The AI hype cycle has mid-market leaders asking: should we be doing something? This article provides a pragmatic framework for evaluating AI opportunities and avoiding expensive mistakes.
The AI FOMO Problem
Every conference, every LinkedIn post, every vendor pitch: AI will transform your business. But for mid-market companies without dedicated data science teams, the path forward is murky.
The enterprise playbook—hire 50 data scientists, build custom models, spend millions—doesn't translate.
A Pragmatic AI Framework
Instead of asking 'how do we implement AI?', ask these questions:
1. What decisions are we trying to improve?
AI is valuable when it improves decisions at scale. If your sales team makes 100 pricing decisions daily, AI can help. If your CEO makes 3 strategic decisions quarterly, probably not.
2. Do we have the data to support it?
AI needs data. Lots of it. Clean data. If you're still reconciling spreadsheets, start there.
3. What's the ROI timeline?
Beware projects promising transformation in 18 months. Look for wins in 3-6 months.
Where Mid-Market AI Actually Works
Based on dozens of projects, here's where we see consistent ROI:
- Demand forecasting: Reduce inventory costs 10-20%
- Churn prediction: Identify at-risk customers before they leave
- Document processing: Automate invoice and contract handling
- Customer segmentation: Personalize without manual analysis
The Build vs. Buy Decision
For most mid-market companies, the answer is buy (or partner). Custom AI development requires expertise you probably don't have in-house—and don't need permanently.
Focus your internal team on understanding the business problem. Let specialists handle the technical implementation.