AI and the Future of Field Service: Moving from Efficiency to Innovation
From the 10,000-foot view for field service, the priority is typically to enable faster fixes, for less cost, at less risk, and ideally with a seamless automation of administrative processes. For the CIO managing a portfolio of field enablement projects, these will likely seem like familiar criteria.
But, field service performance criteria are already under pressure to change. The new age of digital has given the enterprise a different view into what it means to focus on customer connection and startups such as Airbnb, Uber and Tesla have continued to put added pressure on traditional business models to adapt to the new world of customer expectations. The net result is that the customer expectations are rising, and all industries are feeling the pressure to step up their game.
There are also revenue opportunities emerging as what was once was a post-sale service contract is rapidly becoming the platform for new business models. For example, ThyssenKrupp now see significant revenue opportunity in servicing all elevators in the marketplace with their recent enhancements in IoT and maintenance predictive algorithms. Examples like ThyssenKrupp show a trend where goals are shifting from simply selling the product over to servicing a solution end-to-end regardless of which product is being used.
In this new digital world, it seems that connection to customer is a critical differentiator for everyone
In this new digital world, it seems that connection to customer is a critical differentiator for everyone. Field service is a critical customer connection opportunity. So, it would follow that some of our greatest efforts to innovate would be in the field service space. And indeed, we find tremendous renewed interest in this space; yet, even a casual review of articles from researchers, analysts and luminaries reveal that much of the focus in field service is still on the familiar criteria: faster fixes, for less cost, at less risk.
CIOs might recognize this familiar conundrum. The existing field services system works and is continuously being optimized for better performance but there is growing evidence that the whole system or some significant part of the system will need to adapt to competitive forces from digitization. What investments can a CIO make today that will position both the IT Unit and the field service team for the transition ahead?
It may help to recall a previous such change that sprang from the forces of digitization. In the very early stages of digital transformation when on-premises systems started to be more expensive and less adaptable than cloud-based variants, the IT staff worried this might result in the elimination of IT jobs. While it did affect some, it also created new demand for a technology-backed skillset to work closely with the rest of the company to bring stakeholders together and innovate on top of the new cloud-based platform.
Here with field service, we may have a similar opportunity. Just as IT is moving from building and maintaining systems of record over to innovating with partners to grow the business, so too will the field service organization move from its traditional criteria over to more focus on listening to customers and distilling what is heard into insights. Step one in building the capacity to aggregate actionable insights from across thousands of customer interactions a day, is a job well suited to AI.
AI takes in data from a variety of sources. With the help of its programmers it learns to accurately identify certain patterns in the data. Once it reaches a certain accuracy level the AI is taught to trigger a corrective or opportunistic action. For example: automated driving, high volume stock traders and spam blockers all use AI but their AI aren’t self-sufficient, the accuracy of their actions continuously evolves and so requires continuous investment to keep the pattern recognition skills up to date.
There is little doubt that AI will continue to advance rapidly but when your criteria for investment is faster fixes, for less cost, at less risk, continuous investment make the margins on AI ROI very tight.
There are also fundamental data quality differences between what you might collect via a CRM/ERP platform and what’s available on the stock market. Data hygiene within the enterprise has always been a thorn in the side of the CIO. This hygiene issue requires a significant change in culture to overcome thus further tightening the margins for AI to have impact.
In summary, because of the pressing nature digitization it is not advisable to just wait for better models to emerge as that may be too late. But the current capabilities of modern AI also require CIOs to act with strategic insight into what they do well today (recognize patterns) and what they do less well (take unmonitored actions based upon poor quality data). Also, attempting any project where a primary requisite is the cleaning up all the data associated with field service work must have a correspondingly urgent executive sponsorship if it is to have any chance of resulting in a different outcome than the efforts of the past 20+ years of IT CRM/ERP projects.
It is critical for a CIO to have a future-facing, low-risk, high-reward portfolio for field service they might consider striking up a new type of relationship directly between the field service team and an AI implementation, skipping the system of record. This new project would focus upon natural language processing and use a simple mobile-based interface for data capture. This is a scenario where a skunk-works program in this mold can generate quick, reliable, and impactful new insights into customer trends thus allowing the organization to invest in optimization of the old system while putting a future system in a potential “leapfrog” position.