If you told an 8-year-old me that he’d grow up to loathe having conversations with robots, he would have said that was an impossibility. Like the flying car, chatting with machines was supposed to be a signal that yes, we are living in the future. But I’m all grown up now, and after five minutes on the phone with a chatbot, I’m desperate to pull the plug.

I’m not the only one.

Given the opportunity, customers prefer talking with real people over conversing with machines. According to research conducted by Salesforce, 38 percent of callers view the primary benefit of chatbots as connecting them faster to a real person. But if you’re waiting for that human connection, know that your estimated time on hold is getting longer. For most call center operators, the priority is to eliminate costly agents with tirelessly, efficient chatbots.

The irony? Due to a lack of innovation, a once futuristic tête-à-tête with a chatbot is beginning to feel old-fashioned and antiquated.

The future’s not what it used to be—but Enghouse Interactive may have a solution.

Enghouse Interactive enlivens rusty chatbots with IBM Data Science and AI Elite

You’d expect that Enghouse Interactive, a provider of contact center technology including chatbots, to be in lockstep with efforts to automate the human contact out of the call center. However, through a key partnership with the IBM Data Science and AI Elite team, the IBM premier team of AI consultants and innovators, Enghouse Interactive is developing an entirely new offering to reimagine call center automation with people at the center.

The future isn’t chatbots that stand-in for humans in every capacity. Rather, it’s AI assistants that enhance the abilities of agents and deliver an impactful customer experience to grow the business.

For Enghouse Interactive, call centers are not a cost center to automate and forget about. They are a growth engine that merits investment to accelerate growth and innovation throughout the organization.

To learn more about Enghouse’s vision and their engagement with the IBM Data Science and AI Elite, I sat down with Kevin Ming, Ph.D., director of growth and AI at Enghouse to discover how AI is driving new competitive opportunities in the contact center.

Let’s set the scene: Would you say Enghouse Interactive breaks with the conventional wisdom of the call center?

Kevin Ming: Traditionally, the contact center business has been viewed as a cost center. That has been true for at least the last decade, if not longer. Enghouse Interactive wanted to change that perspective.

We asked ourselves: what can we offer, not as a company, but as an industry to our customers?

When you think about it, the contact center really represents the voice of the customer. It represents the front line, where most companies gain direct access to their customers. The contact center should be viewed as the organization’s customer engagement hub – it’s where everything they talk about, call you on, text or email you about. It’s all there.

Rather than try to reduce the amount of interactions with our customers, we should be encouraging more interactions with them. There exist a lot of insights and information that can be extracted from those interactions. That was what motivated us to embark on this AI insights project.

What inspired Enghouse to knock at the doorstep of the IBM Data Science and AI Elite?

Kevin Ming: IBM offers the infrastructure and components we need to implement our vision. The two pieces are IBM Cloud and IBM Watson. We leverage IBM Cloud as the delivery platform for Enghouse’s cloud solution for our customers, and leverage IBM Watson to build functionality into our offering.

Truthfully, we didn’t already have many extra AI resources and capabilities to implement what we wanted to within AI Insights. At the time, we were talking to IBM about migrating everything to the IBM Cloud infrastructure. As a follow-on discussion, we talked about how we can leverage Watson AI and machine learning to drive our AI initiative.

That’s where the IBM Data Science and AI Elite team was brought in to help. To help us understand how we can leverage AI and machine learning on a general level, and specifically for use cases.

Can you walk us through an example use case?

Kevin Ming: The offering is called Enghouse AI Insights. We call it a practice, not a product. It is very dependent on the use case, industry, and the client.

In the use case of travel for example, some things [the CMO] may be looking for, include:

• Personalities of their customers
• Conversational tone of what they are asking about
• Sentiment around certain topics or brands

For a different vertical, for example, the automotive industry, their Chief Product Officer cares more about the product level: Predicting product defects or predicting the usage of products, and therefore, use that [insight] to plan around usage and logistics.

For the financial industry, it’s fraud detection.

For telecoms, it’s churn risk and how to reduce churn.

It varies depending on the vertical and demands of the customer.

What impact will infusing call centers with AI have on end-user agents?

Kevin Ming: With this offering, we want customers to speak more and to write more. Because the more they talk about, the better we understand them, and therefore the more insights we can garner.

From an agent level, the more customers can talk about what they are looking for, the more insights we can share, in turn, with the agents who interact with them to better help solve their problems.

Through more dialogue, we can understand what the customer is looking for and potentially offer new solutions and products that they might not have been seeking initially. For us it’s about driving growth in the call center and adding value to the customer, rather than cutting costs or contact time to increase efficiencies.

Data privacy is a top of mind issue these days—can you speak to any challenges your team overcame to protect client data?

Kevin Ming: A large portion of our customers are based in Europe, so with GDPR and legal regulations, we had to be very careful—and we had to get permission from our customers.

We want to be very sensitive and protective of our customers’ data—who, in turn, have their customers’ data to be mindful of. That’s why we ensured that we considered all relevant factors before we started the project.

Even after that, we made sure that all email data is scrubbed of all sensitive personal information. From a data and AI perspective, artificial intelligence is indifferent to what kind of personal information it has; it’s about identifying high-level patterns and trends. We wanted to ensure that any data that emerged from Enghouse Interactive is removed of any sensitive personal information to safeguard individuals’ privacy.

With the lessons learned from IBM, do you feel well equipped and confident to move forward in your AI aspirations?

Kevin Ming: Internally, we have a better understanding and grasp of how to leverage AI and machine learning. We currently don’t have an extensive data science team, but we are assembling one. In the meantime, we will be relying on and leveraging expertise from IBM services and data scientists to help onboard initial customers. Alongside IBM we will learn together through this process, to enable us to be even more self-reliant in the future.

What’s next for Enghouse and IBM?

Kevin Ming: This is definitely a long-term partnership.

It is win-win for both [Enghouse and IBM]. And a win for our customers. All the capabilities Watson can offer, we can leverage that to help our customers. In fact, it is a triple-win scenario.

Meet the Data Science and AI Elite Team and learn more about how key IBM partnerships and platforms propel forward the Journey to Data and AI.