— MS Partner Apps (@MSPartnerApps) May 21, 2019
We just finished our first experience with exhibiting at Field Service USA. It was a great experience and we met a lot of companies interested in our technology.
We showed a demo at our booth of one of the ways to use our AI. For an unconnected device, we install a special script which creates a dynamic QR code showing the status of the device at any point in time. We call this an “Issue Fingerprint”. When there’s a service event, the user selects a function on the device that displays the QR code, then scans the code with a mobile device and submits it to the AI in the cloud, which returns a root cause and solution to the mobile device. You can see a simple demo of how this works here.
Listen to RevTwo, Momenta, and Senseye discuss how Artificial Intelligence (AI) and Machine Learning technologies are accelerating the evolution of next-generation solutions in the industrial IoT sector. In our complimentary webinar, we explore the “state of the union” around AI – the investment climate and key developments in research and business applications. Watch the free recording!
I was talking to a customer last week about the workflows they use when a customer contacts the support organization for service.
The most common workflow I see is something like this: customer calls the help desk, the agent asks a bunch of questions, and if they can’t resolve the issue then the agent gets a specialist or an engineer involved. They may open a remote access session to connect to the device (IF it’s connected…) If the problem still can’t be resolved, then they dispatch a field service engineer to the customer. While onsite, the engineer either triages and resolves the issue on their own or they talk to their own help desk to help get things sorted out.
This particular customer said “we don’t necessarily do that. If the help desk can’t resolve the issue, then we ask the customer to send us a set of log files for us to analyze.” Then if they can identify an issue through the examination of these files, they dispatch the engineer.
Going through log files is time-consuming and complicated. You have to look at the files sent to you by the customer when there’s an issue and then you have to compare those files with other log files that represent a healthy machine.
Last week I attended this annual event where there were approximately 100 field service executives from medical device companies. The event was 2 ½ days long and included interactive sessions on everything from remote service to retaining service techs (a bigger issue than you’d think!).
From my perspective, these were the main IoT topics that dominated the event and my thoughts on them:
I read this article in IoT World about connected health trends to keep an eye on for 2019. It talks about use cases for AI such as wearables, patient dashboards, surgical robots. It talks about some VS guy who a few years ago said that AI can do 80% of what doctors can do and that radiologists would be obsolete by 2022!
The overall progress of AI adoption in healthcare has been really slow. It seems to me that so many companies are trying to develop AI solutions for applications and use cases that are just too complicated or rely on an ecosystem of other technologies to make them happen.
We need to stick to the basics. No tech project or initiative will be successful unless there’s a business case to drive it. And in healthcare, the obvious business case for AI starts where IoT did: service and support.
Companies trying to deploy an IoT technology so they can remotely support their devices in the field are certainly still facing challenges today. More and more of their end users, particularly overseas, are resisting their attempts to connect to their devices on their network because of security concerns. But the business case for remote service and support still is strong. Reducing downtime, reducing 2nd service calls, improving MTTR and FTF rates still translate right to the bottom line for OEM’s and their customers.
So you’re thinking about trying out RevTwo’s AI technology? We are currently working with several potential customers on proving out our technology for them and would love to work with you.
IDENTIFY KEY ISSUES
To get started, talk to your service and support teams and identify 3-5 common service/support use cases that usually result in having to send a technician onsite to resolve it.
Sometimes our customers may struggle to come up with the right issues they want to address. One way to get your team thinking is to consider issues with one of these characteristics:
-An issue that happens often
-Something that was expensive when it happened
-Something that everyone in the organization would know about
It also doesn’t have to be something cut and dried like “the water pump failed”. Most complex issues are not that straight forward and usually have multiple root causes and symptoms. For example, it could be “my system is running slowly”.
When we talk to customers, we are often asked how we are different from other AI solutions out there.
AI solutions have been around for a while. If you’ve ever gone to a website and needed support for a product that you purchased, when you select the chat feature, you’re talking to a chatbot, not a human. A chatbot employs AI that takes the text you’ve typed in and matches it to a knowledge base and then asks more questions until it gives you an answer. This use case is mostly used in B2C businesses.
AI isn’t hard. It can help you today.
I am reading more and more about how Artificial Intelligence (AI for those of you who have not been online in awhile) is going to change the world. And soon, like the end of next year, according to this article.
But in my opinion, when people think of things like AI, they think of things like chatbots, or in-app support. Indeed, these tools can perform many useful functions that humans once had to do. But chatbots can only help with simple issues. If the issue is complex, then humans have to get involved.