Practical perspectives on where AI creates value, where it fails, and how mid-market companies build durable capability.
A five-part series for mid-market leaders. A practical framework for finding, scoring, building, and deploying AI where it actually pays back — without the Fortune 500 budget or 12-month transformation.
For Leaders at mid-market companies
Most stalled AI initiatives aren't a model problem. They're missing one of three elements — clear instructions, the right context, or the ability to actually act. Three yes/no questions reveal which one in under an hour.
Gartner predicts 40% of agentic AI projects will be canceled by 2027. The problem isn't AI — it's complexity mismatch. A four-tier ladder and the principle that prevents over-engineering: only graduate up when you've proven the lower tier doesn't work.
Klarna replaced 700 customer service agents with AI, then walked it back. The lesson isn't that AI failed — it's that the workflow was the wrong unit of analysis. Here's how to decompose work so AI handles what it does well, while humans stay on the parts that matter.
82% of employees use unsanctioned AI at work. Most companies treat it as a security problem. It is — but it's also the best free market research a CEO will ever get, if you know how to separate signal from noise.
Lumen created $50 million in annual value with AI. Air Canada ended up in a tribunal. The difference wasn't the technology. It was the playbook. Here's the framework mid-market companies actually need.
