Capacity has reached $100 million in annual recurring revenue, a milestone the company shared at CCW 2026 alongside retail partner DSW, as both emphasized that trusted AI customer service starts with practical self-service and reliable human handoffs.
Karaline Venezia, Chief Revenue Officer at Capacity, and Tim Harpe, Director of Global Success at DSW, described how brands can move beyond AI hype by focusing on customer needs first. Their conversation showed that a virtual agent works best when it handles simple, high-volume tasks - and when a live agent is always within reach.
Conversational AI gains traction through practical use cases
Capacity's growth reflects a wider market shift. Enterprises want faster, more scalable service models, and they want automation that reduces friction rather than adding it. This practical approach to AI for Customer Support means starting with tasks like authentication or order status.
"For us, it comes down to what does the customer at the end of the day truly need from us, and how can we provide that," Venezia said. She stressed that not every brand or customer is the same, so a virtual agent must fit the specific journey and service model.
DSW began with those exact use cases. Harpe explained that giving customers quick self-service answers for order tracking and account verification frees agents to handle more complex issues. That kind of targeted automation builds confidence in AI without overpromising.
Live agent handoff protects customer trust
Harpe made clear that automation has limits. A virtual agent can tell a customer where a package is, but if the answer doesn't meet expectations, the brand needs another path. "If by chance it's not doing what the customer needs, how quickly can you get to a live agent? That, to me, is a critical component of the entire process," he said.
That handoff is not a fallback - it's a design choice. Customers are more willing to use self-service when they know a person is available. The best AI implementations don't trap people inside automation; they give fast answers when possible and connect to a human when needed.
Efficiency gains fund better experiences
Venezia argued that the real value of AI isn't just lower costs. It's deciding how to reinvest the efficiencies. "Then it becomes a success across the board… Because it's not just saving money, it's saving money, it's improving the customer experience, and it's improving the overall friction that happens within the business," she said.
Teams can redeploy people who previously handled routine queries into roles that improve the virtual agent, identify friction points, and design better journeys. That turns AI into a strategic tool for continuous CX improvement, not a one-time cost-cutting move.
Daily collaboration drives continuous improvement
Capacity's model relies on close, ongoing partnership. "I have a team that looks at results every day. They have a team that looks at results every day," Venezia said. "We work together every day on how do we make it better."
That daily review matters because customer expectations, products, and business priorities change. A virtual agent that isn't tuned regularly will drift. Venezia described the relationship as "built on two parties working towards the same exact goal," which sets expectations for enterprise buyers looking for more than just software.
Harpe's advice to CX leaders was direct: "Start something." He recommended choosing one useful customer need, proving success, and building from there. Once a team sees results, it's easier to expand into more complex self-service journeys.
Why this matters for customer support professionals
Customer support leaders can apply this approach immediately. Pick one high-volume, low-complexity task - order status, password reset, or account authentication - and deploy a virtual agent to handle it. Measure deflection rates and customer satisfaction, and make sure the path to a live agent is always visible and fast. Expand only after the first use case earns trust. This keeps automation focused on real customer needs, preserves the human connection, and builds a foundation for broader AI adoption without over-investing upfront.
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