From productivity enhancement to optimization and decarbonization efforts, today’s industrial organizations are setting ambitious corporate objectives with even more ambitious deadlines. In fact, according to Accenture, nearly all organizations (93%) will fail to achieve their Net Zero goals if they don’t at least double the pace of emissions reduction by 2030.
Data remains the key driver for progressing toward these corporate objectives. However, despite significant investments in digital transformation strategies, many companies still lack the ability to convert raw data to meaningful and actionable insights.
Innovative analytical technologies provide a mechanism for transforming data to insights, but this requires a workforce capable of understanding the inputs and interpreting insights. As industrial organizations continue experiencing a significant drop in the average tenure and time-in-role for employees, they are rapidly losing experienced subject matter expert (SME) knowledge.
Investments in new technologies like advanced analytics, AI, and machine learning are helping bridge this problematic knowledge gap. Ensuring proficiency in these technologies requires organizational investment in training and upskilling the workforce, which can be difficult due to time and other resource constraints. This article examines both the need for and challenges of upskilling workers across the industrial sector, and details how technology partners are accelerating workforce empowerment to maximize user adoption and efficiency with these tools to help companies overcome these roadblocks.
See also: A Roadmap to Boost Data Team Productivity in the Era of AI
Facing workforce challenges
The shortage of skilled labor in manufacturing has been anticipated and extensively documented for the last several years. Many of the most experienced workers are approaching retirement, while others are moving to more trending fields like renewables. According to the World Economic Forum’s Future of Jobs Report 2023, six out of ten workers will need training before 2027. However, only half currently have access to sufficient training opportunities. Additionally, the report highlights that, across various industries, training workers in AI and big data is a top priority for upskilling over the next five years.
Moreover, recent studies indicate that employees are increasingly seeking more than just high salaries. They recognize that mastering new technologies is essential for their long-term career prospects. For instance, a GETI report reveals that 87% of workers in the oil and gas sector would contemplate changing jobs, with many prioritizing professional growth and learning opportunities as crucial factors in their decision.
As a result, companies that do not invest in new technologies or enable their workforce to effectively use these tools risk losing their competitive advantage and worsening the existing skill shortage.
Upskilling with modern technologies
Luckily, the companies that are proactively investing in both innovative technologies and upskilling are seeing measurable productivity and efficiency improvements, while also attracting and retaining an energetic workforce.
Modern advanced analytics platforms enable personnel across industrial organizations to access multiple data sources to seamlessly combine and analyze data regardless of its source. Combining this capability with intuitive self-service tools empowers subject matter experts (SMEs) to transform their raw data into meaningful insights.
With access to these technologies, teams can easily and efficiently increase uptime, complete root cause analyses, or monitor greenhouse gas emissions in real time. However, while easy to learn and use, there is a notable requirement for training—both in leveraging the technology’s functions and features, and in the principles of data analytics.
Historically, the most prevalent challenge in technical training was a lack of time, especially in large, uninterrupted blocks that often required personnel to spend multiple days off-site. To combat these constraints, modern technology companies are spearheading new approaches to upskill the workforce.
Enhancing workflows with GenAI
The emergence of generative artificial intelligence (GenAI) over the last two years is ushering in new and exciting opportunities for workers to get faster results, while reducing the need for formal education and training.
Forward-thinking industrial organizations are investing in embedded and stand-alone solutions that offer their teams a way to generate text or code based on user prompts, making it easier to achieve operational excellence. By providing summaries and detailed explanations in natural language, SMEs can better understand the full process picture and make data-driven decisions with beneficial results. This empowers personnel to efficiently analyze massive datasets, identify trends and anomalies, and make proactive, informed decisions, fostering operational improvements in production, quality, and yield across the industrial sector.
Additionally, for engineers without formal training or time to study programming languages like Python or R, these solutions significantly lower the barrier to entry for setting up advanced analysis with sophisticated algorithms. It also facilitates better project understanding and collaboration with data scientist colleagues or third parties.
Understanding your technology and your data
While GenAI brings long-awaited improvements for the future of manufacturing, it is not reasonable for organizations to treat AI or generative technologies as a magic bullet that will solve all operational issues. This is especially true in the process industries, which uniquely require data cleansing and contextualization due to noisy data. Data quality is also crucial to the success of these solutions. The technology’s output is only as good as the quality of the data, and as the saying goes, garbage in equals garbage out.
Before deploying these solutions, it is also critical for teams to assess whether users are equipped with the knowledge to prepare their data effectively and the skills to develop and maintain GenAI solutions.
Investing in upskilling
While new technologies, including advanced analytics platforms and GenAI tools, promise to boost productivity, they require a skilled workforce that can understand and act on their results to make meaningful impacts across organizations. Organizations that focus on both empowering their workforce and adopting modern tools will see the greatest returns on their investments.
Dustin Johnson is the Chief Technology Officer at Seeq, responsible for the advanced technology infrastructure, vision, and roadmap of Seeq software solutions. He is a founding partner at Seeq and has played a critical role in growing the Seeq product portfolio to meet the needs of the company’s ever-expanding and diverse customer base. Dustin has more than 20 years of experience in the software industry. Prior to joining Seeq, he served as a Chief Engineer at aerospace startup Insitu, where he led a diverse and talented group of engineers. Dustin has enjoyed a varied career ranging from space launch support to the development of Wireshark, a popular network analyzer.
