In this podcast episode, we explore how SaaS data and metrics are being interpreted differently as companies navigate an AI-driven landscape and increasing scrutiny at moments of diligence and fundraising.
Data quality has always mattered in SaaS, but in 2026, the stakes are higher, the margin for error is smaller, and the questions boards and investors are asking have fundamentally shifted.
In this episode of SaaS Conversations, Katherine Zhang sits down with Dan Palay, CEO of KPI Sense, to explore how SaaS data and metrics are being interpreted differently as companies navigate an AI-driven landscape and increasing scrutiny at moments of diligence and fundraising.
This is not a conversation about dashboards or data tooling. It is a conversation about what it means to know your business, why a single source of truth requires multiple systems, how the wrong metrics or inconsistent definitions quietly erode board confidence, and why AI is introducing a new class of cost and measurement risk that most leadership teams are not yet tracking correctly.
Dan draws on patterns he sees repeatedly across growth-stage SaaS companies, explaining why metrics that look clean on the surface often hide segment-level risk, why AI spend is behaving more like a variable professional services cost than a traditional SaaS fixed cost, and what strong financial leadership looks like when it matters most.
For leaders who want to go deeper, Dan and the KPI Sense team work directly with growth-stage SaaS companies on financial analytics, metric dashboards, and data normalization. You can reach Dan at dan@kpisense.com or visit kpisense.com.
Interested in benchmarking your SaaS business?
Contact us to get started.