Generative AI and the Future of the Trade Industry
Consider the following predictions:
A recent global survey of working professionals reveals that nearly 1 in 3 workers are using generative AI at the workplace. Forrester predicts that enterprise AI initiatives will boost productivity and creative problem-solving by 50%. Current AI projects already cite improvements of up to 40% in software development tasks. We also know that all AI projects begin as data projects. So what happens to industries or job functions that are not data-rich or mature? What other workforce dynamics come into play as businesses ready themselves for competing in an AI-led economy? Do the best algorithms — using the highest quality data, and advanced analytical skillsets — win?
By 2025, according to IDC, organizations will allocate over 40% of their core IT spending to AI-related initiatives, leading to a double-digit increase in the rate of product and process innovations. Furthermore, IDC predicts that enterprise spending on generative AI from now through 2027 will be 13 times greater than the growth rate for overall worldwide IT spending. Gartner predicts that the democratization of generative AI will occur due to the confluence of massively pre-trained models, cloud computing, and open source — making these models accessible to workers worldwide. By 2026, Gartner predicts, over 80% of enterprises will have used GenAI APIs and models and/or deployed GenAI-enabled applications in production environments, up from less than 5% in early 2023. The adoption of AI will lead to what Gartner calls the augmented-connected workforce (ACWF), a strategy for optimizing the value derived from human workers. The need to accelerate and scale talent is driving the ACWF trend. The ACWF uses intelligent applications and workforce analytics to provide everyday context and guidance to support the workforce’s experience, well-being, and ability to develop its own skills. At the same time, the ACWF drives business results and positive impact for key stakeholders. Through 2027, 25% of CIOs will use ACWF initiatives to reduce time to competency by 50% for key roles. Do all of these predictions around the adoption of generative AI timelines apply to all industries? Do businesses need a new operating model to compete in an AI-powered economy? What about cultural norms in certain industries that are not leading transformation with new emerging technologies?
To better understand the impact of generative AI on the service industry, I reached out to a truly innovative executive who is transforming his company and how it serves its stakeholders. Gyner Ozgul is president and chief operating officer of Smart Care Solutions, a national repair and service provider for commercial food service, refrigeration, and cold storage equipment. Smart Care ensures America’s grocery stores, restaurants, and commercial kitchens receive the food service equipment repair and maintenance services they need to stay up and running.
Here is Gyner’s point of view on the impact of automation and generative AI on the trade industry.
In the ever-changing landscape of the trade (aka, blue-collar) industry, businesses are encountering a myriad of challenges that go beyond the mere numbers of a shrinking workforce. From concerns about knowledge transfer among tenured tradesmen to the evolving expectations of younger generations and the overwhelming influx of data, the trade industry is at a pivotal moment. In this exploration, we’ll delve into these multifaceted challenges and discuss personalized strategies that not only overcome obstacles but foster a culture of innovation and resilience.
As technology advances, tenured tradesmen grapple with the looming fear that automation will make their skills redundant. They wish to protect the years of assumed skill and knowledge that they have rightfully earned without the convenience of currently available technologies. Some would argue this to be a selfish view; however, their historical struggle to learn and become successful created a justified bias against knowledge transfer. Although technology has become a proxy for the unwillingness to assist, the outcomes impact the growing shortage of skilled tradesmen and the crucial transfer of experience to the next generation. It’s not merely about replacing but evolving, and for this, mentorship programs and personalized training initiatives must bridge the generational gap during this customer time in transition.
Younger tradesmen, on the other hand, crave more than just technical prowess; they seek confidence in navigating the ever-changing technological landscape. For them, technology is not an option but a necessary tool. They have had technology as a part of most of their lives. In fact, some could argue that — without a successful technology component — attracting new and younger talent will continue to be a challenge in the trade industries. Fostering collaboration between tradesman groups and offering personalized training paths can instill the required confidence in both technical and technological spheres. Also, companies will need to start to build the data infrastructure to bridge the aforementioned knowledge gaps and enable newer talent to accelerate skill development and help to merchandise the virtuous interest of future talent.
In a world drowning in data, companies are challenged with extracting meaningful insights and generating a defined return on investment. The key is not to see data as an obstacle but as a strategic asset. Tailoring data analytics strategies to individual business goals and investing in personalized training empowers employees to extract value relevant to their roles.
Data and customers
Standing out in a crowded market requires more than just offering products or services. It’s about understanding and meeting the individual needs of customers. This demands a personalized approach to customer engagement, incorporating feedback loops for continuous improvement and refining services based on the unique preferences of each customer. In short, simply delivering great service will not be enough moving forward; building data differentiation will enable and foster market leadership in service industries.
The shift from reactive to proactive measures is paramount in today’s fast-paced environment. Predictive maintenance, for example, goes beyond fixing problems; it anticipates and prevents them. Tailoring prescriptive action plans to specific business processes and encouraging a culture where employees feel empowered to contribute their unique insights fosters a personalized approach to innovation and resilience.
In conclusion, the trade industry is facing numerous challenges as it navigates the adoption of generative AI and automation. However, with the right strategies in place, businesses can overcome these obstacles and foster a culture of innovation and resilience. By bridging the generational gap, investing in personalized training, and leveraging data as a strategic asset, companies can position themselves for success in an AI-powered economy.