Published on November 27, 2023, 7:43 pm

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? Also: If AI is the future of your business, should the CIO be the one in control?

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 Gyner Ozgul, 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.

According to Gyner Ozgul, the trade industry is facing numerous challenges beyond just a shrinking workforce. Tenured tradesmen are concerned that automation will render their skills obsolete. They are reluctant to embrace technology due to the fear that it will replace their knowledge and experience. On the other hand, younger tradesmen who grew up with technology see it as an essential tool in their work. Bridging this generational gap requires mentorship programs and personalized training initiatives that build confidence in both technical expertise and technological proficiency.

In addition to addressing the generational divide, businesses must also harness the power of data in order to thrive in today’s market. Data analytics strategies tailored to specific business goals enable employees to extract meaningful insights and achieve a higher return on investment. By utilizing data differentiation, companies can personalize customer engagement, continuously improve their services based on customer feedback, and differentiate themselves from competitors.

Shifting from reactive to proactive measures is essential in today’s fast-paced environment. Predictive maintenance, made possible by AI and IoT technology, allows businesses to anticipate and prevent equipment failures. This not only improves customer satisfaction but also optimizes resource allocation and reduces costs. The integration of IoT into asset management enables real-time monitoring, efficient scheduling, and streamlined operations.

To stay competitive, service trade businesses need to make informed decisions based on data-driven insights. Whether it’s optimizing resource utilization or enhancing self-help options for customers through online portals and knowledge bases, leveraging data is key to success. Furthermore, collaboration and knowledge sharing facilitated by AI can connect professionals across geographical boundaries, creating a collaborative network that fosters innovation.

Generative AI has the potential to revolutionize the service trade industry. By automating mundane tasks and providing customized solutions, it empowers service professionals to focus on their core expertise. From predictive maintenance to virtual training experiences, generative AI enhances productivity and delivers a personalized service experience for customers.

In conclusion, the adoption of generative AI in service trades presents both challenges and opportunities. By bridging the generational gap, harnessing the power of data, and embracing transformative technologies like AI and IoT, businesses can position themselves for success in


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