I spent this past week at the MaestrQA user conference with 200+ contact center and quality managers, where I’m on the board with SaaStr Fund. The attendee list read like a who’s who of both arguably more cutting-edge tech leaders—Brex, Oura Ring, Asana—and established enterprise leaders. Different industries, different stages of maturity, different technical sophistication.
These weren’t CEOs or founders talking their game. These were directors and managers of support dealing with their every day needs and headaches. And every single conversation, every roundtable, every hallway chat was about one thing: AI.
Not “should we adopt AI?” or “when will AI be ready?” Those questions are ancient history. The conversations were tactical, urgent, and deeply specific: How we implemented AI, what we are doing next. How do we implement this? How do we measure that? How do we manage the change? How do we stay ahead?
Sink or Swim, as Oura put it
Captain Obvious Learning #1: The Bleeding Edge vs. The Pragmatic Middle. Both, 100% All-In on AI.
At one end of the spectrum, I watched Oura’s team describe their approach to customer conversation analysis: “AI and The End of CSAT”.
They’re running four different LLM passes on every single customer interaction—not just for insights, but for comprehensive quality scoring. They’re so confident in their AI-driven quality metrics that they’re abandoning CSAT as their core measurement. Think about that: a company betting their customer experience strategy on AI analysis rather than traditional surveys.
At the other end, I sat with contact center leaders from more traditional enterprises who were still figuring out how far to push with their AI tools. But here’s what struck me: even more conservative adopters weren’t asking if. They were asking which. Which AI, how fast, and what their rollout timeline should look like. Often, for their next wave.
The conversation wasn’t about adoption—it was about implementation strategy. Even at the most conservative enterprise companies in the room of 200+ leaders.
Captain Obvious Learning #2: Every AI Implementation is a Snowflake. No Two Were The Same.
What became clear across dozens of conversations is that there’s no template for enterprise AI implementation. Every company is building something different because every company’s needs, data, viewpoints and constraints are different. Several leaders were deep on Decagon, MaestroQA, and custom implementations. Others were using older support tools with ChatGPT + MaestroQA. No one had the same “AI stack” in enterprise support.
Some were focused on real-time agent assistance. Others were building comprehensive conversation analytics platforms. All had some sort of AI-powered customer self-service, but how much varied. The tech leaders were building custom integrations with multiple LLMs. The more traditional companies were more focused on tuning vendor solutions.
But they were all moving. Fast.
The Change Management Reality: Yes, Folks Are Worried About Jobs
The most tense discussions weren’t about the technology—they were about people. How do you retrain quality managers whose jobs are being transformed by AI that can analyze 100% of conversations instead of the traditional 2-3% sample? How do you help agents adapt to AI assistants that know more about customer history than they do? How do you convince executives to trust AI-driven insights over gut instincts? Is it even possible in some cases?
Every leader I spoke with was wrestling with these questions. The technology is ready. The challenge is organizational change at speed.
If You’re Not Leading These Conversations, Someone Else Is
Here’s the reality that every B2B leader needs to understand: your customers are having these AI conversations whether you’re part of them or not. They’re evaluating AI tools, planning implementations, and making budget decisions around AI capabilities.
The question isn’t whether AI will transform how your customers operate—it already is. The question is whether you’re positioned as the guide helping them navigate that transformation, or whether you’re scrambling to catch up to conversations that are already happening without you.
The companies that will win are the ones helping their customers think through AI strategy, not just AI features. They’re the ones bringing frameworks for implementation, change management playbooks, and clear paths from pilot to scale.
Your Customers Want AI Leadership, Not AI Features
In every conversation I had, the subtext was the same: we need partners who understand this AI transformation as deeply as we do. We need vendors who can help us think through not just what’s possible, but what’s practical. We need solutions that don’t just add AI capabilities, but help us rethink our entire approach.
Your customers aren’t looking for vendors who’ve added AI features to existing products. They’re looking for partners who understand that AI is fundamentally changing how work gets done, and who can help them navigate that change successfully.
If you want your customers to see you as the winner, as the solution they can’t live without, you need to be leading these conversations. Because every enterprise conversation is an AI conversation now.
And every AI conversation is an opportunity to position yourself as indispensable.
