Beyond the Hype: The Real Metrics Behind AI Product Launches

In the aftermath of a challenging product launch, a skeptical TPM reflects on the metrics that truly matter—leading vs lagging indicators, KPI trees, and the dangers of vanity metrics in the fast-paced world of AI.

Metrics Reveal The True Journey

It was the end of a long week, and the adrenaline from our AI product launch had faded into a haze of exhaustion and self-reflection. As I stared at the data on my health dashboard, I was reminded of the excitement that had surrounded our project. But as any Technical Program Manager (TPM) knows, the real story often unfolds in the metrics, not the hype. I thought about the leading and lagging indicators that we’d chosen to guide our journey and how they shaped the narrative we would tell our stakeholders.

Leading indicators are like the compass on a ship. They guide you toward your destination, helping you anticipate the winds and currents that might push you off course. In our case, we had identified user engagement metrics—like the number of active users interacting with our AI tool—as leading indicators. They suggested early on whether we were on track to meet our adoption goals. But as I sifted through the numbers, I couldn’t shake the feeling that we might have been too focused on these indicators, overlooking the deeper story they told.

On the other hand, lagging indicators, such as revenue growth and customer satisfaction scores, felt like the ship’s wake—providing a retrospective view of our journey. While they were essential for understanding the success of our launch, they came too late to inform our strategy. The challenge lies in balancing these two types of metrics. When I look back, I realize we might have been so enamored with leading indicators that we neglected to fully explore what the lagging indicators were trying to tell us.

Data storytelling is an art form, and like any good storyteller, a TPM must weave together various threads of information to paint a complete picture. In the aftermath of our launch, I found myself crafting a narrative for our stakeholders that highlighted both the successes and the areas needing improvement. I created a KPI tree that illustrated how our leading indicators fed into our broader business objectives, but I also had to confront the vanity metrics that had crept into our discussions—those shiny numbers that looked good on paper but didn’t necessarily reflect real user value.

For instance, we had boasted about the high number of downloads on launch day, but as the dust settled, I realized that this metric was a classic example of vanity. It didn't account for how many users actually engaged with the product afterward. We had a flashy number, but it was just that—a number. The real tale was in the user feedback and engagement rates that followed. This is where the risk of vanity metrics becomes apparent; they can cloud judgment and lead to misguided decisions. As TPMs, we need to ensure our metrics tell a story that reflects real user experiences, not just an illusion of success.

In the world of AI, where hype cycles can spiral out of control, we have to be particularly vigilant. The allure of generative models and their capabilities can easily lead

Balancing Innovation With Real-World Impact

us to chase metrics that sound impressive but lack substance. During our launch, I witnessed this firsthand as we debated whether to prioritize features that showcased the AI's capabilities over those that truly solved user pain points. It was a constant tug-of-war between innovation and practicality.

Throughout this process, I learned the importance of making trade-offs visible. It became clear that while we could create a stunning product, we needed to anchor our decisions in metrics that aligned with user needs. This meant revisiting our health dashboard regularly, ensuring it contained not just high-level performance metrics but also qualitative data that captured user sentiment and satisfaction. After all, the success of an AI product hinges on how well it meets real-world needs, not just how well it performs in a lab setting.

As I reflect on the lessons from this launch, I realize that the true measure of success lies not in the metrics themselves but in how we interpret and act upon them. It’s about embracing the story they tell—one that encompasses both triumphs and setbacks. Our role as TPMs is to distill this complexity into actionable insights, guiding our teams through the noise of data and helping them navigate the ever-evolving landscape of AI.

In conclusion, the next time you find yourself drowning in a sea of metrics, remember to take a step back and ask: what story are these numbers telling? Are we celebrating vanity metrics, or are we focused on the indicators that truly matter? As we continue to explore the intersection of TPM and AI, let’s commit to a data-driven approach that values substance over spectacle, ensuring our products not only launch successfully but also thrive in the real world.