Vasu Prathipati is CEO and cofounder of Maestro, though he likes to think of himself as the Chief Quality Ambassador.

Maestro’s mission is to help every customer build a culture of quality, doing so through their platform, QA Everything. QA Everything integrates directly with systems like Kustomer to help clients measure quality across their support workflows. They’ve been supporting 500 customers across industries like retail, banking, and insurance.

Vasu has great takes - often on LinkedIn - from his over decade of experience at the helm of Maestro. He shared some here during the CX Summit. Check them out:

 


This interview has been edited for clarity.

 

Gabe: Starting with the elephant in the room—why do you think traditional metrics like CSAT are starting to lose relevance for some companies?

Vasu: Great question. I have been working with CX teams and these metrics for about a decade, and honestly, I think many have always felt CSAT was insufficient. They just did not have better tools.

CSAT often reflects things outside of the agent’s control—product issues, policy limitations, or even fraud-related rejections. A customer might leave a poor score even if the agent did everything right. Same with AHT—it is nuanced. Short handle times are not always good, and long ones are not always bad.

That is why companies build quality programs—to look behind the metrics and understand what is really going on. CSAT can be a signal, but it should not be the final word.

Gabe: That makes a lot of sense. It is still useful—it just cannot be the only thing.

You have worked with a lot of customer-obsessed brands. What are the best ones doing differently when it comes to measuring quality?

Vasu: There are three main shifts I am seeing among the most innovative brands.

First, they are using AI to support quality programs—not to automate them entirely. Some companies want to let AI generate a complete QA score and call it done. But if you care about something, you do not fully automate it. You would not automate relationships with your kids or your partner. The best companies are using AI to flag risky interactions and identify patterns, while keeping humans in the loop for deeper analysis and coaching.

Second, they are thinking about quality holistically—not just in terms of agent performance. They are using quality data to understand issues across people, product, policy, and processes.

Third, they are shifting from transactional QA to journey-based QA. Instead of scoring isolated tickets, they are evaluating the full customer journey—especially churned users—and looking at how quality played a role across multiple interactions.

Gabe: I love that. That first one really stuck with me. You cannot automate what you care deeply about. You mentioned AI a few times already, and I want to double-click there.

Some people see AI as a threat to human judgment in QA. How do you see it playing a role today?

Vasu: It is a tool—and a powerful one. Yes, it can automate low-value work. But more importantly, it helps QA teams spend their time where it matters most.

With AI, you can move from random QA sampling to risk-based or opportunity-based QA. Instead of reviewing five random tickets and getting four password reset requests, AI helps you focus on interactions with potential quality concerns or coaching opportunities.

Also, it unlocks a new kind of skillset for QA professionals. We think of it as “conversational BI.” Traditional BI analysts turn numbers into dashboards. Quality analysts, with the help of AI and prompting, can now turn feelings into facts. It is a new level of strategic insight.

Gabe: That is such a smart reframe. I love that—conversational BI. Let’s zoom out again for a second. Not everyone may be familiar with how you define quality in this context. How do you think about it more broadly?

Vasu: Great question. In industries like healthcare, we actually see Chief Quality Officers because the stakes are so high. But that mindset is relevant across industries.

We work with companies in insurance, tech, and ecommerce that are QA-ing more than just their support teams. They are scoring claims adjusters, underwriters, sales reps—even managers themselves.

One of our clients, DraftKings, even runs QA on coaching sessions. They want to ensure managers are developing their teams effectively. So when I say “quality,” I mean a culture of attention, reflection, and continuous improvement across every part of the customer journey—not just support.

Gabe: That is a helpful framing. Let’s talk about scaling QA programs. A lot of teams struggle with consistency—especially when juggling BPOs, in-house agents, and remote teams. How do you help customers build QA programs that actually scale?

Vasu: First, it is not a technology problem—it is a leadership challenge. Too many leaders want to be innovative with AI, but they are afraid to rip out the old. They want to digitize a newspaper instead of building a true digital product. That does not get you to the future. Take randomized QA scores, for example. We have a slogan: “Death of the QA score.” It is outdated. Leaders need to be willing to rethink quality from the ground up using the new tools available. That means letting go of some deeply ingrained habits.

Gabe: That is a great way to put it. People want both worlds, but sometimes you have to rip the Band-Aid off. Coaching is another area that feels either transformational or like a check-the-box exercise. What separates the two?

Vasu: Right now, most coaching programs are a mess. Companies often do not even know if it is happening—especially with BPOs. When they finally get visibility, it is disappointing. Either it is not happening, or it is ineffective.

Great coaching should be behavior-based, not just “improve your CSAT.” You want to develop skills, not just chase metrics. AI can help surface behavioral patterns and guide better coaching conversations.

And again, companies like DraftKings are even QA-ing the coaching sessions themselves. They are using large language models to analyze how managers coach their reps. That is next-level performance management.

Gabe:  That is powerful. OK, last question before we wrap. Many CX leaders today are overwhelmed. Budgets are tight, AI is moving fast, and teams are under pressure. What advice would you leave them with?

Vasu: First, avoid the easy-button promises. Many vendors claim “instant AI tagging” or “automated QA in a few clicks,” but 98% of the time, it does not work as promised. This leads to misaligned expectations and frustrated leadership. So my advice: bridge the knowledge gap. Bring leaders into conversations where they can really learn what AI can and cannot do. Bring in people like myself or Gabe. Talk to practitioners who are doing the work.

Second, lean into prompt engineering. It is going to become a key skill in CX and QA. Learning how to guide LLMs and use AI thoughtfully will give you an edge—not just as a tech user, but as a strategic leader.

Closing thoughts

Vasu Prathipati emphasizes that the true power of AI in customer service lies far beyond simple automation. Forward-thinking CX leaders are using AI to uncover behavioral insights, guide coaching, and build smarter, risk-based quality programs—not just automate scorecards. AI becomes a strategic partner, helping teams prioritize what matters, uplevel performance, and turn conversations into meaningful data. The future of customer service is not about replacing humans, but empowering them with better, more insightful tools.

Ready to take the next step in your brand’s customer experience with AI? Check out our guide on how to best leverage AI Agents so you can get ready to implement them for your brand.

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