Stop Designing AI Chat Interfaces — Build Decision-Support Tools Instead

The default AI product interface is a chat window. User types. AI responds. Repeat. It worked for ChatGPT because ChatGPT is a general-purpose tool — the input is the interface.

But most AI products aren’t general-purpose. They solve specific problems for specific users. And for those products, the chat interface is a crutch — a lazy default that forces users to think in prompts instead of thinking in outcomes.

The Problem with Chat-First AI UX

Chat interfaces put the cognitive burden on the user. The user has to know what to ask. They have to formulate their request in a way the AI understands. They have to evaluate a wall of text to find the one piece of information they actually needed.

This works for power users. It fails for everyone else. And “everyone else” is most of your users.

Imagine a financial analysis tool that makes you type “show me revenue trends for Q4 compared to Q3 broken down by region” instead of showing you a dashboard with filters. That’s what chat-first AI UX does — it replaces structured interfaces with unstructured text and calls it “natural.”

Decision-Support: The Better Model

Instead of asking users to prompt the AI, present them with the AI’s analysis and let them make decisions. The AI does the work. The human makes the call.

A decision-support interface looks like this:

• The AI surfaces what matters. Instead of waiting for a prompt, the system proactively identifies patterns, anomalies, opportunities, or risks.
• The user gets structured options. Not a paragraph of text, but cards, tables, comparisons, or ranked recommendations.
• Actions are one click away. Once the user decides, the next step is a button — not another prompt.

Real Examples

Hiring tool: Instead of “find me candidates who match this role,” the AI pre-screens applicants and presents the top 10 as profile cards with match scores, key qualifications highlighted, and a “Schedule Interview” button. The recruiter’s job is to review and decide, not to prompt and parse.

Content platform: Instead of “write me a blog post about SEO,” the AI generates three headline options with estimated engagement scores, lets the user pick one, then drafts the post with inline controls for adjusting tone, length, and audience. The writer shapes the output, not the input.

Analytics dashboard: Instead of “what’s causing our conversion drop,” the AI highlights the three most statistically significant changes in user behavior this week, shows the data behind each one, and suggests specific actions. The PM reads, decides, and acts.

When Chat Still Makes Sense

I’m not saying chat is never the right choice. It works for:

• Exploration. When the user doesn’t know what to ask for yet.
• Follow-up questions. When the initial output needs refinement.
• Edge cases. When the structured interface can’t handle a novel request.

The best AI products use chat as a fallback, not a default. The structured interface handles 80% of use cases. Chat handles the remaining 20%.

Design Principles for Decision-Support AI

1. Lead with the output. Show the result before asking for input.
2. Make it scannable. Cards, tables, and visual hierarchies beat paragraphs of text.
3. Show confidence. Let users see how certain the AI is about each recommendation.
4. Provide escape hatches. Always let users override, edit, or ask “why?”
5. Minimize prompting. Every prompt is a moment of friction. Every button click is a moment of flow.

The Opportunity

Most AI products are still stuck in the chat paradigm because it’s easy to build and impressive to demo. But the products that will win are the ones that take the AI’s capabilities and package them into interfaces designed for specific workflows, specific users, and specific decisions. Chat is a starting point. Decision-support is the destination.