Despite the promising capabilities of AI features and the range of services they bring to products, they come with a harsh reality, especially after launch. Unlike traditional product features, AI features do not rely on static conditions and can degrade all on their own. AI features do not align with traditional software engineering rules, and that's a realization for experienced product managers. It's generally believed that day 1 is when your model performs best, so shipping does not equal success when launching AI features. Read on as we look a little deeper into why most AI features fail after launch and how PMs can prevent them. The Hidden Decay: Why AI Performance Drops Over Time AI systems tend to decay all on their own, and this is an infuriating realization for product teams. Traditional software systems are programmed with static logic; so if your code says 2+2=4, that remains true even 10 years from today. If you created a login page, it works the same as long as the server stays on. However, AI features are built on patterns and are dynamic. If you asked an AI the same question twice, you'll get two different responses. While this is a primary feature that makes AI exceptional for user experience, it's also responsible for its model decay. To properly manage AI products, PMs must first understand why they behave differently from the typical products we are used to. Model Drift Model drift is considered the typical silent failure scenario seen in AI products. This happens because an AI model is usually a snapshot of the world at the moment when it was trained. Over time, an AI model might not know how to handle a completely novel scenario because it wasn't trained on data for that instance. In cases like this, it is usually because real-world data diverges from training data or user behavior changes faster than models can adapt. This often comes with a "silent failure" problem where the model continues to generate wrong answers, but product teams cannot instantly notice the problem. When traditional features crash, the problem is usually evident: whether a button becomes unresponsive or it's a 404 error page. However, because AIs are designed to provide the best possible answers, the model continues to generate confident answers, even when it's dealing with data it doesn't understand. As such, PMs are required to view AI features as a living system rather than a one-time deliverable. It's a management approach that requires continuous monitoring, feedback loops so the system can learn when the AI is wrong, and regular training to keep feeding the model with updated real-world data. Why Launch Metrics Create a False Sense of Success When AI products launch, the first month usually delivers impressive metrics that might create a false sense of success. It's all the typical green lights PMs are looking for: traffic spikes, high activation rates, and green lights all over the dashboard. Meanwhile, AI products typically generate such success for the curiosity factor they carry, rather than reaching those who actually need the solution. So, in the first month, many people are testing the limits of the newest AI product. This demo-driven adoption should not be mistaken for sustained usage, and product teams have a lot more to learn after the first drop-off. Metrics such as initial activation rates, short-term accuracy benchmarks, and one-time usage spikes could be referred to as "success metrics" in traditional systems. But the circumstances differ significantly for AI products, and traditional metrics cannot differentiate those who found value in the product and those who tried it. That said, there are qualitative metrics that PMs can look out for that typical launch metrics don't show: Long-Term Trust: Trust is a key quality in modern AI solutions, especially given users' continued skepticism of model outputs. Even though your metrics can show how many clicks your AI products generated in a day, they won't show users' skepticism after it provides the wrong answer. Long-Term Trust: User Reliance: This reflects how much users trust the AI to influence or replace their decisions. So it's important to find out whether users have integrated the AI into their workflows, or if they just have access to it for the sake of having it. Launch metrics typically don't show you any way to tell who needs the tool and who is just playing with it. User Reliance: Quiet Abandonment: With traditional apps, users typically file tickets. However, if an AI solution is underperforming, users tend to just leave it without complaining. It's either they move to a better alternative or just return to the classic manual workflow they had. Quiet Abandonment: These are measurable metric proxies that product teams can find much more informative than traditional performance metrics. Issues Product Managers Should Watch Out For After Launch The qualitative metrics mentioned in the previous section provide sufficient information for product teams to evaluate their AI products. At the same time, it points to some of the reasons AI features may be failing after launch. Once the initial excitement/curiosity phase wanes on new AI features, here are some trends that foreshadow failure after launch. Trust Erosion As mentioned earlier, once users notice an AI feature functioning out of context, being repetitive or generic, they just silently stop using it. The pattern that typically influences that behaviour is because AI features feel unreliable rather than "broken." For something broken, you'd expect it to be fix. But if something doesn't deliver what it promises, you just simply stop expecting it to. With trust erosion, common user responses that indicate declining quality in your AI features include double-checking outputs, ignoring suggestions, or turning the feature off entirely. Broken Feedback Loops In a system where users are not quite ready to provide immediate feedback, product teams have to create flows that make it much easier. However, that does not appear to be the case; many AI products and features have broken feedback loops. AI needs to learn to improve itself, but many AI features are a one-way street UX: the AI provides content to users, with no way for users to push corrections back. Some products have a thumbs-up/thumbs-down button that has no real influence as a feedback mechanism, other than sentiment analysis. The Product Manager's Blind Spot in AI Features In the end, one can argue that AI products fail not because of their underlying tech, but a lack of efficient management. The typical product manager process involves roadmaps where features are delivered, marked as "done," and monitored for uptime. Applying this typical playbook to shipping AI features exposes certain blind spots stemming from treating AI as just another part of a tech stack. Here are some of the common blind spots: Treating AI as a feature instead of an ecosystem: An AI feature cannot be treated the same as a search bar or a dark mode toggle. With traditional software engineering, you build a feature, test it, and ship it. AI features require a constant supply of data, a feedback mechanism, and a restraining pipeline to digest corrections.Over-reliance on model accuracy vs. user perception: With AI products, PMs tend to be blind to user experience in favor of model accuracy. For example, product teams can hide behind "our model is 94% accurate on the test set," and users may perceive the product as 0% useful if it fails 6% of the time. Lack of ownership for post-launch AI performance: The typical PM has moved on to the next item on the roadmap when a product launches. AI decay is silent and does not trigger any emergency alarm, and post-launch AI performance can quickly fall into an ownership vacuum.No clear "AI health" metrics: If PMs are not looking the wrong way with traditional metrics, there's a chance they rarely track metrics that actually affect AI products. To manage AI products effectively, PMs should know how to answer new questions around how users interact with their AI. Treating AI as a feature instead of an ecosystem: An AI feature cannot be treated the same as a search bar or a dark mode toggle. With traditional software engineering, you build a feature, test it, and ship it. AI features require a constant supply of data, a feedback mechanism, and a restraining pipeline to digest corrections. Treating AI as a feature instead of an ecosystem: Over-reliance on model accuracy vs. user perception: With AI products, PMs tend to be blind to user experience in favor of model accuracy. For example, product teams can hide behind "our model is 94% accurate on the test set," and users may perceive the product as 0% useful if it fails 6% of the time. Over-reliance on model accuracy vs. user perception: Lack of ownership for post-launch AI performance: The typical PM has moved on to the next item on the roadmap when a product launches. AI decay is silent and does not trigger any emergency alarm, and post-launch AI performance can quickly fall into an ownership vacuum. Lack of ownership for post-launch AI performance: No clear "AI health" metrics: If PMs are not looking the wrong way with traditional metrics, there's a chance they rarely track metrics that actually affect AI products. To manage AI products effectively, PMs should know how to answer new questions around how users interact with their AI. No clear "AI health" metrics: What Winning PMs Do Differently What we've discussed so far suggests that the default state of an AI product is decay, so the Product Manager's primary job is preservation. The key to the success of AI products is to stop treating them as a one-time delivery and to recognize the technology's fragility. In changing the playbook, PMs must therefore design for post-launch from day one, establish AI health metrics, and create human-in-the-loop systems. Successful PMs recognize that acknowledging that AI decays is step one, and the next step is to build an operational machine that keeps it alive. This operational machine will include scheduling, retraining, and evaluation, monitoring drift and behavioral changes, and internal cross-functional collaboration where the product, data science, design, and customer insights teams can work as a cohesive unit. AI Success Is a Lifecycle Problem One thing is for sure: most AI products won't fail on launch day. But without proper management, that might be the biggest day in terms of metrics for the product before its eventual death. The broader global transition into AI-powered features forces a fundamental redefinition of the Product Manager's role. PMs now have to ensure sustainability after shipping features, as success is no longer about code infrastructure, but ongoing stewardship. This gardening approach to building and shipping is therefore defining how the next generation of PMs should operate. Long-term value comes from assuming your AI product will break, so don't expect your new feature to stay smart forever. Plan for the decay with retraining schedules, feedback loops, and monitoring.

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Why Most AI Features Fail After Launch (And How PMs Can Prevent It)

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