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Chatbots and IIoT: What could they possibly have in common?

The traditional chatbot isn’t much better than being stuck in phone tree hell. This is because they are created using a laborious coding process based on extensive decision trees that attempt to mimic every possible user interaction. They provide a stilted experience based on “if-then-else” logic that doesn’t follow a natural conversation.

Then you have the ever-trendy industrial internet of things, which is based on sensor data pulled from machines and equipment. It covers all sorts of cool use cases from medical devices to industrial assets. As IoT continues to evolve, it will have even bigger business ramifications across virtually every industry.

Now, think about these two technologies from another perspective — what if the chatbot was actually intelligent and conversational? What if you could pair a conversational chatbot with the plethora of sensor data from IIoT to help manage a response or activity based on a predictive outcome?

Applications of chatbots and IIoT

Can’t quite picture it? Let’s look at a practical example. Say the network operations center at a large telecom provider gets a notification that an IIoT-connected HVAC system in one of its 4G base stations is predicted to fail within the next 90 minutes in Texas. It’s the middle of the summer and the temperature is 105 degrees Fahrenheit. At that temperature, the tower’s equipment room will overheat in less than an hour once the HVAC stops.

The automated reset didn’t work to clear the alarms, so a field technician is dispatched after receiving an alert via his mobile device. The tech gets the request and is redirected to the location, which is 60 minutes away. This leaves him only a small window to fix the problem. To save time on site, he uses a voice-activated interaction with the chatbot while driving to get a full briefing on the situation, a summary of which parts are required to make the repair and a step-by-step overview of the repair procedure. Once he arrives, he puts on his smart glasses (complete with augmented reality and integrated chat with the chatbot) to make the repairs.

In this scenario, the intelligent chatbot unlocks a new way for the field technician to operate. Instead of lugging around hefty manuals or tying up other support agents on the phone, the tech can interact with a chatbot to get the step-by-step information he needs. The chatbot can provide information in a more natural context while improving the self-sufficiency of the field agent.

It doesn’t take much to see how this could be applied to other fields as well. Look at healthcare — there is all sorts of patient data streaming in from embedded devices or wearables. Say this data is analyzed and triggers a notification to the patient upon discovering an abnormal reading. The first line of response in addition to automated alerts could be a triggered interaction with an intuitive chatbot that engages the patient to answer questions or schedule a doctor’s appointment.

AI and machine learning: The cornerstones of the future

AI and machine learning form the foundation of this vision. Just as we need to avoid traditional chatbot development approaches to create truly conversational chatbots, we have to do the same for the development and training of the analytical models.

For analytical models, we have to break the dependency on developing models that require intensive labor — there is too much data being generated to analyze it all manually at the scale required by today’s digital economy. We have to use AI to automate the machine learning process. AI is the only way to create predictive models that produce accurate results on an asset-by-asset level and at the speed required to keep up with a fast-moving production environment.

Similarly, AI is also pivotal to creating the conversational chatbot that doesn’t doom users to unnatural conversations and phone tree hell. AI enables businesses to make chatbot communications conversational and intuitive without intensive programming effort. How, you ask? By eliminating the need for decision trees and replacing it with a declarative AI-powered chat experience.

With AI, we can train chatbots based on the capabilities that we want the chatbot to provide instead of taking the time-consuming approach of writing code that replicates every possible chat response. Whether you’re providing support to a field tech or customer service to a patient, you can use FAQ-style information and documentation to train the bot. You can also train the bot to collect key information, like part numbers for the field tech or appointment times for a patient. You basically train the bot as you would train an actual employee.

This all might sound futuristic, but it’s not. This challenge is what drove the bold vision for AI-powered platforms like Progress NativeChat, used for creating your own conversational chatbot. AI and machine learning can do a lot of heavy lifting for businesses when applied correctly, enabling them to unlock new capabilities and capitalize on new opportunities.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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