Do we have a formal AI strategy, or are teams adopting tools independently?

Independent adoption creates tool sprawl, inconsistent data handling, and invisible spend. A formal strategy means shared standards, approved vendors, and visibility into what the organization is actually using.

What is our total AI spend across the organization — including shadow IT subscriptions that individuals are expensing?

Most organizations undercount AI spend by 40-60 percent because individual subscriptions fly under procurement radar. The first step in AI maturity is knowing what you are already paying for.

What governance framework do we have for AI use — and does anyone follow it?

A governance framework nobody follows is worse than none — it creates false confidence. Effective governance is lightweight enough to follow and enforced enough to matter.

Using AI in Your Work

Enterprise AI adoption & maturity

By Mark Ziler · Last updated 2026-04-05

Enterprise AI maturity tracks where your organization is on the adoption curve — from 'a few people use ChatGPT for emails' to 'AI agents run core operations with human oversight.' Most mid-market companies are still in early stages, which is actually an advantage: you can skip the mistakes larger companies made and build AI-ready data foundations from the start. The key is having an honest assessment of where you are before deciding where to invest.

Go deeper

Your board just asked where your organization is on 'the AI maturity curve.' Before you panic and buy a platform, take an honest inventory. How many of your core operational metrics have documented definitions? Is your data in one place or scattered across fifteen systems? Can your managers get answers to basic questions without emailing an analyst? If the answer to any of these is no, you are in the 'data readiness' phase — and that is fine. It means your next investment should be data foundations, not AI tooling. The companies that waste the most money on AI are the ones that skip this assessment.

The trap most companies fall into is benchmarking against enterprises that have 500-person data teams and $20M analytics budgets. Mid-market AI maturity looks different. You do not need a data lake, a dedicated ML ops team, or a custom LLM. You need clean data, clear definitions, and a platform that lets your people ask questions without a technical intermediary. That is the mid-market version of AI maturity — and it is achievable in months, not years.

Questions to ask

Can we honestly describe our current AI maturity stage without using aspirational language?
What is the smallest, most concrete data problem we could solve in 30 days that would demonstrate value?
Are we comparing ourselves to enterprises with 100x our resources, and is that comparison useful or paralyzing?