The Practical Guide to AI Automation for B2B Operations
Cut through the AI hype. A senior engineer's perspective on what actually works for automating B2B workflows.
The AI Hype vs. Reality
Every other LinkedIn post promises AI will revolutionise your business. Most of it is noise. After building AI systems for B2B companies over the past two years, here's what I've learned about what actually works.
Where AI Automation Actually Delivers
1. Data Processing & Enrichment
AI excels at processing unstructured data. We've built systems that:
These aren't sexy demos—they're production systems handling thousands of records daily with 90%+ accuracy.
2. Research & Summarisation
LLMs are genuinely good at synthesising information. Useful applications:
The key is tight scope. Don't ask AI to "research everything about a company." Ask it to extract specific fields from specific sources.
3. Workflow Automation with Human Oversight
The best AI automations include human checkpoints. Examples:
Where AI Falls Short
Complex Reasoning
Despite the hype, current LLMs struggle with multi-step reasoning, especially with novel problems. They're pattern matchers, not reasoners.
Consistency
Same prompt, different outputs. For business-critical processes, you need validation layers and fallback logic.
Cost at Scale
API costs add up quickly. At 10,000 requests/day, you need to think carefully about when AI is the right tool vs. traditional programming.
Building for Production
If you're building AI automation, plan for:
The Bottom Line
AI automation works when you:
1. Target specific, well-defined tasks
2. Accept that it's probabilistic, not deterministic
3. Design systems around AI's limitations
4. Measure ROI like any other engineering investment
The best AI systems aren't magic—they're thoughtfully engineered solutions to real problems.
Need help identifying AI automation opportunities in your operations? Book a call to discuss.