The fastest way to separate AI hype from opportunity is to learn from organizations already putting it into practice. 

The session focused on measurable outcomes: how FOX, Everlane, Amazon Ring, and FuturHealth are deploying conversational AI agents, voice AI, proactive engagement strategies, and AI-powered workforce planning tools across use cases in streaming media, retail, telehealth, and consumer technology.  

Common threads ran throughout the discussions: the importance of grounding AI initiatives in real customer data, the need for continuous iteration after launch, the shift in how success is measured (moving beyond simple containment metrics to sentiment and implied satisfaction), and the reality that human oversight becomes more important, not less, as AI systems mature.  

Our partner presenters from Sierra, Kustomer, Vapi, and Assembled offered concrete frameworks, results, and lessons learned for CX and operations leaders looking to move from experimentation to measurable impact. 

Here’s a recap of the virtual event. 

Sierra + FOX: Building and launching an AI agent in 60 days 

FOX partnered with Sierra to build its AI customer service agent, timed to coincide with the launch of its Fox One subscription product. The AI wasn’t just answering FAQs; it was resetting passwords, updating billing information, and processing cancellations on customers’ behalf. That flexibility proved critical when contact volume spiked 430% in a single week during a major global sporting event.  

FOX’s philosophy: treat AI and human agents as one unified team, holding both to the same QA bar. The payoff has been a retention rate above 80%, driven by continuous iteration rather than a “set it and forget it” mindset. As FOX’s presenter put it, “It is never perfect, and the key to success is to ensure that we are constantly looking to improve.” 

Kustomer + Everlane: Getting ahead of the customer entirely 

Kustomer introduced a four-level maturity model for proactive AI (responsive, predictive, preventative, and orchestrated), arguing that each step up requires progressively richer customer context. To bring it to life, Kustomer walked through its work with Everlane, the sustainable apparel brand, which noticed that customers who returned their first purchase were far less likely to buy again. Everlane used that single signal (a customer’s first purchase) to trigger a simple automated outreach checking in on fit and sizing, reducing avoidable returns without a heavy data lift. The bigger lesson for CX leaders: you don’t need a perfect system to start. “Export the data, download it, drop it in an AI tool with a good prompt. That’s a perfectly valid way to explore a signal,” Kustomer’s presenter noted. 

Vapi + Amazon Ring: Solving the trust problem in voice AI 

Vapi’s team was candid about the uphill battle voice AI faces: “The last 20 years of CX have really broken trust for customers… it truly is until they experience it, they don’t believe it.” Their headline case study was Amazon Ring, which had evaluated roughly 40 voice AI vendors without success before finding Vapi’s platform flexible enough to meet its strict requirements around telephony integration and model choice. Ring went live in about two weeks and posted striking results: a 50% drop in average call time, a 70% cost reduction, and CSAT scores that matched or beat those of human agents. 

Assembled + FuturHealth: AI for what happens behind the scenes 

FuturHealth, a telehealth company that grew patient volume nearly 1,000% in its first year with a single operations leader and no dedicated data science team, partnered with Assembled for workforce planning. By connecting Assembled’s data platform to a general AI assistant, FutureHealth’s team could ask questions in plain language instead of writing code. Tasks that used to take days now happen in a single session, and FutureHealth proposed a simple adoption framework any operations leader could use: Ask, Act, Automate. Start by asking questions, then move to approval-based actions, and finally automate the repetitive work. 

In Summary 

Across all four use cases, a few themes kept surfacing. First, none of these companies started with a “cool AI idea,” they started with data on what customers actually needed. Second, launch is just the beginning; every case study described ongoing iteration, rather than a finished product. And maybe most importantly, the metrics are changing. Presenters repeatedly cautioned against leaning on old-school containment or deflection numbers alone, pointing instead to sentiment analysis and “implied CSAT” as better indicators of whether AI is actually helping, since, as FOX’s presenter noted, “folks who leave CSAT leave it when they’re negative. It’s just like an app review.” 

The overarching message: AI in CX is operational and measurable, and when grounded in real customer data and paired with human oversight, it delivers results companies can actually point to. For CX and operations leaders still in the experimentation phase, the advice from every presenter was the same: start small, start now, and let the data lead the way. 

Ready to see these AI use cases in action? 

The full session, including live demos, deeper dives into each case study, and audience Q&A, is available to watch on demand. Whether you’re just starting to explore AI in your CX operations or looking to benchmark against what these leaders have already built, this recording is worth the 90 minutes. 

Watch the full session on demand → 

The post How Four Companies Turned AI Into Measurable CX Results appeared first on Execs In The Know.

Uber decided to do something different.

The company’s Global Digital Experience team, the group sitting at the crossroads of customer support operations and engineering, started asking a harder question: what if you could infer how every single customer felt, even the ones who never filled out a form?

The answer became an artificial intelligence (AI) engine that analyzes the full universe of support interactions in real time, surfacing satisfaction signals that traditional surveys simply can’t see. No waiting for voluntary feedback, no sampling bias, and no blind spots.

The Three Measuring Levers 

The model is built around three pillars: Resolution, Effort, and Sentiment.

Resolution is the foundation. Did the customer’s problem actually get solved? Effort is the friction audit. How hard did the customer have to work to get there? And Sentiment is the hardest piece: tracking the emotional arc of an interaction from first message to final reply, measuring whether someone left feeling better or worse about the brand than when they arrived.

None of these is new in isolation. What’s new is the synthesis, such as weaving together transactional data, real-time trip telemetry, conversation logs, turn counts, and tone signals into a single, coherent picture of what a support experience actually felt like.

The Lessons Were Hard-Won

Building this wasn’t a clean sprint. Uber’s team quickly discovered that years of CSAT data had given them a false sense of understanding. Once they started peeling back layers to define more nuanced sub-metrics, they found complexity that legacy surveys had been quietly papering over all along.

Aligning stakeholders across different business lines, languages, and markets on one shared definition of “satisfaction” required iteration after iteration. Teaching an AI model not just that it failed, but why, within a specific cultural or operational context, turned out to be genuinely hard work.

It’s the kind of friction that only makes the output more valuable.

What’s Next for Digital Experience at Global Scale

The near-term unlock is significant: for the first time, Uber can compare performance across fundamentally different support technologies (legacy automation and modern conversational AI) using a normalized metric. Apples to oranges, finally made comparable.

But the longer-term vision is more ambitious. The team sees a future where AI doesn’t just measure satisfaction; it anticipates friction before customers feel it, resolves issues without a single click, and transforms every support interaction from a transaction into a trust-building moment.

That future isn’t fully here yet. But the infrastructure being built now is what makes it possible.

Want the full story?  Access the complete case study, including how Uber’s team structured the cross-functional build, what broke along the way, and how they see AI reshaping customer experience on a global scale.

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