Mining the Value of Data in the Contact Center With AI Use Cases

In a customer experience or contact center environment, organizations gather tons of data from customers, whether it's about problems they're having with organizational processes or issues they have with products. AI enables organizations to work through that data as fast as possible by minimizing human interaction.

In a recent CDW survey, 46% of respondents said their organizations have already implemented AI use cases for data analysis. And it seems that trend will continue, as 32% said they are currently implementing AI for that purpose. AI is enabling meaningful data analysis that paves the way for valuable innovation in customer experience.

Call centers are usually the first line of defense when it comes to managing incoming data, and they’re a critical part of diagnosing potential problems. Effective data analysis can help to quickly identify a problem with a particular product or manufacturer, and utilizing AI in real time to find these trends and spikes can equip your organization to pivot quickly and possibly stop a manufacturing problem or prevent a vendor from distributing a flawed product.

Data can also provide insights about sentiment on products. That's really important for marketing and sales teams. We can examine the sentiment of customers based on their interactions with a call center or based on their purchasing habits through a virtual agent or chatbot. This ability is innovating the contact center space as well; the opportunities with AI are endless.

AI has been around for a long time, but its adoption has increased dramatically over the past few years. To enable that kind of growth and innovation, an organization must be able to enlist the skills of data scientists.

There are a lot of unique skills that go into deploying AI solutions: You need business analysts to define what use cases you should pursue. There's the security aspect of AI, making sure you're deploying it in a safe manner. If you're developing virtual agents or chatbots, you need a conversation designer to make sure that your conversations are flowing in that human experience. And, critically, you need a data scientist’s input. But bringing together a team with all the necessary skill sets is a challenge.

Aside from the difficulty of building a complete team with the requisite skills, AI implementation can be tough because the technology is changing at such a frenetic pace. It's hard for coworkers and employees to stay current with the changes going on in the market. As quickly as we get up to speed on something like agentic AI, there’s a new application to consider. Organizations need to maintain focus to remain competitive.

In the near future, we’ll start to see more talent come to market in data science and AI. But for now, with the constant change and ongoing need for skilled workers, there's not enough talent to go around.

Regardless of the dearth of skilled data scientists, we're seeing accelerated adoption of AI use cases in customer experience because there have been so many unknowns in the space. For the past few years, much of that adoption has been through tech providers who have offered bolt-on solutions that require integration into contact centers.

Over the past year, however, most of the contact center platforms have developed AI solutions that are built-in, so you no longer need to have the bolt-on solutions. You can manage it from a single pane of glass because you now have one solution from one vendor.

Having everything within one platform reduces the number of employees required to manage your contact center technology. Whether it’s a Cisco, Zoom or Five9 solution, you now need just a few coworkers to manage a single platform instead of three bolt-on AI solutions. The consolidation that we're seeing in the market around AI and customer experience is going to continue to drive adoption throughout our space.

Consolidation is key to AI implementation, not only for the tech platforms being implemented but also for management and decision-making.

It starts with bringing the right stakeholders together — your experts from marketing, customer experience, data and IT — and making sure they are all on the same page. Sometimes, the best way to provide such a unified approach is through the assistance of an external services provider. According to CDW’s recent survey, many organizations rely on third-party experts for help with AI use cases: 41% of respondents have worked with a third party for assistance with data analysis, 33% have used third-party services to help develop and deploy chatbots, and 48% have enlisted third-party help with security.

CDW offers a service we call a strategic consulting engagement. We bring together all of the business units that are involved in a certain practice. For customer experience, we bring together agents, supervisors and marketing teams, and we do a mind dump of all the challenges customers face when interacting with the organization — and when we refer to customers, it's not always external customers; it could be internal customers as well.

With our strategic consulting engagement, we consider all of the challenges customers have, and then our digital strategists help solve for these use cases. We say, “Here's the problem that customer A has, and here's the outcome that we're looking for as an organization.”

The final step of this engagement is how to solve for that outcome. We identify the technology we're going to use, and then we prioritize all of the use cases to achieve the most ROI with the least complexity and effort. And through that process, we give customers a roadmap for the next six months, year or three years to make sure they're getting the most out of their investment and they're utilizing the right technology.