Digital transformation in manufacturing requires leaders to advance frontline workers’ skills as they implement new technology.

Digital transformation is more about people than technology.
Digital transformation is more about people than technology.

Digital transformation in manufacturing is well underway, though most companies in the industry still have work to do. While AI-powered technologies enable incredible advancements in machine health monitoring, manufacturing maintenance operations, and other activities, successful digital initiatives will ultimately require another kind of change: workforce transformation.

That’s not to say technology doesn’t matter. The integration of AI and advanced analytics will undoubtedly shape the industry for decades to come. In the same way electricity utterly transformed physical, mechanical processes, AI transforms organizational intelligence and thinking processes.

But digital transformation in manufacturing is primarily about people.

Unlike electricity, which allowed the motor to replace human or animal exertion completely, AI is not removing humans from these processes. In fact, it’s making them more valuable than ever before.

Manufacturers are missing the point

The overarching objective of digital transformation in manufacturing is to enhance workers’ ability to make informed and intelligent choices. Yet, that goal can’t be achieved within the outdated organizational structure that still defines most manufacturing companies today.

Traditional production environments that are still in use today were designed to divide frontline workers and managers. Companies task managers with making high-value decisions, while frontline employees are only responsible for execution. The model has worked well enough in the past, but with the integration of advanced technologies, it’s becoming a barrier to unlocking the full potential of digital transformation in manufacturing.

By continuing to view frontline workers as a labor expense and restricting their decision-making abilities and authority, companies severely limit the value they get from digitization. To be clear, this isn’t a moral or ethical failure on the part of manufacturing leaders. Instead, it’s a glaring strategic oversight. Workforce transformation is the solution.

Breaking down barriers

At its core, workforce transformation is about shifting frontline workers away from functional, labor-intensive tasks to knowledge work. It requires companies to equip employees with the skills, resources, and tools they need to make informed decisions with confidence and give them the ability to act on those decisions.

In many cases, frontline workers already possess immense knowledge of the machines that power manufacturing operations. By enabling them with the same tools and capabilities that management teams can access, manufacturers aren’t just boosting speed and agility by flattening the decision-making structure — they’re also making their organizations smarter. And that’s why AI in manufacturing can be so transformative: It facilitates the transfer of knowledge and data from the brains of those with the most experience into a pool of collective intelligence that everyone can access and use.

This phenomenon can fundamentally improve nearly every aspect of a manufacturing operation and yield cumulative benefits over time. So, practically speaking, how might industry leaders start the process of workforce transformation? Here are a few tips:

  1. Prioritize behavioral change.

You don’t need to turn your technicians into data scientists, but you will need to ensure your frontline workers can draw insights from data and incorporate those insights into strategic decision-making.

Aside from training workers to absorb information in whatever format your tools present it in, you’ll need to get them used to incorporating data into decision-making processes that were previously driven by experience-based insights. Do this gradually by making iterative adjustments to existing workflows (whether they involve machine health monitoring, maintenance, or something else), so they increasingly incorporate new data inputs. Ultimately, you want workers to consult data before making virtually any decision, and that mindset takes time to develop.

  1. Foster a culture of collaboration.

Improved information sharing might be the most impactful outcome of AI in manufacturing, but technology is merely a channel connecting a network of intelligence. Your employees are the source of that intelligence, and they must be proactive about sharing their knowledge. By reducing their time on reactive labor-intensive tasks, you can create more time and space for focused collaboration. Be deliberate about breaking down silos and encouraging cross-functional communication.

  1. Focus on precision.

Data gives you the ability to be more predictive and more precise. Emphasize the skill sets that enhance precision during training. These could be related to precision maintenance, precision root cause analysis, or precision in operational processes. Train teams one at a time and then allow employees to become evangelists and teachers themselves.

Frontline employees will need adequate time and resources to develop advanced skill sets, but the training is an investment that will eventually give manufacturing companies a significant competitive advantage.

artem kroupenev augury
Artem Kroupenev

Artem Kroupenev is VP of Strategy at Augury, where he oversees product, market, innovation, and ecosystem strategy. He has over a decade of experience driving the adoption of disruptive technologies and has previously co-founded companies in the United States, Israel, and West Africa.