Why Most AI Products Need Too Much Explaining (And Why That’s a Problem)

Read time: 2.5 minutes

5 Hard Truths About AI Products (and How to Make Them Better)

1️⃣ If I Have to Explain Your AI Product, Its Value Isn't Immediately Apparent
A founder's perspective: "It's a really strong product, and it's a complex one." An investor's perspective: "If I don't see value in 10 seconds, neither will your customer."
Solution: Start with a measurable outcome.
Examples include "30% reduction in fraud" or "40% reduction in support costs."

2️⃣ Most AI Companies Sell Technology, Not Impact
People don't buy models. They buy results.
Solution: Position AI as a decision engine that provides improvements.
For example, note "reducing stockouts by 25%" instead of simply describing "AI forecasting."

3️⃣ New Workflows Create Barriers to Adoption
If end users have to learn to use new systems, it will create friction.
Solution: Integrate AI within the tools people already use (e.g., CRM, ERP, support platforms, dashboards).

4️⃣ Lengthy Return on Investment Timeframes Frighten Enterprise Customers
Enterprise buyers want to see proof fast!
Solution: Design a 30-day measurable pilot with baseline and improvement criteria.

5️⃣ The Best AI Products Are Immediately Valuable to Users
Great AI tools deliver value without requiring training; they provide leverage immediately.
Solution: Focus on solving a single difficult task extremely well.

💡Key Takeaway: 

AI is rarely adopted because the model itself is too weak... rather, it is due to a lack of evidence that supports its value.

To prove the power of AI technology, a company must quickly provide evidence so consumers continue to engage with its products.