CCO Says the Success of Your AI Integration Depends on Your North Star
Teresa Anania explains why successful AI integration in CX starts with clear outcomes, measurable impact, and the right balance of AI and human expertise.
Teresa Anania
Chief Customer Officer at Verint
Outcome focus: AI integration succeeds when CX leaders define measurable customer and business outcomes before choosing technologies.
Production first: Starting AI projects in production with narrow workflows reveals real impact faster than sandbox experiments.
Human judgment: AI handles repetitive service work, while people lead strategic reviews, escalations, renewals, and executive relationships.
Measured gains: Verint reports higher satisfaction, stronger productivity, deeper adoption, and better support conversations after applying AI across CX workflows.
Employee experience: Reducing repetitive work helps employees focus on complex customer needs, strengthening service quality alongside operational efficiency.
Teresa Anania is a seasoned CCO, currently holding the role at Verint, where she is integrating AI across CX workflows.
We caught up with her to find out where she's getting the best results. She told us that a CX leader's clarity on desired outcomes is the determining factor in AI success.
The entire post-sale customer journey
A customer arrives with a question or a problem. Then, the intelligent virtual agent provides contextual, relevant answers quickly and in the right channel. If that’s not enough, all the conversation context travels to the human agent. No starting over and no lost context. This frees up engineers to focus on advanced, complex issues requiring human depth.
My background is deeply rooted in customer experience. I currently lead the CX organization here at Verint as Chief Customer Officer. Before this, I was CCO at Sophos (another Thoma Bravo company), Zendesk, and Autodesk.
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The CX organization I lead at Verint serves an incredibly broad customer base, including 85 of the Fortune 100 companies. This includes enterprise brands across financial services, healthcare, retail, insurance, telco, government, and more. Our customers range from large global enterprises running complex, high-volume contact centers to mid-market and small companies scaling quickly.
As CCO, my remit spans the entire post-sale customer journey. That includes product support, professional services, customer success, and renewals. We help customers achieve and exceed the outcomes they expected when they chose Verint, from initial deployment through adoption, impact measurement, and renewal.
Beyond retention, I also focus on helping customers continuously unlock more value from the Verint platform and demonstrate real, measurable outcomes from their investment.
Where AI can be leveraged in CX
I established a CX Center of Excellence focused on AI automation.
In professional services, AI surfaces recommended workflows for complex integrations. In renewals, it powers auto-quoting and scenario modeling, allowing customers to see the value of different solution sets. In customer success, our AI-assisted QBRs help the team present crisp, data-backed information and guide customers with best practices to achieve even greater quantifiable ROI.
On the support side, a digital agent on our customer portal handles tier-0 inquiries. A customer arrives with a question or a problem. Then, the intelligent virtual agent provides contextual, relevant answers quickly and in the right channel. If that's not enough, all the conversation context travels to the human agent. No starting over and no lost context. This frees up engineers to focus on advanced, complex issues requiring human depth.
We also extended agent-assist and intelligent routing across the team and automated quality assurance for 100% participation with real-time, self-serve coaching instead of manual review cycles.
The through-line remains consistent: stronger, faster, measurable outcomes.
Where to use AI vs. the human touch in CX
So, AI handles a lot. But humans are essential for delivering tailored QBRs, leading roadmap reviews, and achieving shared goals. Humans also engage in renewal conversations and critical support escalations. AI powers customer contextual insights around account health, sentiment, and benchmarking, but humans deliver thought leadership, best practice guidance, and deep executive relationships across our accounts. We do not automate this.
We also run a tech-touch program for our long-tail customers, using digital nurture and behavioral data to meet them where they are. We then layer in pooled CSMs when data shows a customer is struggling with onboarding or key-capability adoption.
You cannot have great customer experience without focusing on great employee experience. I have always said EX = CX. When your people love their work because AI handles the noise, they show up better for customers.
Why good AI results require clear outcome alignment
The results of integrating AI into our CX activities have exceeded my initial expectations in most cases.
We see measurable improvement in the number of simple use cases our agent solves, while our CSAT and tNPS scores also climb. Our support engineers handle more advanced issues more deeply because AI absorbs high-volume, repetitive inquiries. That shift alone has changed the quality of the customer conversation, not just its speed.
We also see meaningful productivity gains for our CSMs and renewal teams. AI-assisted QBRs are more relevant, with sharper value-delivered metrics; we model renewal scenarios faster; and our tech-touch program helps our smallest customers adopt more deeply in previously unscalable ways.
Results like these, however, require rigor. When AI underdelivers, we almost always trace it back to launching without clear outcome alignment and measurement from the start.
Why organizations should build solutions in production environments
…the most important advice I can give: Cut through the AI hype by starting with outcomes. CX leaders face immense pressure right now to move fast, try things, and adapt. But if you create a deflection strategy that reduces inbound tickets by thousands while quietly diluting your CSAT, you’ve achieved nothing.
I often see organizations drawn to AI by the technology's promises and then building solutions in a sandbox. But success there doesn't always translate into production.
That's why we start in production — with a small use case or a subset of workflows, and with the outcome in mind. We align on the outcome up front, integrate measurement into everything, and optimize continuously.
That way, you always have a North Star. You're not chasing the implementation of cool tech, you're proving impact. Internally, the rigor around deployment quality and outcome measurement makes the difference.
Why CX leaders must cut through the AI hype
In fact, I'd say that's the most important advice I can give: Cut through the AI hype by starting with outcomes.
CX leaders face immense pressure right now to move fast, try things, and adapt. But if you create a deflection strategy that reduces inbound tickets by thousands while quietly diluting your CSAT, you've achieved nothing.
Follow along
You can keep up with Teresa Anania on LinkedIn.
More expert interviews to come on The CX Lead!
