Introduction
AI isn’t just a shiny new toy anymore. It’s transforming into the revenue engine for businesses willing to place big bets. During our recent webinar with Lukas Goetz-Weiss, Eppo’s Head of Customer Data Science, we shared some fascinating insights about how the smartest companies are measuring their AI investments. A lightbulb moment for me? How pivotal but messy measuring AI’s real-world business impact truly is. Yet, it’s mission-critical.
This is about separating hype from hard numbers, and today, I’ll break down how the leading companies and Eppo clients are doing just that.
Real Revenue Wins
To appreciate AI’s potential, look no further than these real-world examples of businesses putting it to work where it matters most.
- Stripe: Stripe’s Agent Toolkit SDK is empowering AI to directly manage payment processing. This isn’t experimental tinkering. It goes straight to the heart of their revenue engine, driving high-risk, high-reward operations.
- Pizza Hut: Seemingly simple, Pizza Hut’s AI-powered text-to-order feature on platforms like WhatsApp and iMessage is now responsible for 60% of total order volume, facilitating over 100,000 interactions monthly. That’s not a gimmick, that's transformational.
- Mastercard: On the financial front, Mastercard’s use of generative AI to detect fraud has increased their fraud detection rates by 20%. For a company where risk mitigation defines success, this is game-changing.
Key takeaway: These aren’t AI pilot projects or experiments. These are mature, critical, operational systems that tie directly to major revenue streams.
The AI Adoption Minefield
Here’s the reality check: adopting AI isn’t a walk in the park. Companies face numerous challenges that can derail even the best initiatives.
- Probabilistic Outputs: Unlike deterministic systems, AI produces results that vary, even if the same inputs are used. Debugging feels more like trying to catch a ghost.
- Metric Misalignment: Often, the publicly available AI model evaluations are obsessed with metrics like latency and model "intelligence." But here's the bigger question, is the AI driving revenue, or cutting costs in impactful ways?
- The Speed Trap: AI’s evolution feels relentless. Case in point? I thought DeepSeek R1 had been around for six months, but it turned out to be just two months old. The pace of innovation is so fast, it’s easy to feel left behind.
Adopting AI already feels daunting. Measuring its effectiveness at scale? Even harder. But these challenges are solvable, with the right tools and strategy.
Turning Challenges Into Cash
How do the smartest companies turn AI’s chaos into clear revenue wins? They follow a scientific playbook, one that combines experimentation and practical measurement to validate impact.
Here’s two examples:
- Case Study 1: An AI-native SaaS company ran controlled comparisons between GPT-4o and Llama 3.1. The result? Choosing the open-source option saved them $50 million annually.
- Case Study 2: A three-sided marketplace added an AI variable to its search ranking. The tweak drove a modest 1.5% lift, but at their scale, that translated to millions in organic search revenue.
Eppo’s Philosophy for AI Measurement
- Treat AI like any other experiment; uncertainty is normal.
- Use A/B testing to anchor results to real business metrics, not just model benchmarks.
- Harness your data warehouse for insights beyond surface-level numbers (think conversion rates, churn, and revenue impacts).
- Stay agile, with quick test cycles to adapt to new innovations or risks.
- Remember, AI isn’t just about chatbots. Its true impact unfolds in workflows, structured data transformation, backend automation, and more.
The Eppo Platform (Where the Magic Happens)
Here’s the truth about measuring AI impact. Without the right platform, it’s impossible to track something this dynamic. That’s where Eppo comes in.
Demo Spotlight
During the webinar, Lukas walked us through our platform using an illuminating example from Rustin Trust Motors (our fictional AI-enabled car sales company). This demo showcased how teams can dynamically adjust a chatbot’s tone, run controlled experiments, and scale new configurations with ease. We’ve embedded this demo for review below so you can see exactly how the platform simplifies and streamlines the process.
What Makes Eppo Stand Out:
- Intuitive Configuration: Easily switch between AI variations, deploy, and roll out experiments seamlessly.
- Data-Driven KPIs: Pull metrics like revenue or churn directly from your warehouse instead of relying on vague model “intelligence”, this is where the business truth lies.
- Built-in Tradeoff Management: Compare cost against revenue gains with confidence.
- Variance Reduction Tools: Advanced Cuped-based models for faster conclusions and cleaner insights.
- Insights Hub: Build a transparent, dynamic knowledge base of experiment learnings.
Why This Matters
It’s not just about testing AI anymore. It’s about measuring business impact with rigor, giving your entire team the confidence to act on results.
From “What If” to “Here’s How”
Think about this for a moment. AI is no longer science fiction. It’s actively delivering revenue for top-tier companies across industries. But here’s the flip side. The complexity and uncertainty of measuring impact can feel paralyzing if you don’t have the right approach.
What’s Eppo’s edge? We simplify the hard stuff. Whether it’s through our platform or our guiding philosophy of experimentation and iteration, we deliver the pragmatic playbook for transforming potential into measurable ROI.
What’s Next
If you're curious about how measurable your AI initiatives actually are, don’t guess. Eppo can help turn your AI into a revenue driver. It’s time to bring clarity and control to AI experimentation. Together, we’ll make it impactful, repeatable, and scalable. Reach out for a personalized demo today.
