To make servitization a reality, other major tech trends like the Internet of Things (IoT), Artificial Intelligence (AI) and machine learning are serving as some of the supportive pillars of product uptime. And at Field Service USA 2018, hosted by Worldwide Business Research, the Syncron team led our annual workshop focused on these specific trends and technologies and their potential impact on field service.
Artificial intelligence, technology that perceives the world around it, forms plans and makes decisions to achieve specific goals, is finding its way into computer vision, robotics and natural language processing. Machine learning, a subfield of AI designed to enable computers to learn on their own through algorithms that identify patterns in observed data, helps build models that predict behaviors without having explicit pre-programmed rules and models.
And, IoT, the intertwined network of physical goods, including sensors and software, allows manufacturers to freely exchange data between the products they sell — like large pieces of heavy equipment — and their internal systems. Experts estimate that IoT will consist of about 30 billion objects by 2020, with a global market value of $7.1 trillion.
That’s why we created our newest Syncron original Orange Paper, “State of Emerging Tech in Field Service: Servitization, IoT & AI, Market Disruptors and The Road to Uptime,” to help manufacturers leverage artificial intelligence and machine learning as the keys to gaining IoT insights at scale.
The application opportunities of IoT and AI for field service are vast, including detecting, troubleshooting and resolving issues (often remotely) before customers are even aware there’s a problem, as well as improving first-time fix rates through schedule optimization, performance indicators, remote diagnostics and intelligent service part inventory optimization.
But, ultimately, the real question isn’t should field service organizations incorporate IoT and AI into their service processes, but how quickly can they make it a reality?
How do you add sensors to existing parts in equipment already in the field, and how do you change your manufacturing processes so sensors are a part of the design in all new equipment? Then, how do you leverage machine learning to make sense of all that data?
How Real is IoT?
During the workshop, less than half the group said they had any sort of current initiative for IoT; in fact, less than half have embraced IoT at all, yet. The largest concern here was security and the simple fact that IoT creates potential customer data visibility, spurring concerns on how to protect that private data at a higher level.
And, as the field service technician demographics shift, older technicians may require new training to continue to be effective, as the rise of mobile devices and connected technology fundamentally changes the tools and processes they will need to follow to be successful. The incoming generation is much more aligned with mobile devices and internet functionality, and, from a recruiting perspective, companies are finding it crucial to have technology that addresses training and mobility.
But, it’s clear that the competitive effects of IoT are already playing out in the marketplace. As McKinsey coined Tesla “IoT on wheels,” we’re seeing Tesla’s market capitalization come to a rough market equivalent to that of General Motors, despite its revenue being less than one-twentieth of GM’s – largely because of IoT. By collecting data from its vehicles, and using machine learning to improve predictive maintenance, self-driving capabilities, and the driving experience of its cars significantly and continuously, Tesla is changing the automotive industry through IoT.
Artificial Intelligence isn’t All Artificial
Then there’s the management communication aspect of addressing these technologies. As IoT and AI find their way into consumer examples, like a Dominos app that tracks every step of your pizza order, field service leaders are left wondering how to bring the consumer examples to the enterprise – or in many cases, recognize the difference in feasibility. One oil and gas company even admitted that it’s trying to do more in the Augmented Reality (AR)/Virtual Reality (VR) technology field, going so far as to hire a former developer of the Pokémon Go app.
Ultimately, everyone wants to incorporate IoT, but there’s still major obstacles to making that a reality…
…from the development of products and incorporating sensors, to building databases to house the data, to customer security concerns, and more. And, as consumer technology continues to influence the enterprise, customer expectations are evolving to such a degree that they expect the enterprise to deliver the same sorts of experiences with IoT as B2C-facing products.
To succeed, companies need to embrace artificial intelligence as the key to gaining IoT insights at scale — before being left in the dust. Download your complimentary copy of our Orange Paper today, and start incorporating this new strategy in your field service organizations now.
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