After many digital transformation projects consisting of both successful adoptions and efforts stuck in pilot purgatory, manufacturers in many industries are recognizing that success requires more than adopting new technologies. It should be no surprise that the technical parts of digital transformation are far less important than its business and human aspects. In fact, the most important technological consideration is how well it supports people to adopt new practices and behaviors in pursuit of efficient workflows and increased business value.
Despite this understanding, many business leaders still initiate digital transformation programs without clearly outlining and communicating the rationale, business impacts or nature of changes and the steps for achieving them. They also commonly overlook the upskilling required to empower their workforce to use these new technologies, risking not only a subpar return on investment, but also contributing to today’s widening skills gaps.
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The most forward-thinking organizations invest in innovative technologies, such as advanced analytics and generative artificial intelligence (genAI), which improve workflow efficiency, use of operations data and manufacturing insights, while also providing industry-relevant, just-in-time learning on demand. By pairing digital transformation with user empowerment, these companies create insights, and decrease the learning curve to understand them, yielding better business results, while easing the tasks of personnel who perform them.
Today’s workforce challenges
The shortage of skilled labor across the industrial sector presents a fundamental challenge for organizations implementing long-term digital transformation. While the most knowledgeable employees are retiring, industry is simultaneously losing talent to more popular fields that address evolving employee expectations more effectively.
According to the World Economic Forum's "Future of Jobs Report 2023," 60% of workers will require training before 2027, but only half have access to adequate training opportunities. The same report found that, in all industries collectively, training workers to use AI and big data ranks third among company upskilling priorities over the next five years.
Cross-industry surveys find large salaries are no longer enough to retain employees. Workers recognize that longevity in a sector and their future employability depend on access to and successful adoption of new technologies. According to Deloitte, manufacturing employees are nearly three times less likely to leave an organization in the next year if they believe they can acquire skills that are important for the future. Similarly, Deloitte predicts that nearly 2 million manufacturing jobs could remain unfilled if organizations don’t address these skills gaps.
Providing the right technology
Successful digital transformation requires personnel to use new tools effectively to improve workflows. Companies that invest in modern technologies and upskilling can expect measurable improvements in productivity and efficiency, while also attracting and retaining a more motivated workforce.
For industrial organizations, modern, advanced analytics platforms are optimized to connect disparate data sources, so users can seamlessly combine and interrogate information regardless of its origin. These platforms provide a combination of intuitive, self-service tools for data cleansing, time-stamp alignment and contextualization, empowering subject matter experts (SME) to quickly derive reliable insights referencing all available data. With live connectivity built into the software, SMEs can apply their analyses to near-real-time data, whether it’s stored in the cloud or onsite.
"Companies that invest in modern technologies and upskilling can expect measurable improvements in productivity and efficiency, while also attracting and retaining a more motivated workforce."
Access to these platforms lets users make tangible impacts in their organizations. For example, they can optimize operational efficiency and increase uptime, conduct root-cause analyses and mitigate issues, and/or monitor sustainability key performance indicators (KPI) in real time to decrease waste and emissions. Despite the relative ease of using these tools, companies must ensure their users are provided with sufficient training in software’s functions and features, and in general data analytics principles.
Traditionally, a primary hurdle for effective technical training was a lack of time, especially in sustained blocks, to engage in formal training courses, which often required multiple days offsite. Today’s technologies, however, pioneer new ways for industrial organizations to upskill their workforces, improving adoption with more time focused on value-added activities.
Generative AI and technology’s new frontier
Recent investments in genAI demonstrate this new approach. GenAI lets workers obtain faster results, while reducing the need for highly technical education and training. For example, engineers without formal training in programming languages can benefit from embedded and standalone genAI solutions, which lower the barrier to setting up complex analyses with advanced algorithms. This helps establish better project understanding, and facilitates improved collaboration with data-scientist colleagues.
Organizations must also ensure that users understand fundamental principles of interpretation, such as how to validate results. When enabling more workers to deploy complex machine-learning (ML) algorithms using simple prompts, it’s critical to precede that with the knowledge, understanding and ability to evaluate model validity and trustworthiness. It’s essential to pair every new technological deployment with a comprehensive and well-defined training path to ensure efficient and accurate outcomes.
Achieving success with advanced ML techniques and AI implementations requires giving workers sufficient training in these essential areas. Research shows that e-learning paths often yield better results than traditional classroom instruction, emphasizing the importance of access to resources while the work is performed. When delivered as part of an intentional and sustained upskilling program, this capability can also enhance the likelihood of successful adoption.
Putting it in practice
Lowering the barrier for employees to engage in tasks and approaches that support agile and digitalized workforces often incites organic transformation initiatives at the grassroots, including scaling pilots and even starting new ones. Seeing these direct impacts on the business, in addition to pathways for career growth, drives and sustains personnel’s motivation in their roles, which simultaneously improves worker satisfaction and retention.
One U.S., multinational, oil-and-gas company created a structured adoption plan to roll out AI-informed, advanced analytics technology across more than 50 sites globally. As adoption increased and experts in the technology emerged, the company followed a strategy known as “train the trainer,” which prioritizes first empowering internal champions, who understand where the technology should be implemented. These champions then facilitate training, and share tangible successes with new learners in the organization. Thanks to this community-led approach, the company has more than 4,000 unique users per month in the software platform, who can identify process anomalies and monitor equipment.
As with all new practices and procedures, fostering successful adoption requires providing staff with the right skills and training. The most successful digital transformation adopters are prioritizing just-in-time learning, empowering their workforces to use innovative technologies, which, in turn, motivates employees to pursue career growth, and push the boundaries of what’s possible for the business.
