Artificial intelligence (AI) is primed to reinforce customer service in the field.
In every industry, the stakes are high for enterprises to deliver both accurate results and memorable experiences. A Microsoft study for Dynamics 365 found 58% of consumers would terminate a business relationship because of negative service interactions.
In the pre-digital era, field service generally involved interim responses to immediate customer concerns. Increasingly, it is one of the many facets in a larger relationship that a customer has with an enterprise and its brand, cultivating meaningful connections for the former and generating a boost to the latter's bottom line from greater public loyalty.
Those powering field work are also experiencing change. These roles are increasingly careers and investments rather than simply jobs. More engaged technicians, engineers and support workers serve more connected customers and create brand differentiation.
Well before the pandemic, businesses turned to digital tools to adapt to necessary shifts in their service models. Recently, AI added new layers to their presence in customers' lives, supplying a key tool to respond to constantly evolving expectations and allowing for customers to be better understood.
As the leader of a visual support platform that for more than a decade has supported companies' service and growth goals through digitization, I'm perhaps a predictable evangelist of AI's transformative power. Still, from my experience, two takeaways demonstrate how AI can enable businesses to thrive and innovate within an ongoing field service revolution.
Nearly all businesses strive to create satisfying customer interactions. Customers may not always realize, but they enter interactions wanting to be seen and understood, two nuances that create loyalty over time.
Companies that use AI benefit from data that is fed into a larger engine of knowledge — analyzing events and detecting patterns — thus driving a more genuine understanding of customers and the problems they face. Armed with this insight, service providers can more quickly cut to the chase.
Potential use cases are extensive. Field technicians, agents and knowledge workers are empowered by intuitive, trainable algorithms allowing them to immerse themselves in the customer experience. Interactions are backed by tools that track feedback received or offer live suggestions prompting the next steps in an interaction. The technology also has the potential to start a service session on its own, such as through a chatbot system, with a human agent only stepping in after a situation has been triaged first or only in complex cases.
This isn't to say adoption of AI is an instant fix. Whether enterprises are purchasing a system or building one on their own, ensuring their business model is prepared to scale AI to take on challenges is essential for success. Even if a tool is brought on to augment person-to-person service, it would need to be knowledgeable enough about company operations to answer questions like, "What is the status of my claim?" or "What's the estimated completion time for my repairs?"
Secondly, the preparedness of your AI partner and associated solutions will not make up for enterprises not having the proper data and research to back up their project. It is essential to get granular on desired key performance indicators and outcomes, asking which parts of the customer experience would benefit most from AI. This will vary from one company and industry to the next. In all cases, if this background work hasn't been done yet, now is the time to start.
There is still a practical ceiling to AI's use. Most systems can handle answers to straightforward questions, rather than holding complex conversations. Lack of purpose and realism in adopting AI is like a car trip with no destination — not only going nowhere, but also depleting your fuel or battery. It's critical to consider these tools a valuable partner, but not a replacement.
Cultivating the "workers of the future" is a frequent AI goal. In my career, I've had the chance to work with field service groups who used the technology to transform their culture from a skills-based "do what you were trained for" organization to one where everyone can succeed. The impact on teams is fantastic, and the service delivered drastically improved.
These groups grasped one lesson in particular that separated their experiences from the majority of enterprises for whom AI projects failed. The most important lesson — for both new users and those who stumbled post-implementation — was to critically evaluate how well their teams were trained to work with the technology. For example, Gartner suggests that, for 56% of companies, concern over staff skill with AI is a major impediment to effective adoption.
Your company's goal should not be to work "alongside" AI or for workers and technology to coexist in separate silos. The most successful adopters consider it to be another member of the team, one who does what humans don't want to, but one that must also be trained and molded to fit into daily operations and work toward overarching goals.
Apprehension frequently precedes digital tool adoption. Businesses fear a lack of receptiveness by customers and teams fretting over a lost personal touch, concerned that only the young or tech-savvy will feel comfortable with a virtual service tool, or that resistance by teams will hinder their efforts.
However, these benefits respond to an ongoing revolution in service by channeling technology's strengths into human goals. Wisely balanced AI-human solutions are the most innovative way to satisfy more exacting expectations for both customers and the workers who serve them, ensuring companies can remain at the cutting edge no matter how quickly shifts occur in their industries and around the world.
This article originally appeared in Forbes
