Customer service teams have long relied on coaching to maintain quality. Traditionally, that coaching came from supervisors listening to calls, reviewing scorecards, and giving feedback based on limited samples. As contact centers grew larger and customer expectations increased, those methods became harder to scale. Today, many companies are beginning to explore and test AI-based coaching tools as a way to improve training, and early indications suggest that meaningful results could be achieved within just a few months.

These tools are being designed to analyze call and chat transcripts across entire teams. Rather than reviewing full conversations at random, AI is being evaluated as a way to surface specific moments that matter most. It can examine how agents handle key situations, such as billing disputes or refund requests, and can compare emerging patterns with those seen in consistently high-performing agents. “Contact centers generate a huge amount of data every day, but the real challenge is knowing which parts of that data actually explain performance,” said the professional involved in shaping the early direction of these capabilities.

That professional is Tanvi Kopardekar, who has been closely involved in early-stage development and client discussions around AI-driven coaching approaches for contact center platforms. Her work has focused on understanding how large volumes of conversation data might eventually be translated into practical guidance for agents and coaches.

The intent behind the technology is still being refined and is not to replace human judgment. “The goal was never to replace coaches, but to help them focus on the moments in a conversation that truly affect customer satisfaction and resolution,” she explained. By studying how agents speak, respond, and guide conversations, initial system concepts aim to highlight potential strengths and gaps across teams, giving coaches clearer direction.

One of the key shifts being explored is moving away from one-size-fits-all training. Different teams face different challenges. “We observed that agents in different departments need very different skills, and treating everyone the same often slows improvement,” she said. An agent handling billing issues may require a different approach than someone focused on accounts or customer onboarding. The proposed AI-driven framework is intended to reflect those differences when recommending training.

Speed and focus are also central considerations in the client’s thinking. Instead of asking coaches to listen to entire calls, the goal under evaluation is to surface a short list of calls and specific transcript segments that may need attention. “Rather than reviewing full conversations, we wanted to surface the exact segments where behavior seemed to make a difference,” she noted. This approach is expected to make coaching sessions more targeted and less time-consuming.

In one internal initiative, the idea of generating training content from real examples was piloted. Transcripts from agents who consistently performed well in specific areas were analyzed as potential inputs for targeted learning material. “When training is grounded in real examples from strong performers, it becomes much easier for others to understand and apply,” she said.

Early adoption and feedback from pilot efforts suggested that these tools could remove several manual steps from the coaching process. Teams no longer needed to guess which calls to review or where issues might have started. In limited trial settings, organizations began to see early signs of improvement within roughly a few month. Customer satisfaction scores showed upward movement, trending toward the 4 to 4.5 range, and coaches were able to intervene earlier when performance indicators shifted.

The process, however, revealed several challenges. One of the hardest problems was identifying the true drivers behind low scores. Early analysis sometimes pointed to misleading signals. “One of the biggest lessons was learning to filter out false positives,” she added. “A metric might look like the problem, but the real issue often sits deeper in the conversation.” Addressing this required iterative correlation analysis to ensure the system focused on the most meaningful call segments.

This challenge reflects a broader issue across the industry. While AI can summarize conversations quickly, determining which insights actually matter is still evolving. “AI can surface summaries very fast, but deciding what truly drives outcomes still requires careful thinking,” she noted. Understanding how specific behaviors influence results is becoming an important focus as these systems mature.

The takeaway for many organizations is that AI coaching tools show the most promise when they reduce guesswork rather than add complexity. “The most meaningful improvements came when coaching was tied directly to outcomes, not just activity,” she said. As more companies experiment with and refine these tools, the emphasis will remain on clarity, relevance, and helping people improve faster, without add