A Skeptic's Guide to Practical AI Deployment in the Mid-Market

42% of companies are now abandoning the majority of their AI projects before they ever see the light of day — up from 17% just a year prior. The cost isn’t just the sunk investment. It’s the six to twelve months your team spent trying to make it work instead of running the business.

This paper is written for the skeptic. It diagnoses the four traps that sink most AI projects (treating it as an IT project, bad data, trying to do too much at once, ignoring your people), then shows what the winning minority does differently — through three real mid-market case studies:

• AKA Brands used AI-driven SKU rationalization to cut its assortment by 50–75% while maintaining sales, generating $5 million in projected savings.
• mnml replaced spreadsheet-based planning with an AI platform and reduced on-hand inventory by 40%, freeing up working capital without stocking out.
• Stio applied AI to allocation decisions and cut end-of-season send-backs by 30%, protecting gross margin across 100+ store locations.

The paper closes with a four-step getting-started framework: pick one fight, clean your data, run a focused pilot, and put your people first. If you’ve been told you need a data lake and a data science team before you can start, this paper will challenge that assumption.