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Given all of the hype that they’re experiencing, it’s a safe time to start thinking about chatbots as disruptors to the field service industry, specifically their place in mobile app development.

First, the Hype

For the uninitiated, a chatbot is a program that mimics conversations with people using artificial intelligence techniques such as natural language processing (NLP), image and video processing, and audio analysis.

Often, chatbots can learn from their past interactions, improving their responses over time to more accurately accomplish their tasks. It’s this ability to converse in a natural manner while learning on-the-fly that holds the most promise as far as the future of chatbots is concerned. This is precisely where the opportunity for disruption arises; providing automated question-and-answer interactions with your customers and employees with human‑like interactivity without the associated costs and overheads – sounds like real disruption at scale.

The efficiency and ease of interaction they create for field service employees and customers has driven the broad appeal. For organizations that drive significant revenue from field operations, the potential benefits are significant.

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Let’s Take a Deep Breath

While chatbots hold a lot of promise, there’s still some way to go before they can confidently be deployed as replacements for both human interaction and applications currently driving customer interactions.

Today, chatbots are better suited to support preliminary interactions between user and system, such as collecting basic contact information and beginning the process of issue resolution. The benefit here is making the eventual human interaction more efficient and personalized. A common example is providing a customer care chatbot with your name and account number, so the eventual live-person representative you speak with has your records pulled up and ready for your call.

Chatbots have yet to master the subtleties and nuances of language, however – slang, colloquialisms, and even sarcasm all pose challenges for the rigidity of current chatbot deployments.

Also to be fully useful, chatbots will need to go beyond just understanding definitions, but also to detect and synthesize context. This will require analysis and understanding of an organization’s unique product and service offerings (and all of their permutations), as well as internal process flows. This is a huge challenge for any organization – to capture hard-to-document tribal knowledge that creates meaningful customer interactions.

It’s important to point out that chatbots aren’t useful in all scenarios, as not all interactions lend themselves to a chat interface alone. This is definitely true in the field service world.

Like humans, chatbots learn from experience. When a person asks a question that the chatbot doesn’t know, that question is automatically flagged, and an administrator adds the answer to the list of keywords and phrases. That being said, if the only way to figure out what your bot needs to know next is to frustrate a customer, you’re looking at losing potential business.

But even with that in mind, some field-service companies are using chatbots to make their operations more efficient and save some cash. There is an opportunity for a service company to differentiate itself from the pack: chatbots offer customers with questions a way to get answers without waiting on hold, or taking up your admin team’s time.

Most chatbot deployments aren’t ready to accommodate the complexities of conversations and still rely on human intervention to be successful. Successful chatbots depend on core technology such as natural language processing, artificial intelligence, and machine learning — aided by thousands of iterations to drive learning.

So, chatbots are not quite ready to take over the world, but they are useful for automating basic data collection tasks that can be repetitive or time consuming, freeing up field resources for more productive activities. At this point, they’re more likely to be incorporated into mobile apps, as opposed to replacing them.

So, Where Do We Start?

When evaluating use cases for chatbots in your customer facing apps here are some suggestions on where to get started:

• Common interaction points are probably a good place to start. For example, when looking to better manage interactions with clients, ask yourself where do clients most often engage with your organization? For example:
  • Article and resource suggestions for visitors to your knowledge base
  • Information collection for more efficient handoff to support or field service technicians
  • Scheduling or rescheduling of appointments
  • Simple repeat orders or facilitating returns
• Focus on narrow use cases early on, and expand from there. Experiment with non-critical business processes on chat platforms.
• Use existing human resources as a backstop for important processes and service-level agreements. Cursory content and programmed responses (supported by “if, then” statements) may support some consumer requests, but humans must be in place to quickly take over chats within the context of the conversation or previous customer engagements.

In Conclusion

Chatbots have the potential to enhance your mobile apps with improved information distribution and customer care over time by making them more natural in their responses and suggested next steps. However, complex chatbots that surpass (aka replace) apps in convenience will be complex and depend heavily on artificial intelligence to interpret consumer intent and reduce consumer burden. The industry isn’t there yet. Moreover, chatbots will never be a standalone solution to business challenges. They will be a part of a company’s larger portfolio of digital touch points.

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This is the first article in MobileFrame’s Mobile Disruptions Series.  Check our Mobile Disruptions topic from time to time for other interesting articles about disruptive trends in the mobility industry.