For some, the terms artificial intelligence (AI) and machine learning conjure up images of science fiction or futuristic technology. However, AI and machine learning are real and now and can make a difference for field service providers. AI and machine learning, when integrated into field service management solutions, help to streamline workforce management and ultimately enhance customer experience.
Is your organization ready to leverage the power of AI and machine learning in its field service operations? In this blog article, we define AI and machine learning as they apply to field service, review market trends, and look at applications in field service management today.
Not One and the Same
AI and machine learning are often used as synonyms. However, technically they do not mean the same thing.
AI is an umbrella term for technology applications that mimic human intelligence and provide automated reasoning and decision-making capabilities. Machine learning, a subset application of AI, gives computers the ability to automatically learn and make decisions by recognizing patterns based on data gathered and stored rather than from direct programming. In other words, machine learning is AI, but not all AI is machine learning.
AI as it relates to field service software is the ability to optimize every aspect of field service delivery—even without prior programming. By processing real-world data and running simulations based on various objects, a field management service solution with AI enables a smarter, more efficient workforce with benefits delivered in real time.
Machine learning in field service empowers organizations to provide better service with predictive insights and data-driven decision making. With machine learning powering predictions and automating optimal decisions, your team can service more customers per day and quickly adjust to disruptions that will inevitably pop up.
Growth Sparked by Data
AI and machine learning were originally coined in the 1950s, with government interest in research and funding ebbing and flowing for decades. Recently, the concept of applying AI and machine learning in business has re-captured the imagination of the global business and technology community. Why? Exponential data growth.
According to a study by IDC, worldwide data will grow from 33 zettabytes in 2018 to 175 zettabytes by 2025, for a Compound Annual Growth Rate (CAGR) of 61%. By 2025, IDC predicts that nearly 30% of the world’s data will need real-time processing and 49% of data will be stored in public cloud environments.
In another IDC report, worldwide spending on AI systems is forecast to reach $57.6 billion in 2021. McKinsey estimates total annual external investment in AI was between $8B to $12B in 2016, with almost 60% of that investment targeted at machine learning. To further support the growing impact of machine learning, patents speciﬁcally targeted at machine learning techniques grew at a 34% CAGR between 2013 and 2017, the third-fastest growing category of all patents granted.
With this research in mind, where does the field service industry stand when it comes to adopting AI and machine learning? According to Gartner, “in 2022, only 30% of field service providers will be ready to deploy AI-based decision support in their field service management platforms in order to differentiate, despite robust capabilities being available by then.” However, early adopters that are using AI-enabled field service management software now are seeing favorable results in field service delivery and customer experience.
Real World Applications
There are many ways that AI and machine learning can be applied throughout the service cycle to deliver value to business operations and customer experience. One way that field service providers are already seeing benefits with AI and machine learning is through optimized scheduling and dispatch.
- Diebold Nixdorf, an ATM manufacturer, is using AI-driven automated scheduling to manage aggressive service level agreements against millions of job tickets. As a result, Diebold can provide better customer experience and access single-source data related to capacity planning and workforce management. Specifically, Diebold increased technician productivity by 33%.
- SBA Communications, an independent owner and operator of wireless communications infrastructure, optimized the operational efficiency of its mobile workforce to accomplish more in each day. Due to route optimization, SBA saw miles driven go down from 60.4 to 48.2, a decrease of 20%.
- Unisys, a leading technology company, needed its service organization to run more efficiently. With optimized scheduling, Unisys reduced the number of schedulers while increasing the number of jobs completed per day. Unisys saw a 40% improvement in jobs scheduled per day, more than 90% of calls in its next-day business scheduled automatically, and other positive results.
Field service organizations are also experiencing operational improvements and boosting customer experience through predictive field service. Powered by AI, machine learning, and data science, predictive field service increases schedule accuracy and ultimately delivers better customer service through predictive job duration, predictive customer cancellation, predictive first-time fix, and predictive parts maintenance.
Scheduling and predictive field service are just two ways that AI and machine learning are making a difference in field service operations today. There are other applications on the horizon, such as chatbots and self-driving cars, that will transform field service delivery.
The Time is Now
AI and machine learning are making slow, but significant inroads in field service management—with early adopters already reaping benefits today. Mountains of data, coupled with the power of AI and machine learning, are setting the stage for a powerful and valuable role for electronic brain power to streamline workforce operations further and take customer experience to a whole new level of excellence. Now is the time to capitalize on this powerful opportunity to bring your business a future of new advantages.