Will Customer Service Leaders Become the Next “AI Strategists”?
June 14, 2024
Advanced tools are simplifying the utilization of AI technology in contact centers, enabling both IT teams and non-technical, line-of-business managers to unlock its full potential like never before.
Chris Miller, SVP of Solutions Consulting at Five9, delighted the audience with this scenario in a demo at the 2024 Five9 SKO. Picture this: you’re out to dinner with your spouse and another couple on a Saturday night. The conversation inevitably turns to what do you do for work and you say you manage the contact center for a local company. A week or so later, you and your spouse have dinner with a different couple and this time you say that you’re an “AI Strategist”. No change in your responsibilities or company. How did that happen? Read on.
An adoption curve for AI in enterprise communications is quickly taking shape. The earliest adopters appear most interested in improving discrete workflows, such as post-call summarization or targeted coaching. Five9 customer Truconnect is a notable example. More advanced deployments are transforming nearly every role in the contact center, from agent to supervisor to IT manager. The anticipated results, which we are already starting to see, will include: improved productivity, faster time to value, and higher ROI.
The Big Shift: AI Can Now Reason on Its Own
Natural Language Processing (NLP) and Natural Language Understanding (NLU) served as the underlying technologies for conversational AI before the advent of GenAI. This represented a huge step forward for the accuracy and effectiveness of self-service models, but still required considerable time, money, and resources to train and iterate. The promise of GenAI is that it can reason on its own, cutting the need for expensive and time-consuming tuning. Jonathan Rosenberg, our CTO at Five9, eloquently summarizes the 3 eras of AI evolution and how we got to where we are today in this blog post. The implication is that nearly all aspects of the contact center operation, not just discrete workflows, will undergo transformation. As Jonathan writes “almost no area of our industry will be unaffected.” Let’s examine the likely changes for Supervisors and Quality Assurance Managers, Agents and IT Managers.
Supervisors and Quality Assurance Managers
Traditionally, supervisors focused on ensuring appropriate staffing of queues to minimize wait times. Now they’ll enhance real-time coaching by configuring and updating AI-driven guidance cards, which will intelligently pop up during conversations to assist agents at the right moment. Further, Supervisors will configure AI-driven analytics to identify which interaction types can be automated, alleviating volume for agents and improving customer satisfaction.
Similarly, QA Managers will steer AI applications that help identify trending interaction topics that meet certain criteria for targeted coaching. And they will configure auto-scoring for other types of calls. This can all be done using Five9s GenAI Studio, Agent Assist, and AI Analytics tools.
Agents
In the past, Agents were burdened with the mundane task of summarizing conversations. Now they can immediately jump to the next call and review a well-written automated summary from the customer’s last contact with the company. Taking this example even further, AI will enable these conversation summaries to be automatically formatted into customer follow-up emails.
Agents will further benefit from more tailored coaching because QA managers can see which calls are rated high for sentiment and CSAT, illustrating high performance and cause for recognition, and which calls rate low for sentiment and CSAT, offering targeted coaching opportunities.
IT Managers
For IT managers, the shift will involve more oversight of GenAI applications, including integrations for data inputs, and away from manual tasks like ticket management and resolution. If a company deploys GenAI to help with real-time guidance and collects agent feedback on the effectiveness of the guidance, IT can help interpret the weaknesses in the algorithm and improve data inputs to generate more relevant responses.
In these examples alone, we’ve showed:
How supervisors and QA managers can improve agent productivity through relevant, targeted coaching.
How agent satisfaction and retention improve by removing the mundane task of writing notes after calls and instead giving them noticeably clear, readable summaries.
How IT shifts to the role of AI oversight.
No role is unaffected. And while customer service leaders enjoy a nice dinner with friends, they can now credibly describe their new role as “AI Strategists”.
