AI Hype vs. AI Reality
Everyone's talking about AI. But for most businesses, the question isn't whether AI is impressive — it's whether it actually solves a problem worth solving.
Here's what we've learned building AI-powered automations for mid-market companies.
Where AI Genuinely Helps
Document Processing
AI excels at extracting structured data from unstructured documents. Invoices, contracts, emails — AI can read them and pull out the relevant fields with high accuracy.
Example: A client receives hundreds of vendor invoices monthly. AI now extracts the key data and creates records in Salesforce automatically. Human review only happens for edge cases.
Lead Classification
AI can analyze incoming leads and route them appropriately. Not just basic criteria like company size, but nuanced signals like intent, urgency, and fit.
Example: Analyzing email content and behavior patterns to score leads before they even talk to sales. The AI catches signals that would take humans hours to compile.
Content Generation
Drafting emails, summarizing meeting notes, creating first drafts of proposals — AI is a good writing assistant when you need volume with personalization.
Example: Auto-generating follow-up emails based on call notes, with personalized content that would take a rep 15 minutes to write manually.
Where AI Falls Short
Replacing Judgment
AI can inform decisions, but it shouldn't make them. Especially for anything customer-facing, human oversight matters.
Complex Business Logic
If you can write down the rules, you don't need AI. Traditional automation is faster, cheaper, and more predictable for well-defined processes.
Small Data Problems
AI needs training data. If you only have 50 examples, you're better off with rule-based systems.
The Right Approach
We recommend starting with traditional automation for your core processes. Get the foundations solid first.
Then layer in AI for specific tasks where:
- 1The problem is fuzzy or unstructured
- 2You have enough data to train/validate
- 3The cost of errors is manageable
- 4Human oversight is built in
Getting Started with AI Automation
Don't start with "we should use AI." Start with "what problem are we trying to solve?"
A Discovery Sprint helps you identify which problems are AI-shaped and which are better solved with traditional automation.