Every pitch deck in 2025 has “AI-powered” somewhere on slide one. Investors expect it. Users are curious about it. But here’s what most founders won’t admit: half the AI features out there don’t actually need to exist.
Before you bolt AI onto your product, let’s figure out what’s worth building and what’s just noise.
High ROI: Customer Support and Automation
This is the easiest win. AI-powered support — chatbots, ticket routing, auto-responses — has a clear, measurable return. You reduce support costs, improve response times, and your users get answers faster.
If you’re handling more than 50 support tickets a week, this should be on your roadmap. Tools like Intercom and Zendesk already offer AI layers, but a custom solution trained on your docs and product can outperform them significantly.
Good If You Have Data: Recommendations and Personalization
Recommendation engines work — when you have enough data to feed them. Netflix, Spotify, Amazon — they all run on this. But they also have millions of data points.
If you’re a pre-launch startup with 200 users, a recommendation engine is premature. Focus on getting more users first. Once you have real usage data, personalization can dramatically improve retention and engagement.
Expensive but Sometimes Necessary: Custom Models
Training your own model is expensive. We’re talking tens of thousands in compute costs, months of development, and a team that knows what they’re doing. This only makes sense if AI is the core of your product — not a feature, but the product itself.
Think: medical diagnosis tools, fraud detection systems, proprietary data analysis. If you’re building in one of these spaces, custom models are worth the investment. Otherwise, use existing APIs and models.
Fast to Build: LLM Wrappers
Building on top of OpenAI, Anthropic, or other LLM providers is the fastest path to shipping an AI feature. You can go from idea to working prototype in days.
The catch? Low barrier to entry means high competition. If your entire product is a thin wrapper around GPT, you’re one API update away from obsolescence. Use LLMs to test market fit quickly, but build defensible value on top — proprietary data, unique workflows, domain expertise.
The Decision Framework
Before adding AI to your product, ask three questions:
- Does this solve a real user problem? Not “would this be cool” — does it solve a pain point your users actually have?
- Can you measure the impact? If you can’t define what success looks like, you can’t justify the investment.
- What’s the simplest version? Start with an API call and a basic integration. Prove it works before building anything custom.
Don’t Build AI for the Sake of AI
The best AI features are invisible. Users don’t care that it’s “AI-powered.” They care that it works, it’s fast, and it saves them time. If a simple rule-based system solves the problem just as well, use that instead.
Thinking about adding AI to your product? Talk to IN2Labs — we’ll help you figure out what’s worth building and what’s not.