AI Automation for Business: Where to Start and What to Avoid
A practical playbook for identifying high-ROI automation opportunities, choosing the right LLM stack, and avoiding the costly mistakes most companies make.
AI Automation for Business: Where to Start and What to Avoid
Every founder in 2025 is asking the same question: "Where should we use AI?" Most of them are asking the wrong question. The right one is: "What work is our team doing that a model could do 80% as well, but 100x faster?"
Start with the boring problems
The highest-ROI AI automations are usually the least exciting ones:
- Sorting and routing incoming emails
- Extracting data from invoices and receipts
- Drafting first-pass responses to common customer questions
- Summarizing long documents for executives
- Categorizing support tickets
These aren't glamorous, but they free up hours every week. Start here.
The 80% rule
Don't try to build AI that's 100% accurate. Build AI that's 80% accurate, then design a workflow where a human reviews the output in 30 seconds. This is dramatically cheaper than trying to push accuracy from 80% to 95%, and the human-in-the-loop catches the edge cases anyway.
Build or buy?
For most use cases, buy. OpenAI, Anthropic, and dozens of SaaS tools have done the hard work. You don't need to fine-tune a model. You need to integrate an API into your workflow.
Build custom only when:
- Your data is highly sensitive and can't leave your infrastructure
- You've already tried off-the-shelf and hit clear limits
- The AI is your product, not just a feature
Common mistakes to avoid
1. Treating AI as a magic box. It's not. It's a statistical pattern matcher. If your training data is bad, your output will be bad. Garbage in, garbage out still applies.
2. Skipping evaluation. Before shipping, build a test set of 50-100 examples and measure accuracy. Without numbers, you're guessing.
3. Ignoring latency. A 10-second response feels broken. Stream responses, cache aggressively, and show progress indicators.
4. No fallback. When the AI fails (and it will), what happens? Have a graceful path to a human, every time.
A simple playbook to get started
- List 10 repetitive tasks your team does weekly
- Pick the one with the highest time cost and clearest success criteria
- Prototype with ChatGPT or Claude manually first — no code
- If the prototype works, build a thin integration
- Measure time saved weekly. Iterate.
We help companies identify and ship AI automations that actually move the needle. Tell us what you're trying to automate.