How Alcon turned customer service into a revenue-generating engine and converted first-time buyers with AI-powered guidance

A front line hiding in plain sight

For more than 75 years, Alcon has helped people see brilliantly. Becoming a standalone organization in 2019, Alcon is today the world’s largest eye care device company, serving surgeons, eye care professionals, and patients across surgical and vision care in more than 140 countries and territories. Behind those products sits a customer service and support organization that touches customers far more often than any other company function.

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That reach was almost entirely untapped. The organization operated on a serve-only model — answer the question, place the order, resolve the issue. The service quality program was manual, so the interaction review process underperformed and limited the organization's ability to consistently monitor service quality. Leadership asked a bigger question: could a contact center become a genuine value lever for the enterprise, not just a cost of doing business? Alcon partnered with Observe.AI to make that a reality.

“I’ve seen customer operations evolve from a transactional back-office function into a true value lever for the enterprise. We knew the opportunity was there; finding the path to realize it was the challenge. We believe we’ve found that path.”
- Bill Walker, Head of Americas Customer Operations, Alcon

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Seeing every conversation for the first time

Alcon started with the foundation: call recording, speech-to-text, sentiment analysis, and quality monitoring, giving every interaction a consistent, reliable data layer. Then it deployed Auto QA, and the change in scale was immediate. A service quality program that previously relied on a manual, underperforming interaction review process now evaluates 100% of its voice conversations, with an eye toward extending automated quality assurance across every channel.

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Complete coverage did more than close a blind spot. Alcon’s agents are strong to begin with; many are early-career hires or seasoned service professionals working in a demanding, highly technical medical-device environment, so the wins were incremental rather than dramatic. But seeing everything let the team pinpoint coaching at both the individual and organization-wide level, and QA scores improved after Auto QA went live.

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From “serve” to “serve, plus…”

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The deeper shift was in what a service interaction is for. Historically, the job was to transact with efficiency, fix what was broken, and educate the customer. Observe.AI’s real-time capabilities opened a second dimension on top of that — what Alcon calls “serve-plus.” The same conversation that resolves an issue can now, when appropriate, drive an incremental sale, educate a customer on an adjacent product, or build on share of voice.

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This isn’t instinctive for service professionals, and you can’t bolt it on by simply asking agents to remember more. The cognitive load of running a technical support conversation while also tracking a handful of commercial opportunities is more than any one person can reliably carry. Alcon believes transformation accelerates with tools that consume and contextualize customer data in the background, surfacing timely, relevant insights that enable the right actions at the right moment.

‍“We used to focus primarily on service. When customers presented a need, we answered a question, resolved an issue, or fixed a problem. Today, we have an opportunity to deliver Serve Plus. That ‘plus’ is where we create additional value for both our customers and the enterprise. It can be economic value through stronger engagement and growth, functional value through education that helps customers get the most from our products, or experiential value by making every interaction feel effortless, personal and memorable. These tools help provide the insights and context that enable us to deliver that higher level of service at scale.”
- Bill Walker, Head of Americas Customer Operations, Alcon

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Guidance, not dashboards

The engine behind Alcon’s revenue results is the Companion Agent, Observe.AI’s real-time agent assist. When a call arrives, the system checks a purpose-built database in the background to determine whether the customer is part of any active outreach campaign. If so, the Companion Agent presents the frontline team member with a targeted nudge naming the relevant campaign.

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The design principle that makes this work is deliberate: the nudge is guidance, not context. Rather than surfacing charts for a frontline team member to interpret mid-call, it tells them exactly what to do — flag that a customer is a prospect for a specific product, or note a concrete detail like a fall-off in a product last purchased several months ago and prompt them to ask why. The heavy analysis of identifying the right targets happens in advance and is prepackaged, so the real-time experience stays fast and simple. Campaigns are tightly targeted rather than one-size-fits-all: Alcon runs multiple targeted campaigns designed to provide relevant and timely customer engagement opportunities.

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“The message we put in front of them is simple. It’s meant to be guidance, not context, to tell them exactly what they need to do, not give them a bunch of graphs to decipher.” 
- Pat Brenner, Head of AI, Automation & Innovation, Global Customer Operations, Alcon

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Giving frontline teams their focus back

While frontline teams handle live conversations, Observe.AI handles the paperwork behind them. Auto Summary generates the post-call summary, selects the wrap code, and writes the disposition directly into the CRM automatically, so agents do little to no manual note-taking. In a regulated, technical environment, the reliability of that data matters as much as the time it saves: automated summaries are consistent across every interaction, eliminating the variability that comes when 120 agents each write up calls in their own way.

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Scaling with discipline

Integrating Observe.AI into Alcon's existing technology ecosystem went about as smoothly as this kind of technical work can. The rollout to 120 agents was equally deliberate. Alcon’s customer care organization is split into teams — Surgical, Vision Care, and others — each with its own queues, segments, and campaign-specific prompts. The team piloted with its key Surgical group, and once the approach proved effective, moved to a phased schedule of one team per week over roughly 12 to 15 weeks. That pacing let Alcon control training, confirm readiness, and tailor prompts team by team.

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It was a marked improvement over the pre-Observe.AI approach, which relied on a jury-rigged setup built around Salesforce’s native campaigns feature. That earlier version proved revenue was possible, but it was clunky—frontline teams had to manually check whether a customer belonged to a campaign, then remember to have the conversation, and preparing and managing target lists was cumbersome. Today that same workflow runs as a well-oiled machine.

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Proving a repeatable engine

The results arrived fast, and then they compounded. On day one of the agent-assist launch — after a friendly lunch bet between Alcon’s leaders over whether it would even work — the team closed a sale, without anyone trying to sell. More followed that first month, and the numbers grew from there. To date, Alcon attributes meaningful incremental revenue to the program, resulting in net-new customer adoption of products they had not previously purchased. Many program-sourced customers have already become repeat buyers, reinforcing the long-term value of the initiative.

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The most important proof came when Alcon introduced a second revenue-producing product with a different profile and cost structure. It performed remarkably well, reaching a target list of customers no one else was contacting — evidence that the results weren’t tied to one product but to a repeatable system. As the team put it, they had built the engine; now they simply need more to run through it.

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“Remember, we’re not trying to sell; we’re trying to inform. And on day one, we got a sale. That was ‘this is possible.’ It’s compounded ever since.”
-Pat Brenner, Head of AI, Automation & Innovation, Global Customer Operations, Alcon

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What’s next: Jarvis on every desktop

Everything Alcon has built so far lives in a single market, its largest, but only one of many. With 120 agents deployed here and roughly 550 across the global service and support organization, there is substantial room to scale, and interest is already spreading to adjacent functions including inside sales, tech services, and medical safety, with commercial teams taking note. Internally, Alcon calls its AI capabilities — and the Companion Agent in particular — “Jarvis,” and the team’s ambition is to put Jarvis on the desktop of everyone who interacts with a customer.

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Beyond revenue, leadership sees a broader prize: unlocking intelligence from the vast number of interactions handled across the organization, a gold mine of insight, given how much more often service touches customers than the field force does. Having proved the hypothesis first, Alcon now has the backbone others across the enterprise can build on.

“The enthusiasm across the organization has been remarkable because people see the potential. What began as a customer operations initiative is increasingly viewed as an enterprise capability, and we're excited about the opportunities to continue expanding it in ways that create value across the business.”
- Bill Walker, Head of Americas Customer Operations, Alcon

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