The first time I came across a field service application was back in the mid-1990s and it seemed to be just one step up from using a blackboard and chalk to keep track of technicians, scheduled jobs, and spare parts.  The remote worker “app” was a folder with forms and instructions.

Innovation came later but was very limited: technicians got laptops to run crude, gritty applications that did no more than capture the information and print the same forms which were then manually processed; processes and tools remained antiquated.  We later switched remote workers for mobile workers, and folders for great mobile apps – even “grease boards” used by dispatchers incorporated artificial intelligence components and became smart.

We moved from trying to efficiently operate remote technicians to making it mission-critical, part of customer experience initiatives, and strategic to the company’s ambitions for customer-centricity. We realized that technicians in front of customers must be part of the solution; they’re as valuable to the overall experience as a contact center agent chatting them online or a call center agent talking to them on the phone.

This is not your parent’s field service anymore.   

As technologies evolved to make customer service better aligned with customer-centric, effectiveness-focused operations we identified the mission critical advanced technologies needed.  The slow evolution over the last decade for Field Service Management (FSM) meant that companies couldn’t equate their improvements in other areas to FSM.  With the rise of the post Big-Data age, and better applications for the data collected, finally FSM can be comparable to other parts of customer service and the organization.

There are three technologies that are increasingly being adopted by enterprises and allocated to field service management to do that:  data management (part of digital transformation), optimization (part of advanced analytics), and automation (part of artificial intelligence). 

Digital transformation is here to stay; it is powering budgets for more changes in customer service and field service than any other source in the next couple of years.  The goal of digital transformation – after all the hype dies and according to the first few strategic implementations we have seen so far – is to use data management techniques better to optimize processes and outcomes.

In FSM there are two areas where data management, better use of the data, can yield great results:

●  Provide better visibility. In traditional field service applications, data was only as good as the last update that was done to the laptop-based application.   This limited dramatically what could be seen and done when in the field.  New apps, always connected via cellular and Wi-Fi networks, can use the latest and greatest data – even that owned by partners and other stakeholders – to provide mobile workers a perspective of the customer that was heretofore impossible.  Using the latest and greatest data, knowledge and content can reduce the number of visits to solve an issue, improve the communication with the customer, and even provide real-time updates to the customer of other outstanding issues.

●  Create better back office processes.  Like the outbound service model in the point above, being able to collect data on the field and share immediately (or soon after, if not connected) with the rest of the organization, has improved the feedback that can be provided not only to front-office processes (e.g. a solution to a common problem can be easily distributed to other mobile workers and other customers in need), but also to optimize back office processes.  Delivery drivers personify the best implementation of this benefit, whereas before they had limited ability to share with the organization what they learned about customers’ expectations and can now provide that feedback and the organization can use it to create new products and solutions for those same customers.

Optimization has been the innovation area for field service for the past decade or so – at least on paper.  While there have been advances made over the years in field service and optimization, they have been very meek compared to the potential evolution it can embrace.

Advanced analytics innovation is the last step before embracing machine learning, artificial intelligence, and automation – and the next logical stop when data is better managed.  While traditional optimization done in field service was done using past data to improve future outcomes (hopefully), it was traditionally done without any intelligence; you had to know what you were optimizing (technician schedules being the most common) and stick to that.  Iterations were uncommon, repetition of the exercise often led to conflicting results, and the time it took to calculate and implement erased potential gains.

With advanced analytics optimization we see three potential areas where FSM can be optimized as a warm-up stage for AI:

●  Logistics. Offering the largest potential, by far, from the many examples – logistical nightmares were always the dark cloud of FSM.  Making sure the right technician with right know-how, the right part, and the right tools were delivered to the right place to solve a problem could sometimes take weeks – if not longer.  Using new optimization techniques not only we can ensure that the all the right elements coincide at the right place, but we can also manage ordering, inventory, training, and even dispatch using the latest service call results to plan future logistics and the operations behind them.

●  Pre-emptive service. Knowing which customers will need what type of service soon can effectively turbo-charge the performance of the FSM staff.  Mobile workers and their tools and parts can be easily sent to customers’ locations right before failure – or even better, during expected down-time or slower-production times but before failure.  This can optimize, automatically, FSM as a semi-automated, fully-autonomous operation.

●  Agile process improvement. One of the tenets of digital transformation is the ability to optimize processes the organization uses to support customers.  Unfortunately, for most organizations that means doing a change in processes once and then waiting for the next issue to arise to do it again – usually without a specific plan, rather waiting for systems to suggest trouble areas.  In FSM this change is more impactful than for other parts of customer service due to the remote nature of the work to be done.  The ability to more quickly optimize those mobile processes will give FSM a better foothold in the emerging world of business transformation.

Automation is the final frontier for customer service.  Customer service implementations will continue evolving and embracing automation to eventually have self-service and chatbots supporting sixty to eighty percent of interactions in an automated customer service solution.

Automation holds the most promise for field service when putting together the three areas we have discussed in this paper because it cannot happen without proper data management (it is about the data, after all) and without optimization sowing the way for automation to happen.  We are seeing early indication that AI can be used to automate two key processes in FSM:

●  Workload management. Most of the use of optimization for workload management, while successful at managing workloads, failed to address two key issues: timing and recurrence.  While most companies did optimize their workload based on past data, the questions to ask, the preparation of algorithms, and the processes were intransient.  Adding artificial intelligence can manage workloads constantly, not just occasionally, and can even do it automatically.  This will change the resource allocation for FSM and align it with corporate transformation strategies.

●  Dynamic FSM. Embracing ecosystems, the natural outcome of the technology changes brought out by digital and business transformation initiatives, prepares the organizations to deliver dynamic, real-time-influenced experiences to customers as they demand and need them.  For FSM to adapt and comply with this model, it is necessary to create dynamic processes and outcomes, and those are only possible in fully automated implementations driven by machine learning and artificial intelligence.

This is not to say that these examples are the only way these technologies can help.  Nor that these are the only technologies: we did not cover IoT because it is becoming an entire new model for field service that will need these three technologies adopted and in place first to deliver strategic value.  We are already seeing vendors deploy IoT in projects that are showing promise; time is going to improve these models over the next few years.  The value FSM generates will grow by magnitudes with these elements in place.  

The bottom line: Disruptive innovation, digital transformation, and the evolution of mobile-first solutions for field service will make it a cornerstone of an end-to-end experience generation infrastructure where customers and organizations can co-create value: customers will be fully covered in their needs for support and organizations will embrace continuous optimization as the way to get there.

FSM today is not your parents (or even grandparent’s) model anymore – it is a critical part of solving the pains of mobile workers and remote operations being part of a dynamic working model to deliver a transformed business. 

And that’s what you parents wanted to see FSM do.

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