Featuring:
Christina McAllister, Senior Analyst
Show Notes:
Contact centers are major test beds for generative AI (genAI), and for good reason. Contact center work relies on the natural language and information retrieval capabilities that genAI is designed for, notes Senior Analyst Christina McAllister. This week on What It Means, McAllister discusses how genAI could transform contact centers and what leaders need to do to capitalize on its potential.
McAllister begins by noting three categories of opportunity for the contact center: customer-facing use cases (think genAI-powered chatbots); agent-facing use cases (such as retrieving information and summarizing calls); and harnessing call center analytics. Simple agent-facing use cases such as drafting answers to email support tickets and creating call summaries that are saved in a CRM system are already starting to show ROI, she says.
“Historically, that’s been 2–4 minutes of an agent tapping at a screen, trying to consolidate all of the notes that they have about the customer,” McCallister says. “[The summaries] are not consistent. They’re often not accurate. And it’s very, very expensive.”
To realize more of the transformational benefits of genAI in the contact center, companies need to address some fundamental shortcomings. These include upgrading old technology infrastructure and improving knowledge management systems — often, McAllister notes, agents need to toggle between multiple screens to resolve a customer issue. Contact center leaders will need to invest in agents’ and supervisors’ AIQ (their readiness to adapt, collaborate with, trust, and generate business results from AI) along with soft skills.
“Once we have generative AI supporting more of the simpler stuff … what is left over will be the trickier stuff, the exceptions, the stuff that requires more relationship building,” she says.
The episode concludes with McAllister’s advice on actions that contact center leaders should take and tech investments that they should make now to ready their organizations for success with genAI in the future. Understanding agents’ workflows and where their sticking points are, she says, could surface near-term opportunities for improvement.
