As Chief Experience Officer (CXO), Lou Senko leads Q2’s team delivering an enhanced customer experience.
If you lead an operations or service organization right now, you are being sold the same promise I was. Adopt AI and productivity will follow. Buy the tools, drive adoption, watch the numbers move. It’s a seductive story, but it’s mostly wrong.
The productivity AI promises doesn’t come from the software. It comes from redesigning how the work is done and raising what your people are capable of, with AI as an amplifier on top of a system that already works. Get those two things out of order and you’ll buy a lot of technology and move very few outcomes.
Our transformation didn’t start with a strategy deck. It started with our customers telling us that we had to be better. When the people who depend on your products and services are that clear, you’d better listen.
The uncomfortable realization was that our old way of working was familiar and, for years, good enough. But that’s the trap every established team faces. Choosing to change, deliberately and at the level of how people actually work, is the hardest decision an operating leader makes. Here’s how we did it.
Rebuilding Workflows
We reorganized into small, skill-aligned pods where a specialist owns a case from first touch to resolution, ending the handoffs and transfers that consumed our time and frustrated customers. We stood up a function whose entire job is to surface the highest-severity issues in real time, protecting our premium response commitments and keeping the escalation path clear and fast.
We built a reliability engineering practice to fix root causes and stabilize the platform upstream so problems are prevented before they become a customer case. And we rebuilt how work moves with structured intake, smarter location-aware routing and clear prioritization so each issue reaches the right expert quickly.
Rebuilding The Team
We created competency models at every level so expectations were explicit, fair and forward-looking. You can’t develop people against a standard that doesn’t exist. We invested in our managers before asking anyone else to change, focusing on coaching, prioritization and outcomes.
We made performance visible and consistent across teams and paired accountability with real development support rather than pressure alone. Through upskilling and the ownership model, our specialists expanded what they can handle. Many now cover roughly double the range of case types they did before.
Rebuilding How We Measure
We now measure throughput, quality, flow health and speed together for a fuller and fairer picture than any single number—at the team, squad and individual level. We moved to per-interaction satisfaction, effort and account-health measures so we hear the customer continually rather than occasionally. We stopped rewarding tool usage and started measuring banked results and customer outcomes.
Adding The AI Amplifier
We use AI research and diagnostic assistance to speed the time-to-answer and to spot and prevent emerging issues before they spread. We use AI-assisted quality review against a clear rubric, and AI-accelerated onboarding to shorten time-to-competency. And we are consolidating these functions into one assistant that comes to the specialists in their workflow, supported by a governance layer that keeps automation accountable as it scales.
Lessons And Results
I think we underestimate how genuinely painful it is for people when you change how they work. When you change processes a team or an organization has relied on for years, doubt and skepticism are the natural responses, even when everyone can see the old way wasn’t working.
We decided early not to treat that skepticism as an obstacle to overpower, but as trust we had to earn. We listened before we moved. We developed and redeployed people rather than simply cutting. We were transparent about what was working and what wasn’t.
The results are early, but they’re showing up exactly where we were failing before: with the customer. Our customer-satisfaction scores are at the highest levels we have ever recorded.
The Financial Institution Perspective
If you lead a financial institution, you are on the other side of this equation. You buy the platforms and the service that sit behind the experience you deliver to your account holders and members, and there’s a lot of AI noise in the vendor landscape right now. You can use the lessons we’ve learned as a short list of what to demand from anyone who asks for that trust.
Ask what changed, not what was bought.
A partner who bolts AI onto a broken process just gives you faster access to the same friction.
Judge AI by your outcomes, not their adoption.
Seat counts, logins and “hours saved” are activity. The real measure is whether resolution, reliability and the experience you deliver to your account holders and members improved.
The people behind your service are your risk, too.
A partner that treats their workforce as line items in an efficiency story will hand you that instability as churn, inconsistency and slower answers when it counts.
Prize prevention over response.
Ask what your partner does upstream so their problems are solved before they become your incident.
The Work Isn’t Finished, That’s The Point
I won’t pretend we’re done. We’ve built the foundation and the momentum, and the next chapter, embedding AI deeper into how we run the business and continuing to grow our people, is where the biggest gains still are. But we know something now we didn’t when we started. Transformation in the AI era is not a technology program. It is a people program that technology makes faster.
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