OpenAI, one of the OGs of the age of artificial intelligence, recently found itself facing a situation that every company hopes to avoid. An upgrade to its flagship product failed miserably. Even the company’s CEO, while initially describing the rollout of GPT-5 as “bumpy,” ultimately admitted that his company “totally screwed up” the launch that happened on August 7, 2025.
In the aftermath of the GPT-5 failure, CEO Sam Altman gave users explanations of what went wrong, such as an out-of-commission autoswitcher. He also detailed what they could expect if they stuck with OpenAI, such as an interface that is better at remembering users’ habits. But if Altman really wanted to fix things with his users, he should have been focused on how he rolled out instead of what he rolled out.
How Should AI Companies Handle Product Rollouts?
AI evolves over time, making traditional “finished product” launches risky. Companies can avoid backlash by:
- Framing rollouts as chapters or waves, not final products.
- Offering tiered rollouts with opt-ins.
- Publishing transparent change logs.
- Closing the feedback loop quickly and visibly.
- Prioritizing continuity and personalization as core features.
Product Rollouts in the Age of AI
AI isn’t like any other technology, so it isn’t developed like other technologies are. Companies don’t produce it. Instead, they initiate it, after which it evolves. It’s not like canned soup — put in all the ingredients in the right proportions and stamp a lid on it so the user can simply crack it open, heat it up and enjoy. AI is more like jazz. It’s built on a comprehensive knowledge base but in a way that doesn’t lock it into one destination.
What does that mean for rollouts of AI-driven products and platforms? You’ll be most successful if you don’t promise soup. In fact, you may not want to promise anything except an opportunity to be part of a journey of innovation.
When you present AI as a finished product instead of the next chapter in an unfolding story, you invite the kind of problems that OpenAI is now sorting through. When GPT-5 went live, users got a whole new book, not a new chapter.
The personality users had grown to love was replaced with a safer, more neutral tone that felt flat compared to what was there before. Issues with memory and context handling also made it feel like past conversations were closed, which disrupted continuity for ongoing work. Scaling to improve performance becomes a problem when it requires you to sacrifice the draw of familiarity and personalized connection that has kept people loyal to a platform.
Give Users a Sense of Ownership
AI companies that want to avoid stumbling into the type of problems GPT-5 caused should consider reframing rollouts to acknowledge the ever-evolving — and often unpredictable — nature of their products. They should rebrand them as waves, chapters, quests or some other term that feels individual on the micro level yet provides an inclusive experience for all users of the product.
Practically, that type of rebranding involves giving consumers clear context on what’s new, what’s experimental and what’s still being fine-tuned. This approach gives users a sense of ownership in the process, which allows every change to become less of a jolt and more of an event they’re part of shaping.
To add to the sense of ownership, companies can also make closing the feedback loop faster and smarter by setting targeted timeframes for responding to customer inquiries (e.g., within 48 hours) and automating workflow channels across platforms to ensure all feedback contributes to technological improvements and the overall user experience. Providing frequent updates through email newsletters or social media posts that are informed by users’ feedback is valuable in an age where people want companies to implement features that enhance the feeling of personalization. Prioritizing feedback from power users who notice the subtleties that casual users miss is even more valuable.
One of the big missteps OpenAI made with the GPT-5 rollout was failing to give users the choice to stick with their preferred version. Worse, failing to provide a clear explanation of the changes they should expect exacerbated the problem. Companies can avoid inciting widespread user frustration by offering tiered rollouts with opt-ins that don’t force every user into the deep end on day one. With AI, the emotional connection is part of the utility, which means companies that deal in it need to consider continuity as a feature, not a nice-to-have.
Transparent change logs are another component that helps with continuity and ownership. Where clarity builds trust, which is the currency you’ll need to keep your users, mystery breeds distrust, which can cause users to theorize that any forthcoming changes may not be in their best interests.
Embracing Evolution From the User Side
If your company doesn’t develop AI tools, then it probably relies on them. For those on the user end, the best way to stay efficient is to remain patient.
We’re watching GPT-5’s rollout the way you watch an NFL rookie play his first game. This usually involves flashes of brilliance mixed with obvious growing pains. Remember that AI evolves. It’s not a sculpture that you carve to perfection and put on display, but an evolving entity that mutates, adapts and occasionally trips over its own feet. It progresses in a series of ebbs, flows and course corrections, not a single, glorious leap.
When the GPT-5 rollout left users frustrated, OpenAI chose to focus on why it failed to perform and how it would do better in the future. That focus needs to change. Rather than merely reacting to user feedback after fumbling a product rollout, companies that produce AI tools must adopt a proactive approach to receiving and implementing feedback.
The simple lesson to be learned from the rollout is that you can’t win AI loyalty by hitting benchmarks alone. You win it by keeping the magic alive while you scale. Reframe rollouts to support that vision, and success will be much easier to achieve.
