What is Being Done Today to Capture Service/Support Expertise?

by | Jan 28, 2020

We have previously posted about the “Silver Tsunami” and it’s impact on service organizations. Most companies are responding to this concern by trying to create some kind of centralized knowledge base or service data repository that could be accessed by technicians in the field. Many of these companies either have already deployed or are looking at some kind of chatbot solution as well that can supplement their call center agents’ capabilities. Many are also looking into AI.

Let’s explore each of these potential solutions, particularly from the perspective of service/support organizations tasked with supporting complex products.

Knowledge Base (KB): Many either already have a KB or are trying to implement one. Most who have a KB are now trying to develop a mobile app that will enable their technicians to access it remotely. From our experience, the table below shows the positives and negatives of this solution:

Knowledge Base:

Positives Negatives
Multiple departments can contribute/author documents Different knowledge levels of authors will result in non-standard documents
Better information tends to float to the top, old information gets archived based on usage Need full-time staff to manage, curate, validate and update documents
A good KB can relieve the workload on the support team If you are starting from scratch, the effort and time needed to create a usable KB could take months or even years
  Requires a search function. The vagaries of search systems and how keywords are entered by different users can lead to inconsistent or inaccurate results
  Authors have to contribute and take the time to write detailed, organized and informative documents. Some groups, like product management or technical writers, may have the time and resources to do this. Service and support technicians typically do not.
  Time-consuming and expensive to maintain


Chatbots:  We have written in an earlier post about chatbots.  Most of the companies we talk to are looking at chatbots to enable some kind of customer self-help so that they have someplace to go to get answers before contacting help desk. Some companies are looking at chatbots to act as initial support for their technicians in the field. Here are some positives and negatives: 


Positives Negatives
Quick response time Do not work well on issues where in-depth consultation is required
Great for answering repetitive actions (ie “what are your service hours?” and common issues (ie “when will my spare part arrive?”) Requires some kind of coding or programming to set up
24/7 availability Does not learn from success or failure; changes must be re-programmed
Utilize AI in order to recognize language and text (NLP) Can be frustrating to customers if answers aren’t found
  Don’t use AI in order to learn from success and failures and therefore improve solutions


AI: There is a lot of hype around AI. When we attend field service conferences and get these “pre-spend reports” from the organizers, investing in AI is always near the top of the list of areas where service organizations plan to invest. There are more and more emerging companies that tout their solutions as “AI”. There is also a lot of hubris. Here is a list of what we see that’s out there in terms of potential solutions for service/support organizations that classify their offering as AI:

Data Scientists. For years, companies have approached AI as a data science exercise. Put all of your historical data into a data lake or warehouse. Hire a data scientist(s) to sift through the data and then come up with recommendations on how to change the business. My favorite use case is a fast food restaurant that used scientists to look through years of data in order to determine how best to avoid food waste. They looked at data from traffic patterns, weather, local sporting events, etc.  This is an expensive and time-consuming undertaking. 

AI + Data Science. These companies use data scientists to examine historical data from your machines and documentation (such as emails, support articles, etc) and look for patterns.  When tickets are created, the jobs are prioritized based on these patterns using their own algorithms. If parts need to be sent, they can also be optimized based on technician availability, location, etc. There are a dozen or so companies out there that offer some variant of this. Many have been around for some time.

Context-Driven AI. RevTwo Navigator takes a different approach from all of the above. We are not a knowledge base. We are a form of chatbot, but we utilize AI and issue context to provide guidance to technicians so they can resolve issues right the first time. As more and more technicians use it, the smarter it gets and the greater the benefit to the entire support team.

With RevTwo, you don’t need data scientists. You don’t need all of the overhead that comes with a knowledge base. RevTwo learns as it gains experience. No more searching. Within hours, you can have your technicians using it, without a huge dollar investment.

RevTwo Navigator is available for a free download and trial, for as many of your techs as you want. Click here to get started.