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? 

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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.

Workforce Dynamics

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.

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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 problem-solving. The value provided to the customer is peace of mind that their business will deliver on its goals efficiently without concern about the uncertainty of process or product failures. In addition to data, the Internet of Things (IoT) can help bridge some of those customer needs.

Also: 7 ways to make sure your data is ready for generative AI

The integration of the IoT into asset management is crucial for various industries. IoT enables assets to be connected to the internet, allowing for real-time monitoring and tracking. This connectivity facilitates predictive maintenance, helping organizations schedule repairs efficiently and reduce downtime. The data collected by IoT devices enables data-driven decision-making, which in turn optimizes asset utilization and streamlines operations. This approach leads to cost savings, improved security through real-time asset tracking, and efficient resource utilization.

Furthermore, IoT contributes to compliance with regulatory standards, enhances customer service by ensuring reliable asset availability, and aids in supply chain optimization. Environmental monitoring is another key aspect, particularly for assets with environmental impact, as IoT sensors can track and ensure compliance with environmental standards. Overall, the incorporation of IoT into asset management practices provides organizations with valuable insights, leading to increased efficiency, reduced costs, and improved overall performance.

Data and efficiency

In the dynamic landscape of service trade, harnessing the power of data is pivotal for businesses aiming to enhance efficiency and deliver exceptional service. Through data-driven insights, service trade enterprises can optimize their operations in several key areas. Efficient resource allocation is achieved by analyzing data to understand service demand, ensuring the right deployment of staff, equipment, and materials. Streamlining scheduling and dispatching processes becomes possible with real-time information, minimizing travel times and improving response times to customer requests.

CRM systems fueled by data enable businesses to tailor services to customer preferences, fostering satisfaction and loyalty. Automated billing systems, driven by accurate data, simplify invoicing processes, contributing to improved cash flow and reduced administrative overhead. Data analytics also plays a crucial role in inventory management, preventing overstocking or stockouts by providing insights into usage patterns and demand fluctuations. Monitoring performance through key metrics allows businesses to continually enhance the efficiency of field service operations.

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Moreover, self-help efficiency for customers is facilitated through data-driven approaches. Online portals and knowledge bases, powered by insights into customer behavior, empower clients to troubleshoot issues independently, reducing the need for extensive support and enhancing overall customer satisfaction. Staying competitive in the market is facilitated by analyzing trends and customer behavior. This insight enables businesses to adapt services to evolving customer needs and preferences.

In essence, the integration of data-driven approaches empowers service trade businesses to make informed decisions, optimize resource utilization, enhance self-help options for customers, and navigate the evolving landscape of customer expectations, ultimately contributing to improved overall efficiency and competitiveness.

Embracing generative AI

In the not-so-distant present, the winds of change are sweeping through service trades, carried on the wings of generative AI. Picture this: a world where the tedious shackles of repetitive tasks are loosened, where service professionals are liberated to focus on the essence of their craft. In the here and now, generative AI is weaving itself into the fabric of service trades, automating the mundane. Mundane tasks, such as scheduling and invoicing, are handled effortlessly by AI, allowing plumbers and electricians to redirect their expertise where it matters most.

Yet, this is just the beginning of the narrative. As the tale unfolds, generative AI evolves into a bespoke storyteller, crafting customized solutions tailored to the unique needs of each customer. In construction, for instance, AI becomes the architect of innovation, sketching structures that defy convention. The plot thickens with the anticipation of predictive maintenance. Today, AI pores over sensor data, predicting equipment hiccups before they morph into disasters. Tomorrow, the narrative deepens, algorithms predicting with surgical precision, orchestrating maintenance schedules like a maestro conducting a symphony.

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On the training grounds, generative AI acts as a mentor, immersing service professionals in lifelike scenarios. The training is not just about mastering skills but about stepping into a virtual realm where mistakes are a safe prelude to real-world mastery. The narrative of skill development unfolds, augmented by VR and AR applications, creating a tapestry of expertise. As our story ventures into the realm of customer service, AI dons the hat of a conversational maestro. Chatbots evolve into virtual assistants capable of engaging in nuanced conversations, enhancing the service experience. The dialogue is no longer scripted; it’s a dynamic exchange, a dance of words choreographed by natural language processing.

Resource allocation becomes a subplot in this grand narrative. AI, armed with historical insights, guides service businesses in optimizing their resources. Yet, as the narrative evolves, AI becomes a dynamic conductor, adjusting resources on the fly in response to the ever-shifting cadence of market demands and unforeseen disruptions. The narrative arc extends to collaboration and knowledge sharing, where AI becomes the glue binding professionals in a global network. It recommends solutions, shares best practices, and connects experts across geographical boundaries, fostering a collaborative symphony of expertise.

In the epilogue, sustainability takes center stage. AI emerges as a guardian of eco-conscious practices, guiding service trades toward greener pastures. The narrative concludes with a vision of service industries harmonizing with the environment, a finale where generative AI plays a pivotal role in orchestrating a sustainable future.

Thus, the story of generative AI in service trades unfolds, a narrative rich with automation, customization, and innovation. The characters in this tale — the service professionals, the customers, and the AI itself — are woven into a tapestry of progress, with each chapter promising new dimensions to the evolving narrative of transformative technologies.

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In the ever-evolving trade industry, businesses face challenges beyond a diminishing workforce, encompassing issues such as knowledge transfer, changing expectations among generations, and a deluge of data. The tension between tenured tradesmen and technology is palpable, as fears of automation creating redundancy clash with the necessity of embracing technological tools.

Mentorship programs and personalized training initiatives are crucial for bridging the generational gap, fostering a culture of evolution rather than replacement. Simultaneously, younger tradesmen, born into a world saturated with technology, necessitate collaborative efforts and tailored training to build confidence in both technical and technological realms. Furthermore, companies must invest in robust data infrastructure to facilitate knowledge transfer and accelerate skill development for the incoming workforce.

In the landscape of service trade, data emerges as a strategic asset rather than an obstacle. Tailoring data analytics strategies to individual business goals empowers employees, leading to meaningful insights and a defined return on investment. Customer engagement becomes personalized through data differentiation, incorporating feedback loops for continuous improvement. Shifting from reactive to proactive measures, predictive maintenance, and the integration of the Internet of Things (IoT) into asset management emerge as essential strategies for optimizing operations, reducing costs, and enhancing overall performance. 

Harnessing data-driven approaches allows service trade businesses to make informed decisions, optimize resource utilization, and stay competitive in a rapidly changing market. As the winds of change sweep through service trades, generative AI emerges as a transformative force, automating mundane tasks, crafting customized solutions, and playing a pivotal role in the narrative of progress. This narrative encompasses skill development, resource allocation, collaboration, and sustainability, promising a future where generative AI orchestrates a sustainable and innovative path forward for service trades.

This article was co-authored by Gyner Ozgul, president and chief operating officer of Smart Care Solutions.

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