Traditional Approach to Change in Industry

Industrial enterprises typically adopt technological innovations at a slow pace. Major investments in new technologies go through lengthy stages of evaluation, testing, budgeting, implementation, and integration before being scaled up. Equipment can remain in use for decades, and deploying enterprise resource planning (ERP) systems can take years. Employees require training, and production processes must remain stable. Safety, quality, cybersecurity, and reliability are top priorities that cannot be compromised. This environment ensures predictability and control over investments.

The Impact of Artificial Intelligence

The emergence of artificial intelligence (AI) has accelerated the pace of technological advancement. AI models are improving rapidly, new capabilities are introduced every week, and the cost of adoption is decreasing. Software companies regularly add AI features, new competitors enter the market, and established players may exit. By the time a company finishes evaluating an AI solution, the market landscape may have changed significantly.

The Rhythm Mismatch

Industry values stability, reliability, capital discipline, and risk management. In contrast, AI development thrives on experimentation, iteration, and rapid innovation cycles. Fully adapting production processes to the speed of AI evolution may not be practical, but ignoring the pace of change is not an option. This creates a gap between the rapid development of AI and organizations’ ability to implement and utilize it.

The Need for Adaptation

To successfully implement AI, industrial companies must rethink their approach to change management. Instead of traditional project models where technology remains unchanged throughout the cycle, organizations need to continuously identify new technologies, experiment, assess potential, scale successful solutions, and quickly abandon ineffective ones. Adaptation becomes a continuous, rather than linear, process.

A Two-Speed Implementation Model

Not all parts of an industrial organization need to change at the same pace. Critical systems—such as production controllers, safety systems, ERP platforms, and infrastructure—require stability and strict management. However, AI applications like engineering assistants, analytics tools, or internal productivity solutions can be tested more rapidly. To enable this, it is recommended to create separate, controlled zones for fast AI experimentation while maintaining the security of the core production environment.

Value-Driven Implementation

AI adoption should be guided by economic value, not just technological capability. Instead of asking, “Where can we use AI?” organizations should determine where AI can make a significant impact on economically important decisions or processes, such as reducing downtime, minimizing defects, increasing productivity, optimizing energy consumption, and other key performance indicators.

Flexible Architecture and Resilience

Traditionally, industrial technology choices have focused on longevity. However, with the rapid evolution of AI, models, vendors, and standards can change quickly. It is advisable to design architectures that allow for component replacement, ensure data portability, favor modular solutions, and avoid excessive dependence on specific models or suppliers.

New Metrics for Success

Rather than measuring project milestones and completion, the speed of organizational learning becomes crucial: how quickly a company can identify a new opportunity, test it, assess its value, and decide whether to scale or abandon it. Rapid response to unsuccessful experiments becomes a competitive advantage.

Conclusion

Industrial companies cannot and should not adopt innovations at the same speed as AI evolves. Constraints related to physical assets, safety requirements, regulations, and operational complexity demand a tailored approach. The main challenge is to learn how to quickly and safely adopt important changes without disrupting existing processes. Competitive advantage will go to organizations that can adapt faster, identify priority areas for implementation, and efficiently discontinue ineffective solutions.