The AI Gold Rush
It's 2024, and if your product doesn't have AI somewhere in the marketing copy, you're apparently not a serious company. ChatGPT changed everything, and now everyone—from note-taking apps to accounting software—is racing to add "AI-powered" features.
Most of these integrations are, frankly, terrible. Chatbots that hallucinate. "AI summaries" that miss the point. "Smart" features that feel anything but.
We've spent the past two years helping clients integrate AI meaningfully. Here's what actually works.
Principle 1: AI Should Reduce Friction, Not Add It
The best AI features are invisible. They work in the background, making the product faster, smarter, more intuitive—without requiring users to learn new interaction patterns.
Good AI integration:
- Auto-categorizing expenses as you add them
- Pre-filling forms based on context
- Surfacing relevant information before you search for it
- Correcting errors before they cause problems
Bad AI integration:
- Forcing users through a chat interface for simple tasks
- Requiring prompt engineering to get useful results
- Adding AI features that duplicate existing functionality
Principle 2: Transparency Over Magic
Users don't trust black boxes. When AI makes decisions, show your work.
"We categorized this as 'Marketing' because:
- Vendor (Google) is typically advertising
- Amount ($450) matches your usual ad spend
- Timing aligns with your campaign schedule
[✓ Confirm] [Edit Category]"
Explainable AI isn't just an ethical imperative—it builds trust and helps users develop accurate mental models of what the system can and can't do.
Principle 3: Graceful Degradation
AI systems fail. Models hallucinate. APIs timeout. Your product needs to work anyway.
Every AI feature we build has a fallback:
- If classification fails, present options
- If generation seems off, flag for human review
- If the service is down, disable gracefully
The worst AI integrations are those that become single points of failure. Don't let your product break because OpenAI is having a bad day.
Principle 4: Start with the Problem, Not the Technology
"We should add AI to our product" is not a product strategy. Start with user problems:
- What tasks are tedious and repetitive?
- Where do users make predictable errors?
- What information is hard to find or synthesize?
- Where would prediction or recommendation add value?
Then ask: is AI actually the best solution? Sometimes a well-designed dropdown beats a sophisticated classifier.
Principle 5: Measure What Matters
AI features need metrics beyond "cool factor":
- Task completion rate: Does the AI actually help users finish what they started?
- Time to value: Is the workflow faster with AI?
- Error rate: Are AI-assisted actions more or less accurate?
- User trust: Do users accept AI suggestions or override them?
If your AI feature isn't moving these metrics, it's not adding value—it's adding complexity.
The LLM Playbook
For products integrating large language models specifically:
- Constrain the output space — Don't let models free-write when structured data is needed
- Use retrieval augmentation — Ground responses in your actual data
- Implement guardrails — Check outputs before showing users
- Cache aggressively — Similar inputs should return cached results
- Have human review loops — Build feedback mechanisms to improve over time
The Future We're Building Toward
The best AI products don't feel like AI products. They just feel like products that deeply understand you—that anticipate your needs, reduce your cognitive load, and help you accomplish more with less effort.
That's the standard we hold ourselves to. Not "has AI" but "is better because of AI."
Exploring AI for your product? We'd love to discuss what's possible. Get in touch.