Enterprises are moving fast on Generative AI—but many are still struggling to answer the most important question: what value are we actually getting for what we’re spending? At a recent Human AI Advisory Council session, David Tepper, CEO of Pay-i, introduced a practical approach to closing this gap: bringing financial clarity, operational accountability, and performance insight to GenAI adoption.
David outlined why ROI measurement in GenAI is uniquely difficult. Costs aren’t straightforward—spend is spread across multiple models, vendors, clouds, and toolchains, with hidden fees, usage spikes, and “capacity drift” that can quietly inflate budgets. Attribution is equally complex: the same chatbot experience might route through different models or versions, producing different outputs and dramatically different costs. And then there’s shadow AI—usage happening outside approved workflows—further obscuring what’s really being consumed.
Pay-i addresses this challenge with a private SaaS platform that tracks GenAI usage, cost, and performance end-to-end, across clouds and models. Unlike traditional cost tools, Pay-i connects spend to the full interaction lifecycle—tracking cost across entire chat sessions, identifying model-level variations even for identical code, and surfacing failure spend (money burned on hallucinations, retries, timeouts, or poor outputs). It also provides visibility into overlooked drivers like vision costs and opaque pricing structures.
What makes this especially actionable for enterprises is the dual lens: executive dashboards for strategic oversight and ROI narratives, and engineering-level telemetry for deep debugging—down to call stacks, HTTP headers, latency and failure analysis. The platform also supports value tracking via business KPIs such as satisfaction, deflection, and outcome quality, enabling teams to define custom “value policies” tied to what matters most.
For organizations spending $500K–$1M+ annually on AI, especially in ROI-driven sectors like financial services, Pay-i offers a clear promise: turn GenAI spend into measurable unit economics—and turn experimentation into scalable, governed value. With partners like AWS, IBM, and Microsoft, Pay-i reflects a broader truth the Council reinforced: the next phase of GenAI isn’t about more demos—it’s about disciplined deployment, transparency, and provable returns.
