AI Just Raised the Bar on Experimentation

AI is evolving on a weekly basis. Models shift quickly, and prompts that worked yesterday may feel off today. In this environment, simply relying on intuition won’t cut it. With so much changing, companies need a reliable way to separate the signal from the noise.

That’s why OpenAI’s acquisition of Statsig has caught the attention of so many product and data teams. When a company of OpenAI’s size brings experimentation in-house, it sends a clear message: Testing tied to trusted data is no longer a nice-to-have. It’s business-critical.

3 ways AI makes experimentation business-critical

Small AI tweaks mean big impact on experiences

A tweak to a prompt or policy can ripple across accuracy, latency, safety, and satisfaction all at once. Even a basic update to an AI feature can simultaneously affect each of these factors.

This means you need to reliably measure a variety of outcomes, not just look for a single “win.” Product teams need to run more experiments to make sure they can confidently improve their customer journeys and make the most of every product investment.

Personalization is the default experience

AI tailors content to each user, but that process is fundamentally probabilistic. This is an incredibly powerful addition to your digital products and experiences, but nearly impossible to understand and trust what is happening.

Experimentation offers a causal link about which changes drive the most impact, so teams can continue to iterate with a higher degree of confidence instead of hoping AI will improve the experience on its own.

Teams that can experiment at scale are in a far better position to understand how AI is delivering personalization without negatively impacting the experience (and driving the right business outcomes).

AI evals matter, but they aren’t enough on their own

AI evaluation adds an important step before features reach users. Evals let you optimize prompts and models for desired outcomes and catch regressions, safety issues, and quality drift early. But evals alone don’t tell you whether an AI feature drove business impact.

You still need experiments with real users and real metrics to know if activation, revenue, or retention improved.

Disjointed tech stacks still stand in the way

Most teams have a pretty jumbled tech stack, with their analytics sitting in one tool, feature flags in another, replays in a third, and the warehouse off to the side. This leads to mismatched metrics and endless arguments about which results to trust.

While teams try to integrate their way out of this mess, it often leads to constant fire drills and requires ongoing maintenance. These integrations force teams to wait for help (slowing down experiment velocity) or run lower-impact experiments, limiting the value of experimentation overall.

When teams run more tests, they learn faster and ship better experiences. Best-in-class teams eliminate tech roadblocks and empower product managers, marketers, and growth teams to run experiments on their own. This enables them to optimize faster and drive business results without waiting for help.

Where we fit in

Amplitude’s Digital Analytics Platform brings experimentation, analytics, session replay, guides, and surveys together in one place, fully connected to your data warehouse. This unified approach reduces integration overhead and empowers more teams to run experiments, which ensures organizations can make the most of their AI investments.

Amplitude helps teams:

• Scale experimentation faster. Set up tests quickly, see results in real time, and roll out with confidence.
• Learn from every release. Pair metrics with session replays and heatmaps to understand what happened and why.
• Trust your results. Connect every test to governed warehouse metrics like retention, revenue, and activation to optimize the right outcomes.
• Democratize testing. Put experimentation in the hands of the teams closest to your digital experience, not just data science and engineering.

Experimentation as a competitive differentiator

AI raises the stakes. The companies that win will scale experimentation across the full customer journey and improve every user experience. By empowering teams to run tests on their own, teams can iterate quickly and drive faster impact.