AI for the Nervous Support Executive
I’m starting a multi-month series about AI in support. This first post was curated by me (FT) and John Ragsdale and summarizes discussions from the Service Leaders Roundtable with Phil.
What is AI and what can it do for my organization?
AI (Artificial Intelligence) and ML (machine learning) work together to find patterns and act on these patterns, either by taking some kind of automated action or by making recommendations to the user. For instance, in a support environment, AI can recognize the intent behind a search query and serve up the appropriate documents. Or it can pick up on a flurry of support cases from the same contact and flag a manager that a customer escalation is likely.
Just from the two examples above—and there are many more potential use cases—you can see that your customers, your own team, and other internal teams can benefit from AI solutions.
- Customers can have a better experience, both in self-service and during assisted support.
- Your team can be more effective at providing support, from routing cases automatically, to troubleshooting issues, to detecting escalations.
- Your team can onboard new members faster, and more generally pinpoint opportunities to improve its processes and maturity.
- Other internal teams including Customer Success, Engineering, and Product Marketing can use data collected during support interactions to detect product and customer trends and thereby influence product and marketing decisions.
The benefits of AI have been touted for a decade or more, but the outcomes have been mixed, until now. We are seeing some wonderful successes, which we will share in upcoming posts.
Why can’t I just wait and see?
At this point, you may be thinking that you will just wait until clear winners emerge to get going with AI. Let others be the pioneers and you will wait, perhaps several years, until the field of vendors is more established. We believe that this is short-sighted:
- Can you afford to lose customers because you cannot provide the AI-backed experience that your competitors will provide?
- Can you afford to lose the efficiency gains brought about by automation and trend detection?
- Can you afford to lose team members because they are keen to use the latest technology and you cannot offer it to them?
We also believe that there are ways around (well-founded) concerns about AI, whether they are about cost and ROI, implementation timelines, or adoption. A wait-and-see approach is not the prudent way forward.
What should I do next?
Start exploring now! Waiting just allows your competitors to get an edge.
- Identify specific scenarios that would benefit your organization. It could be a chatbot, sentiment analysis, or search analytics (we will provide a more complete list of use cases in the next post). What would benefit your customers? Your team? Other internal teams?
- Seek other leaders who have adopted AI solutions and learn from their experience. We will provide candid case studies, not sponsored by vendors, so you can appreciate what it takes to roll out an AI solution and what you can reasonably expect.
- Select a commercial tool for any non-proprietary scenario. In other words, if you are seeking patterns in your product’s log files, you’ll need to build a tool for yourself. But if you are trying to improve your search, route cases, or measure case complexity, buy a ready-made solution. It will save you a few years (really!). Again, we will share popular use cases.
- Choose a short-term goal. As with any other tools, a reasonably quick win (say, in less than 90 days) will help convince doubters and your CFO.
- Think about your team members’ concerns. If an AI-powered chatbot handles 20% of incoming traffic, will they be without a job? If customer sentiment is automatically detected, will working with a grumpy customer mean a career hit?We will expand on users’ concerns in the case studies, but you know your team best.
Stay tuned for the next installment: AI scenarios for support organizations. In the meantime, tell us how your team is using AI, or what you would like to read about in the future.