Your First AI Project: Avoid Customer-facing Chatbots

Your competitor has a shiny new AI-driven chatbot, so shouldn't you be developing one, too? Not necessarily. Here's why these customer-facing bots may not be the best first project for your AI program, and what you should choose instead.

Jessica Davis, Senior Editor

April 3, 2018

3 Min Read
<p>(Image: panuwat phimpha/Shutterstock)</p>

A lot of the buzz around artificial intelligence in enterprise organizations is going to customer-facing projects such as chatbots. After all, if your competitor has a chatbot, shouldn't you hurry up and get one, too?

But a new report from Forrester Research suggests that the focus on chatbots and other high-visibility, customer-facing AI projects may be premature. Instead, organizations may be better off focusing their current AI efforts on the tasks that AI is good at today, such as computation and search, and leave the customer interactions to the humans. Those humans, in turn, can access the organizations' data and information using AI such as search or bots to deliver a better experience to customers.

The approach will lead to greater productivity, because employees will not waste time on repetitive tasks, and better customer service, according to the report's author Craig Le Clair, VP and principal analyst serving enterprise architecture pros. In an interview with InformationWeek, Le Clair said that many employees spend about 15% of their days navigating legacy systems. But if a bot were set out to perform those tasks for the employees, productivity would rise.

"A lot of the conversation about AI has been influenced by the prevalence of Alexa and other chatbots," he said. "The real value for where we are today with the evolution of technology is not human to machine conversation. It's more support functions. That's where we should be focused."

Le Clair said that natural language processing and related technology is evolving, but it's still not where it needs to be. It needs to become more mature before it's mainstream, and that is another 5 to 7 years away. Meanwhile, humans continue to be best at conversation, semantics, and similar tasks. But that doesn't mean organizations should give up on NLP for now.

Organizations should think about AI and automation in terms of tasks rather than in terms of job replacement, according to the Forrester report, which offered the following three key takeaways:

  • Today's opportunity is really all about AI helping employees excel and increasing their productivity.

  • Machines excel at background tasks such as navigation and search, not human tasks such as conversations. That's why today's AI is best directed at internal employee support rather than customer-facing bots.

  • The most successful employee use cases mix machine and human tasks.

The report suggests that organizations divide the labor between humans and machines depending upon where each excels. Humans are best at conversational intelligence, collaboration, connecting ideas, and sentiment analysis.

On the other hand, machines excel at machine learning, multichannel context, statistical correlation, rapid search, content analysis, risk management, and repetitive task execution.

The best approach lets employees tap bots for support.

For instance, a financial advisor who deals with customers on a regular basis may spend time going out to locate a form on the Internet, helping a customer fill out a form, and loading form information into a database. These are repetitive tasks that don't make the best use of the human worker's strengths. Robotic process automation, an area that's gotten a lot of attention from enterprise organizations in the last year, according to Le Clair, can be designed to replicate these tasks. With these tasks removed from the human's to-do list, the human can focus on what humans are good at instead.

"There should be a greater focus on how AI can help employees to do their jobs beter, Le Clair said. "We are under utilizing the workforce now. Humans shouldn't be spending three hours a day buried in copying things from one spreadsheet to another."

About the Author(s)

Jessica Davis

Senior Editor

Jessica Davis is a Senior Editor at InformationWeek. She covers enterprise IT leadership, careers, artificial intelligence, data and analytics, and enterprise software. She has spent a career covering the intersection of business and technology. Follow her on twitter: @jessicadavis.

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