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AI is everywhere—featured in demos, sales decks, and investor calls. Founders feel the pressure to “add AI” as if it’s just another feature to check off. But here’s the reality: most AI features either don’t work reliably in production or fail to deliver real value.
At TLVTech, we’ve partnered with dozens of startups to integrate AI—sometimes with great success, other times with hard lessons learned. Before you invest your team’s time, money, and energy into building an AI product, here’s what every founder should know.
Don’t chase the latest model just because it’s trendy. Focus on the real user pain you’re solving. You don’t need GPT-4 to deliver value; you need clarity on what your users actually need.
The best AI is invisible. If users notice the AI, it’s either broken or unnecessary. Ask yourself: “Would anyone use this feature if it didn’t have AI under the hood?” If the answer is no, rethink your approach.
It sounds counterintuitive, but your first version shouldn’t be “smart.” Before investing in models or APIs, validate your assumptions manually or with simple rules. If a human can handle the task in 30 seconds, do it manually behind the scenes. If users find value, then—and only then—should you automate.
For example, instead of building a custom AI support agent, route key queries to your team and tag them. If you see strong user engagement, you’ll know it’s worth productizing.
The AI feature that impresses in a demo isn’t always what keeps users coming back. Users don’t care about novelty—they care about reliability and usefulness.
Test for failure early. What happens when your model gets it wrong? What’s your fallback? Users will forgive limited features, but not unpredictable or nonsensical results.
Plugging into OpenAI or another LLM provider doesn’t solve everything. You’re still responsible for:
You need guardrails, error handling, and a user experience that anticipates mistakes. Otherwise, you’re shipping a prototype—not a product.
Unless you’re building deep tech, off-the-shelf models will get you 90% of the way. Focus on user experience, feedback loops, and smart prompting.
Only train custom models if:
You’ve pushed off-the-shelf solutions to their limits
You have unique, proprietary data
The potential impact justifies the investment
The best AI products we’ve seen didn’t start with fancy models. They started with tight user feedback loops, clear success metrics, and relentless focus on utility.
AI is a tool—a powerful one. But it won’t rescue a weak product or a fuzzy value proposition.
If you’re building something real and want AI to be part of it, let’s talk. We help teams launch AI features that actually work.
Struggling to choose the right type of AI? Our framework shifts focus from tech to execution, helping you avoid the 80% failure rate. Find your fit.
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- UI (User Interface) and UX (User Experience) design both focus on the user but address needs differently. UI deals with the look and layout, making the interface attractive. UX, however, focuses on how the user interacts with the product, ensuring a smooth journey. - A UI designer creates visual elements, making a product visually appealing and easy to navigate. In contrast, a UX designer designs the overall flow and operation of the product, ensuring each step is intuitive and easy. - UI design is guided by making the product visually pleasing and intuitive. UX design is about making the user's journey smooth and efficient. - Average salaries for UI designers start at $50,000 and can increase up to $85,000 with skills and experience. For UX designers, salaries start around $60,000, with potential earning up to $95,000. - UI and UX overlap in influencing user interaction. UI focuses on visual design and placement, while UX looks at ease of navigation. UI design affects UX, with poorly designed elements resulting in a bad user experience.
