Discover how top SaaS and AI leaders built and launched AI products and features that customers actually pay for. Explore real case studies, pricing strategies, and GTM lessons from industry leaders.
See how Recall.ai turned the most painful part of building AI products into a business — and why conversation data is the missing piece in AI automation. Read more.
Learn why founders Mike Murchison and David Hariri spent months as customer service agents working support desks before writing a single line of code. Read more.
Commercializing AI requires decisions about pricing, positioning, and go-to-market strategy. These case studies detail how successful SaaS and AI leaders in our portfolio made those calls. We’ve collected actionable frameworks from their hard-won lessons across different industries, company stages, and business models.
What to build: How leaders identified which AI use cases customers would pay for
How to price: Strategies for usage-based, seat-based, and hybrid models
How to sell: GTM approaches that drove adoption and revenue
What worked (and what didn’t): Honest lessons from the front lines of AI experimentation
“You need to understand the entire chain of causality from your infrastructure to the end user value.”
— David Gu, CEO of Recall.ai
While every company’s journey is unique, several patterns emerge across successful AI commercialization efforts:
Rather than building features developers were already trying to build, companies like Recall.ai instead created the infrastructure to make it possible. The technology enabler (LLMs) mattered less than the customer pain point (spending years building meeting recording infrastructure).
Even the best AI product fails without the right go-to-market strategy for enterprise adoption. Ada helps customers build what they call “Agentic Customer Experience organizations.” This operational transformation approach empowers enterprise customers to increase AI investment.
The best AI pricing models grow in tandem with customer success, rather than leaving room for misaligned incentives. Recall.ai’s natural alignment meant customers were comfortable paying more as their products gained adoption, avoiding the tension that often emerges with seat-based models.
Building AI agents that outperform humans is only half the challenge. Enterprises need new organizational capabilities to deploy them successfully. This recognition that AI adoption requires workforce transformation, not just software deployment, has become central to Ada’s enterprise success.
This series is designed for leaders at traditional SaaS companies navigating AI product decisions such as:
