Adding AI Features to Existing Products: A Practical Guide

You don't need to rebuild your product around AI. Here's how to add AI features to what you already have, starting with the ones that actually help.

The AI gold rush has convinced a lot of companies they need to "become an AI company." They don't. They need to be the same company, with a few AI features that make their product better. The distinction matters because the first approach leads to expensive rewrites and the second leads to shipped improvements.

Start with the tedious stuff

The best first AI feature isn't the flashiest one - it's the one that eliminates the most manual work. Data entry, categorization, summarization, and content generation are all tasks where AI is good enough to save real time right now. For Careit, the first AI feature we built was photo recognition that auto-fills item descriptions. It turned a 2-minute data entry task into a 5-second photo.

The integration is harder than the AI

Calling the Claude API takes five lines of code. Building the UI, handling errors, managing costs, dealing with latency, caching responses, and handling edge cases - that's the real work. Plan for it. The API call is 10% of the effort; the integration is 90%.

Patterns that work

• Auto-fill: pre-populate form fields with AI suggestions the user can edit
• Summarize: condense long documents, conversations, or reports into key points
• Classify: automatically tag, categorize, or route incoming data
• Search: add natural-language search over your existing content and data
• Generate: draft emails, reports, descriptions, or other structured content

Cost and latency

LLM API calls cost money and take time. For user-facing features, latency matters - stream the response if it takes more than a second. For batch processing, cost matters - choose the cheapest model that produces acceptable quality. Don't use GPT-4 class models for tasks that GPT-3.5 class models handle fine.

Ben Arledge

CEO & CTO, CloudOwl