The AI Hype Cycle 

If we listen to the hype, AI is the secret elixir we must add to everything. AI excitement (or fear) dominates product conversations these days. We’ve seen this pattern before. 

The Machine Learning Sensation

Think back to 2015. Every investor pitch, every product roadmap, every conference keynote featured a machine learning (ML) angle. Companies raced to add ML-powered features to their products (“Customers who bought skateboards also bought aluminum crutches”). New features were often pushed without a clear answer to the question, “does this actually help anyone?”. You may recall Google’s infamous “Deep Dream” which set out to visualize neural networks. Trained on a source full of dog images, Deep Dream simply ended up visualizing dogs everywhere. Bravo. 

The ML hype eventually cooled, leaving useless gimmicks on the junk heap. But practical ML advances remained. We use them every day because they solve real problems. Spam filters, recommendation engines, and predictive search are now so seamlessly embedded in everyday software that we've stopped noticing them. We just expect them.  

AI is headed the same way, toward ordinary, largely invisible uses. 

The Generative AI Craze 

We are now in the thick of a new hype cycle. Generative AI is the latest next big thing. Again, we are simultaneously excited, fearful, and unsure. Recent evidence reinforces this anxiety. 

Gartner's 2025 Hype Cycle placed generative AI in the “Trough of Disillusionment,” the phase where expectations collide with (sometimes harsh) reality. Less than 30% of CEOs report being satisfied with their AI investments, despite spending an average of $1.9 million on GenAI initiatives in 2024. 

Generally speaking, AI users are skeptical, too. According to a June 2025 Pew Research Center survey of 5,023 U.S. adults, 50% expressed more concern than excitement about the increased use of AI in daily life.  

Lack of trust abounds, with good reason. Frivolous uses of AI are everywhere. And we’ve all experienced AI hallucination and dealt with AI slop. As AI evolves, we’ll need to evolve the way we use it. Businesses that succeed in the AI era will ship useful features that people understand and trust. 

Making Exceptional Tools in the AI Era 

People are largely indifferent to the inner workings of technology. They simply want reliable tools that work quickly and easily. As the hype cools and AI features become more standard in digital products, basic principles of solid user experience will become more important than ever. 

Apply these tried-and-true UX principles to your AI-forward projects: 

Always Ask “Why”  

We must understand why people do what they do. Only when we know what people are trying to accomplish can we envision, make, and evolve tools for them. 

Why do people use a product in the first place? Why do they complain about it? Why might they benefit from something better? Why invest in a 3D AI avatar generator before defining a more efficient search? 

When we grasp how people behave online, ideas for newer, better features logically follow. Today this almost always means AI. But we don’t always think carefully. Caught in the throes of hype, organizations often try nearly anything Agentic or AI adjacent, regardless of whether it truly helps people with real-world tasks. This is how we end up with a zillion AI-generated, fake product reviews. 

When we ask, “why,” we are driven to practical advances that solve real problems. If we know a retailer is looking for space to lease, AI can help identify appropriate, available locations. Even better, it can proactively suggest possibilities our retailer might never have considered, then initiate next steps. That’s helpful. It’s also a far cry from an AI solution looking for a problem. You hear that, chatbots? 

Fight Uncertainty with Experimentation 

AI technology is changing and improving daily. With this change comes uncertainty. Navigate this uncertainty by experimenting with AI features rapidly. Fast iteration cycles lower risk and create feedback loops that reveal problems early on.  

Testing our work with users is the most effective way to build user-friendly products. Savvy teams test ideas, gather feedback (from people who really use their product), adapt to what works well, and adjust what needs revision.  

AI tools also give us the ability to experiment far faster than we ever could before. Have a new feature in mind? Scaffold it up with Claude in half an hour. Put it in front of users and have them tear it apart. Take away insights and make the product better. Your AI Agent of choice will learn and adapt. If you standardize this process and do it often, you will build and deploy AI features that people actually want. 

Build Trust with Transparency  

We use tools we trust. Be transparent about what your AI feature does and doesn't do. This is the most direct way to build user confidence and avoid user disappointment and mistrust  

If AI has generated content, say so. Label AI suggestions as, wait for it, AI suggestions. Show confidence scores where accuracy matters. Cite sources or add short explanatory content describing how a feature reaches its output.  

Trust and clear expectations are bedrock principles of solid user experience. Users who understand how AI features work are more likely to trust them. 

Beyond the Hype 

Irrational excitement and enthusiasm around AI will fade. We will inevitably shift back to investing in what people really care about: efficient, practical, useful software. This requires careful thinking and a focus on users. 

If you want your product to outlast the hype cycle and stand the test of time, focus on understanding the people who use your software, solving the problems that really matter to them, and building their trust. Businesses that invest in excellent user experience instead of pursuing AI for its own sake will inevitably lead the pack.