General Discussion, page-5286

Customer support market huge

Customer support is an unglamorous but essential function for any business. It is also an enormous market: the global market size for contact centers (a useful proxy) was an estimated $332 billion in 2023, projected to grow to over $500 billion by 2030.

In many ways, customer support represents an archetypal end market for AI agents. It is a standardized, formulaic activity in which most types of customer requests (say, help with a forgotten password) occur over and over. And as noted above, it includes a “natural human in the loop”—the customer herself and/or a customer support manager—who can provide oversight and signoff before any high-stakes action is finalized.

For these reasons, customer support is one of the first areas in which agents are already in production and creating real value for enterprises today.

Fintech unicorn Klarna is a case in point. Earlier this year, Klarna announced that it had deployed an AI assistant powered by OpenAI to automate its customer service engagements. According to the company, this AI assistant has been able to handle two-thirds of all customer service requests (2.3 million conversations in its first month alone), automating the work of 700 full-time human reps and driving an estimated $40 million in added profit for the company this year.

A number of young startups has emerged to build AI customer support agents.

The most high-profile and well-capitalized of these startups is Sierra, which has raised over $100 million to date from blue-chip venture capital firms Benchmark and Sequoia. What sets Sierra apart? Its world-class founding team. Sierra CEO/cofounder Bret Taylor—former Salesforce co-CEO, former Facebook CTO, former board chairman at Twitter and current board chairman at OpenAI—is one of the most admired technology executives in the world.

Sierra’s AI customer support agents can respond in real-time to customer queries; retrieve all necessary customer information by integrating with internal systems and calling the appropriate APIs; and take action when needed to satisfy a customer request (say, updating a customer’s address or canceling an international data plan).

Sierra plans to price its agents based on work completed rather than the more conventional software subscription model. As discussed above, this notion of charging for work rather than for software represents an important business model paradigm shift made possible by agents.

“We think outcome-based pricing is the future of software. I think with AI we finally have technology that isn’t just making us more productive but actually doing the job. It’s actually finishing the job,” said Taylor.

Two other promising startups building agentic solutions for customer support are Decagon and Maven AGI, both of which recently announced Series A rounds.

Maven claims that its agents can autonomously handle 93% of all customer questions while reducing resolution times by 60%.

Decagon, meanwhile, boasts an impressive list of early customers that includes Eventbrite, Rippling and Substack.

“Technology differentiation is an interesting question in this category,” said Decagon CEO/cofounder Jesse Zhang. “Everyone is using the same underlying AI models, whether it’s OpenAI’s models or open-source models like Llama. So the differentiator is in the infrastructure, the orchestration that you build around those models. Companies building agents today are basically building graphs, where each node in the graph is an API call or an LLM call or so on. We have our own views on the best way to architect that graph.”