To build the ultimate AI-powered CX team, start here

To get a glimpse of the future of customer support, take a look at Manu Dwievedi’s journey. Ten years ago, he was a contact center agent fielding customer calls for Etech Global Services consultancy. Today, after the company sponsored his upskilling in machine learning and data science at MIT, Dwievedi is Etech’s senior director of AI. 

In fact, most of Etech’s roughly 500 AI analysts, engineers, and data scientists were, like Dwievedi, call center agents at one time, says Jim Iyoob, chief customer officer for Etech and author of “AI in Contact Centers: Boost Efficiency & CX.” 

“As automation came in, I began building a career path to bring data skills into the company,” he recalls. “I realized that to be successful in this space, your people have to have data literacy.”

When it comes to building a modern CX team, Iyoob’s instincts were prescient. In the age of AI, top-notch customer service is not just about having the right AI tools, it’s about having a team able to implement AI to deliver stellar customer support.

Research shows that assembling an AI-powered CX team can provide a key competitive advantage, with first-response times that once took over six hours now taking less than four minutes, according to Freshworks’ Benchmark Report 2025. Best-in-class teams help companies bridge the gap between savvy CX strategy and AI’s potential to improve customer experience.

“AI is not just a technology shift, but a fundamental change in how customers interact with you and how your customer service agents do their work,” says Venki Subramanian, Freshworks SVP of product management with a focus on CX. “The success of AI in customer support relies on a clear definition of the improved outcomes you want to drive and the ability to measure and optimize for these outcomes.”

A modern CX team’s roster

As customer support teams reimagine their organizational structure and job requirements in the age of AI, we’re in a period of transformation. Two short years ago, “prompt engineer” was the hot new job title, but as LLM models have become “smarter”—or more fluent in natural language processing—we have largely outgrown the need for humans to manually contextualize language prompts.

One thing AI continues to make clear: When it takes over routine tasks, employees are freed to take on more meaningful work. Companies using AI are well-positioned to uplevel their teams, elevating frontline agents into new roles where they can leverage their understanding of customer behavior and pain points—what makes customers tick—and use AI to improve their support operations with tools like self-service chatbots and AI copilots for agents. 

“AI will be a long transition of skills,” says Kate Leggett, VP and principal analyst at Forrester Research. “It’s not like you roll out AI and your whole contact center goes away. As AI takes on more low-level work, such as answering basic customer support queries, you will be reskilling your Tier 1 agents to manage the AI.”

Read also: How top customer support teams are thriving with AI

According to experts like Leggett and Iyoob, some of the critical new job roles that modern CX teams will need on their teams include:

Customer experience AI manager

This role is the team’s lead on AI strategy. With a 10,000-foot view of the customer lifecycle, they design specific initiatives aimed at enhancing customer experience using AI. They manage cross-functional project teams, ensure the CX team is achieving business outcomes in customer service delivery, and provide training and support to the rest of the CX team, helping employees from contact centers to the C-suite find their footing with the new technology.

CX bot manager

This team member is responsible for managing and continually refining customer-facing bots and AI copilots. Understanding customer needs and behaviors, they fine-tune the models to ensure customer interactions are efficient, engaging and reliable across support channels. 

“Just like you have a manager supervising the work of human agents, you are going to have one managing bots,” says Leggett. “This person is managing them to SLAs or parameters, and making sure that governance guardrails are in place so that the information your bot is giving customers is up-to-date and accurate.”

CX data analyst

Data analysts on the CX team sift through mountains of data gathered from customer interactions, translating the patterns they find in customer feedback and behavior, as well as AI and human agent performance, into actionable insights. They’re also the team members who ensure data quality and accuracy. 

“In the old days, they were called quality managers or quality auditors,” says Iyoob. “Today, they analyze data to find where there are support operations problems and tell you how to fix these problems.”

CX operations manager

These CX professionals are tasked with process strategy, optimization and implementation. They oversee the rollout of new AI tools and processes across existing platforms, fine-tuning them to ensure they continuously deliver quality support. Managers “own” customer-facing AI products and oversee their governance. 

“AI is not set-it-and-forget-it,” Iyoob notes. “It takes tuning like anything else. There’s a lot of work to make sure it’s operationally seamless across silos and delivering the outcomes you want.”

While these emerging job roles serve clearly defined functions on the modern CX team, say Iyoob and Leggett, there is no widespread consensus on job titles. A search of popular job sites Indeed or Zip Recruiter may surface a handful of positions called “bot manager,” while job titles like “quality assurance manager” or “AI control specialist” reflect the same function.

And, says Leggett, as AI agents arise, ones who are capable of multistep processes beyond the capabilities of today’s bots, the needs of CX teams are sure to evolve, too.

“Think about copilots,” Leggett says. “Those are AI tools that assist your customer service agent to know the next step in the process to answer a customer’s question. Now, imagine the flip side where an AI agent is doing most of the work, but the human is assisting the AI. That’s where we’re going.”