An SFG℠ Analysts Take: There’s Nothing Artificial About Artificial Intelligence

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The global field services community is always looking for “the next big thing” to impact Field Service Management (FSM), and many research analysts (including myself) are far too willing to debate whether something like 3-D printing, wearable technology or Augmented Reality (AR) are merely new technology “fads” or, rather, transformative technologies that will ultimately (and quickly) change the face of field service forever. [Note: I believe they’re transformative!]

Whenever a new technology (or a new application for existing technology) is introduced, the initial discussions may range from “It will be the best thing since sliced bread” to “it will never be accepted by the marketplace”. Most, fortunately, find their way into the ability to support the increasingly expansive functionalities of today’s (and tomorrow’s) FSM solutions. Technologies like Augmented Reality (AR) have already established a strong foothold in field service, both as a standalone platform or, integrated with Virtual Reality (VR) into a Mixed Reality (MR) platform.

However, the one “new” technology for which there is virtually no debate, even among the industry’s diverse research analysts, is Artificial Intelligence (AI). For that matter, you can also include Machine Learning (ML) in this category.

What makes AI and ML so different from most of the “new” technologies we have seen talked about in the past is that, first and foremost, neither one is really a “new” technology. The term “Artificial Intelligence” was first introduced in 1956 at an academic conference. However, it was not until 1961 when mathematician Alan Turing (the lead character in the movie, “A Beautiful Mind”) wrote a paper on the application of machines to “simulate” human beings and their ability to perform intelligent tasks – initially to play chess (and to win at it!). [Even I co-authored a published article on neural networks and artificial intelligence applications for field service back in 1993!]

Fast forwarding to today, we see just about every services analyst writing about AI and ML. For example, analyst firm, Gartner, included both AI and ML among its “Top 10 Strategic Technology Trends for 2017”, stating that, “AI and machine learning have reached a critical tipping point and will increasingly augment and extend virtually every technology enabled service, thing or application. Creating intelligent systems that learn, adapt and potentially act autonomously rather than simply execute predefined instructions is primary battleground for technology vendors through at least 2020.”

Further, Gartner “advises CIOs to look at areas of the company that have large data sets but lack analytics. AI can provide augmented intelligence with respect to discovery, predictions, recommendations and automation at scale” – a perfect fit for field service!

However, research firm, Forrester, believes that “there is still a lot of AI progress to be made before machines can truly understand and guide next best actions” and that “Robots, AI will replace 7% of US jobs by 2025 (i.e., “16% of US jobs will be replaced, while the equivalent of 9% jobs will be created – a net loss of 7% of US jobs by 2025.”)

UK-based firm, iTouchVision cites the following four areas where it believes AI will likely have the greatest impact on the field service segment in the coming years:

  • Customer Experience – Primarily through the use of chatbots, “it will be possible to help customers with more speed and accuracy. These bots containing the customer and their equipment information can find out the problem and suggest a quick fix”.
  • Work Productivity – AI overcomes the hurdles faced by manual dispatchers. In the near future, we may also see the replacement of human dispatchers with an AI virtual assistant that considers all the service event parameters including unexpected events. It increases the job completion rate in the first visit by ensuring the worker has right tools and skills.
  • Predictive Maintenance – Predictive, rather than Preventive, maintenance is “the way to increase asset life and quality. The machine-to-machine interaction and the connected devices drive predictive maintenance. It eliminates the unnecessary technician visits to check machine condition”.
  • Data-Driven Decisions – “AI is all about data. With AI in use, it is possible to take more strategic decisions. Reduced repetitive administrative work allows human operatives to focus on predictive analysis. It governs end-to-end work and data flows with automation. Continuous data evaluation and processing presents a clear picture with analytics.

Overall, AI (and ML) are certainly not “artificial” – nor are they simply current fads or trends that will eventually bite the dust. They are real – not artificial; and, as such, should be carefully – and quickly – considered for incorporation into the field services management solution your organization uses to run its services operations.