is generated directly from the learning management system (LMS). This integration enables plant managers to assign operators to specific machinery based on their verified proficiency and skill levels. This automated allocation of production line personnel is becoming increasingly standard, effectively eliminating the risk of unqualified staff operating sensitive equipment. By ensuring the right person is at the right machine, organizations can significantly improve safety, ‘first-time-right’ rates and overall product quality.

Keeping the workforce AI-ready

The digitalization of manufacturing generates vast quantities of data. While IT and digital teams are responsible for ensuring this data is captured securely on scalable platforms like the cloud, it is equally vital that the shopfloor workforce understands the underlying dataflow. When operators grasp how information moves through the system, they can better support the integrity and efficiency of the smart factory.

Furthermore, the workforce must recognize that data quality is the foundation of any effective AI solution — whether it involves shopfloor assistants or predictive forecasting. Because AI models are trained on specific datasets for specific use cases, their output is only as reliable as the input. Enterprises must strategically determine whether these models should be trained exclusively on internal enterprise data or supplemented with broader industry and internet-based information.

The bottom line is that AI-based solutions help organizations to stay ahead of the curve in terms of differentiation, competitive edge, business decisioning, growth and so on. The upskilling and cross-skilling of the workforce, as per the talent development plan, should be updated and tracked from AI lens, especially as this technology is changing at a rapid speed.

The best practice I have seen being followed in the industry is when the digital/AI team works with the HR and BU teams to identify training for different sets of employee groups. Shop floor training on digital and AI, for instance, will be a lot more hands-on and manufacturing-focused compared to training for mid/senior level executives, where the focus will be about the technology, its impact on the business and how to stay abreast of it.

Industry-specific certifications in digital and AI technologies can significantly enhance workforce productivity and efficiency. To complement formal training, many organizations now partner with startup ecosystems on relevant business projects, giving employees first-hand experience with emerging tools. Furthermore, ‘AI playgrounds’ allow business units to democratize these technologies by applying them to live use cases. Ultimately, bridging the skills gap requires more than just academic instruction; practical, hands-on exercises are essential to ensuring an AI-ready workforce.

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