Everyone wants to add AI to their product now. Some of those projects will create real value. Most of them will be expensive features that users ignore. Here's how to tell the difference before you spend the money.
When AI is worth it
AI works best when it does one of three things: processes information that humans can't process at scale, makes decisions that follow patterns in data, or generates content that would take humans significantly longer to create.
Search and discovery. If your product has a lot of content and users need to find specific things, semantic search (powered by embeddings) dramatically outperforms keyword search. This is one of the highest-ROI AI integrations we've seen.
Data summarization. If ymy users deal with large volumes of text — support tickets, documents, research papers, meeting notes — AI summarization saves real time. The key is that the summaries need to be accurate and the source material needs to be accessible for verification.
Content generation with guardrails. AI-generated first drafts of emails, descriptions, reports, or analysis can save significant time — but only when humans review and edit the output. Fully automated content generation without review is a liability, not a feature.
Anomaly detection. If you have time-series data (usage metrics, financial transactions, sensor data), AI can spot patterns and outliers that humans miss. This is one of the most mature and reliable AI applications.
Classification and routing. Automatically categorizing incoming requests, tickets, or documents based on their content. This works well when categories are well-defined and training data exists.
When AI is not worth it
Chatbots that replace search. If users can find what they need with a good search bar and clear navigation, a chatbot adds complexity without value. Chatbots are the most over-deployed AI feature we see.
AI for simple automation. If the logic can be expressed as if/then rules, you don't need AI. Rule-based automation is cheaper, more predictable, and easier to debug. AI should handle the cases where rules break down, not replace rules that work.
Personalization without enough data. AI-powered personalization requires meaningful user data. If you have 100 users, a hand-curated experience will outperform any algorithm. Personalization AI needs scale to work.
Features that feel impressive in a demo but don't solve a real problem. I've seen teams build amazing AI demos that nobody uses because the underlying problem wasn't painful enough. The AI is cool, but cool doesn't drive adoption.
How to evaluate an AI feature
Before building any AI feature, answer these three questions:
What human task does this replace or augment? If you can't point to a specific task that takes real time and effort, the AI feature is a solution looking for a problem.
What happens when the AI is wrong? Because it will be wrong. Every AI system has a failure rate. The question is whether wrong outputs are annoying (AI suggests a bad email subject line) or dangerous (AI misclassifies a financial transaction). Design for the failure case, not just the happy path.
Is the accuracy bar achievable? Some tasks need 99.9% accuracy to be useful (medical diagnosis, financial compliance). Others are valuable at 80% (content suggestions, search ranking). Know your accuracy bar before you build.
The integration approach
If you've validated that an AI feature is worth building, here's how I approach the integration:
Start with the API
For most AI features, you don't need to train a model. Start with a pre-trained model via API (OpenAI, Anthropic, etc.) and see if it solves the problem well enough. Custom models are expensive to train and maintain — don't go there unless the off-the-shelf models genuinely can't do the job.
Build the pipeline, not just the prompt
A prompt that works in a playground is 10% of the work. The other 90% is:
- Input validation and preprocessing
- Context retrieval (RAG) if needed
- Output parsing and validation
- Error handling and fallbacks
- Caching to control costs
- Monitoring and logging
- Rate limiting and usage controls
This is the engineering that makes AI features production-ready. It's also the part that most demos skip.
Design for cost
AI API costs add up fast. A feature that costs $0.01 per request doesn't sound expensive until you have 10,000 daily active users making 5 requests each. That's $500/day — $15,000/month for one feature.
Control costs from the start:
- Cache aggressively. If the same question gets asked repeatedly, cache the answer.
- Choose the right model size. You don't always need GPT-4 — smaller, faster models work fine for many tasks.
- Batch requests when possible.
- Set usage limits and monitor spend daily.
Monitor everything
AI systems behave differently in production than in testing. Monitor:
- Response quality (sample and review outputs regularly)
- Latency (AI calls are slow — measure impact on user experience)
- Cost per request and total spend
- Error rates and failure modes
- User engagement with AI features (are people actually using them?)
Common mistakes
Building AI features because competitors have them. "They have AI, so I need AI" is not a product strategy. Build AI features that solve real problems for ymy users, not features that look good in a press release.
Underestimating maintenance. AI features aren't set-and-forget. Models update, APIs change, costs fluctuate, and output quality drifts over time. Budget for ongoing monitoring and maintenance.
Not designing the fallback. What happens when the AI is down or returns garbage? Your product should degrade gracefully, not crash. Every AI feature needs a non-AI fallback path.
Skipping the evaluation step. Before building the full feature, run a small test. Feed real data through the model and evaluate the output quality. If the model can't handle your use case in a controlled test, it won't work in production.
The bottom line
AI integration is a tool, not a strategy. The best AI features are invisible — they make existing workflows faster and smarter without asking users to change their behavior. The worst AI features are bolted-on chatbots that add complexity without value.
If you're considering adding AI to your product, start with the problem, not the technology. Define the task, evaluate whether AI is the right solution, and build with reliability and cost-awareness from day one.
I help teams integrate AI into their products the right way. If you have a use case in mind, I'll give you an honest assessment of whether it's worth building.