Practical Implications of AI in Contact Centers

Like it or not, artificial intelligence (AI) is here to stay. As is the case with many innovations, it can be used in a positive way to improve the world (or a company or department, such as a contact center), or it can be used with bad intentions. So, let's take a look at some of the concerns contact centers face as they begin their AI journeys.

Concern #1: AI will eliminate the need for live contact center agents.

Reality: AI-enabled self-service solutions, including bots, are intended to reduce the volume of inquiries that need to be handled by live agents and to provide support when agents are not available. Externally, customer-facing intelligent virtual agents (IVAs) should be able to automate many of the repetitive and less-complex inquiries that do not require agents' deep analytical capabilities. Internally, employee-facing virtual assistants (VAs) can reduce the amount of rote and lower-value work agents perform, such as copying and pasting information from one system to another or kicking off an automated workflow.

These solutions can also act as advisors to agents, automatically retrieving context-based knowledge articles or procedures to enable them to do their jobs correctly and efficiently.

Intelligent automation should minimize the time agents need to spend completing many tasks, giving them more bandwidth to dedicate to problem-solving. Real-time guidance (RTG) applications take it to the next level by listening to (or reading) both sides of conversations and providing tactical suggestions and best practices to assist salespeople and collectors comply with regulations and handle objections.

Then there are after-interaction summarization applications, which are designed to automate much of the wrap-up process, minimizing the time agents dedicate to this essential but time-consuming activity.

While these and other AI-enabled solutions should reduce the need for live agents to engage in tasks and activities that do not require critical thinking or human reasoning, they do not eliminate these roles completely. In most situations, AI is not driving an exodus of agents; instead, it positions companies to scale their service and contact center departments to meet increasing customer needs. AI enables organizations to fill empty seats as they grow, add digital support channels, and expand their business.

Here's another way to think about it: Contact centers have always had a very large agent attrition problem, and it has reached new heights in the past few years, at the same time it became even more difficult to hire new employees. AI is enabling contact center managers to automate inquiries that agents find less interesting to handle. AI is also empowering agents with the guidance and information they need to accurately resolve issues at the point of contact, which improves the customer experience (CX), employee experience (EX), and productivity. This makes it a win for the customer, agent, contact center, and enterprise.

Concern #2: AI might give wrong, inappropriate, or dangerous answers and recommendations to customers.

Reality: This is another fair concern and one that can be prevented by using industry best practices. AI initiatives depend on a large data repository from which they can identify appropriate trends and patterns to formulate responses. The larger the data repository the better, as it increases the probability of finding a pattern that lends itself to an accurate response.

But this is also a challenge; large repositories also likely contain totally irrelevant or incorrect information that could lead to wrong or misleading responses or information. For these reasons, DMG recommends that companies use an AI data repository that is targeted, tagged, and curated for each specific use case.

The data used to seed an AI repository should be limited to sources that are relevant to the business purpose. If, for example, a company is creating an IVA to answer banking questions, the data accessed by the AI-based application should be from banks with similar products and services. Additionally, the AI-enabled solution needs to review and cleanse the data to ensure that there is nothing inappropriate in the repository. (It's increasingly a best practice to have this function performed by the system and overseen by a human data manager.) And of course, the company needs a process for growing and expanding its data repository to keep it current, but again, any information added needs to be properly handled and curated before it is included.

The future of contact centers is predictive analytics, which is AI. Contact center technology will be run by an AI brain, a predictive analytics engine that decides when and how its various systems and components are initiated, what they do, and how they interoperate with each other and humans. This does not mean that contact centers will no longer require managers, supervisors, agents, operations managers, project managers, data specialists, etc.. They will. Instead, there needs to be a balance between the automated and live support delivered by these technically sophisticated and intelligent AI-enabled operating environments.

Donna Fluss, president of DMG Consulting, is an expert on contact centers, analytics, and back-office technology. She has 30 years of experience helping organizations build contact centers and back-office operating environments and assisting vendors to deliver competitive solutions. She can be reached at