Merriam-Webster has named slop the word of the year for 2025. While it primarily refers to low-quality digital content, the nomination signals broader public sentiment toward AI. Nobody wants AI slop, and companies that are overly eager to push AI features into existing products are subject to relentless mockery.

Just look at how Microsoft’s commitment to AI features created backlash that quickly generated memes renaming the company to Microslop. It might be just a small group of Reddit nerds complaining, or a smear campaign from competitors, or a combination of both.

Still, some AI features remain beneficial. The challenge for product managers, CEOs, or anyone in digital business is knowing when your product actually needs AI.

Products Solve Problems

I already wrote about the importance of hype resilience when replacing human expertise at a team level. The same logic applies when building your product: fundamentally, AI features are just a part of the product. In marketing terms, a product is something that solves a problem or a customer need.

Successful products do it well, and failing ones do not. It is because of this ability that they become valuable, and a company making them can capture part of that value to turn a profit. There are examples of AI features making meaningful business improvements to increase product value.

Large Language Model (LLM) applications in documenting meetings, writing corporate emails, creating design or code prototypes, improving customer service chatbots, and many other similar applications are successful. The impact of AI features is undeniable and is only likely to grow.

Yet, many generative AI pilot initiatives fail to deliver any meaningful impact. The underlying reason is often the inability to solve problems relevant to users or employees, but even when that is the case, other challenges must be addressed.

Most of these challenges are nothing new to entrepreneurs scaling digital products. Almost any feature, AI or not, needs to solve similar pain points. The difference lies in managing the expectations that come with the AI label. There’s so much hype surrounding generative AI implementations that companies fear being left behind.

Competitive FOMO

Fear of Missing Out (FOMO) applies to CEOs and other decision makers as much as it does to consumers. When ChatGPT was released, many carefully planned strategies were abandoned to chase the visibility of staying ahead.

Competitive pressure for AI features resembles an arms race. One company announces AI integration, and others must respond with something similar or presumably risk their product appearing outdated. Never mind that such a feedback loop might not reflect the needs of consumers.

The calculation seems to be that a critical mass will start choosing products primarily for their AI features. It’s undeniably true for investors, since AI has become the dominant growth narrative in markets.

Any public or venture-capital-backed company now faces pressure to implement at least some AI features. Investors expect AI initiatives, and analysts track AI announcements. Without them, the company seems to be missing out on the most important technological innovation since the Internet.

The same reflects in marketing as companies are quick to rebrand existing automation or machine learning workflows as AI-powered. Such a buzzword helps to drive attention and justify premium pricing, and up to a point, it works.

Why Users Might Prefer Products With AI?

Studies show that people tend to choose products with more features, even if they won’t actually use them. If cost isn’t an issue, consumers almost always choose a product that can do more, and the AI label is the ultimate signal for this.

However, consumers also experience feature fatigue, a frustration stemming from feature overload. Feature fatigue has been known since at least when common household appliances, such as fridges and TVs, became more complicated.

An AI-powered browser, as in the case of recent Microsoft Edge updates, is an easy way to market the product as more capable than competitors. Yet the AI features risk complicating the product's core purpose: browsing the web.

Privacy-conscious users are also suspicious of AI features as just another way to collect user data for the company's own benefit. That’s where the "AI slop" label comes from, and why it means more than just reduced usability.

Some products are trying to reverse the trend, promising no LLM AI features to stand out for their user segment. Vivaldi, a privacy-focused web browser, is doing just that by advertising their intentions to avoid AI features unless they align with its browser vision.

The approach may be too extreme, as most users will still choose AI-enabled products, at least initially. The solution should be to find the right balance and learn when your product needs AI.

Does Your Product Need AI Features?

We are already seeing that traditional product development frameworks, such as RICE (Reach × Impact × Confidence ÷ Effort), are being adapted to consider the challenges that AI brings. Maryl Nika is suggesting an RICE-A framework that considers AI features brought to product development.

The technical limitations, user trust erosion, adoption, monitoring, and data privacy concerns we mentioned earlier must be included in typical RICE calculations. It might also be useful to give them more weight by using a multiplier for AI complexity. Doing so forces you to think about AI features as features during the development stage.

Weighing the difficulties of implementing AI does not help with determining whether AI features will actually help increase consumer satisfaction. The Kano model addresses this by defining three product feature types.

Many generative AI features, at least in the early stages, fall into the performance and attractiveness categories. In e-commerce, customer support chatbots, contextual product summaries, and similar features can enhance appeal or improve performance, but they hardly replace good product selection and UI.

That's not to say some AI features don't have the potential to become must-haves. AI search with at least basic spell check in e-commerce is almost a necessity nowadays. Similarly, AI-fueled web unblockers for online data collection are becoming increasingly crucial, too.

As websites block data access with AI tools, web scrapers need to leverage AI tools to manage proxy pools, browser fingerprints, and other details. Finding such must-have examples in your niche is key to determining whether your product needs AI features at all.

Conclusion

AI feature failures are product management failures. A product that starts with technology and then thinks of what it might solve is bound to fail. The same applies to all AI features. We must ask which problems AI can solve for our users and implement it only when it is a fit.

It’s undeniable that generative AI is a transformative technology, but the real winners will be those who resist the hype and stick to traditional product management frameworks. Other approaches risk getting labeled as just another AI slop.