The Three Levels of AI Adoption

I keep having the same conversation with different executives. They ask some version of: "We're using AI, but I'm not sure we're getting everything we could out of it. What are we missing?"

The answer usually becomes clear once I understand which level they're operating at. Organizations tend to adopt AI at one of three levels, and most are stuck at the first one.

Level 1: Point Solutions

This is where most teams start. They plug AI into a specific task without changing anything else around it. An AI bot for support tickets. Automatic meeting notes. Smart routing.

It's quick, low-risk, and the ROI is usually real but linear. You're shaving hours, not redefining anything.

You can spot Level 1 companies because when someone says "we're using AI," they can only point to a handful of discrete tools. The org chart hasn't changed. The workflows are the same. The headcount hasn’t dramatically shifted. AI is something they purchased, not something that changed how they operate.

Level 2: Platform Solutions

At this level, teams stop thinking in isolated tasks and start rethinking an entire function.

Instead of an AI bot answering questions, you get an AI-powered customer support platform that triages, drafts responses, predicts sentiment, routes intelligently, and automates the 80% of work humans shouldn't be doing in the first place.

Workflows change. Roles shift. The return has the potential of becoming nonlinear (if the company really leans in). At this level, AI isn't just replacing a task. Rather, it is reshaping how the company performs an entire function.

The pattern shows up in the org chart. Job descriptions start changing. New roles emerge. Old roles either expand in scope or disappear entirely.

Level 3: System Solutions

At this level, a company stops asking "How can AI help our support team?" and starts thinking bigger… "What would world-class support look like if we re-designed it from the ground-up with AI at the center?"

The answers almost always break the constraints of the old model.

Consider: customer support today is reactive because humans are expensive. You wait for a customer to reach out to you, and then you respond. It’s entirely reactive. But what if you had a 1:1 ratio of support agents to customers? Your "agent" could monitor real-time usage signals, which would allow them to proactively identify needs before the customer even reaches out. 

Example: someone struggling to log in can be obvious in an activity log, which can then trigger an AI support agent to proactively send a reset link before the customer even gets frustrated and has to reach out to you.

The trick at this level is knowing how to implement this level of automation without it feeling “creepy” to the customer or leaving them thinking “they must really be tracking my every move…”

In an AI-native world, that level of proactive support is not only possible… It's cheap!

System-level AI means reimagining an entire function (or the entire business) with the assumption that intelligence is abundant, always-on, and infinitely scalable. The complexity is higher. The organizational friction is real. But entire cost centers can become value engines.

You recognize Level 3 companies because their competitive advantage becomes hard to explain without referencing AI. It's not a tool they use. It's how they operate. They fundamentally are changing the unit economics, scalability, customer experience, etc for their business.

Where Companies Get Stuck

Many teams want the outcomes of a Level 3 approach but try to get there using Level 1 tactics. They duct-tape point solutions together and wonder why it doesn't feel transformative.

The companies making real progress treat AI the same way they'd treat any major technological shift: start with clear goals, understand the current constraints, then design from first principles. You can't bolt your way into an AI-first operating model.

The Gap Is Widening

We're early in the adoption curve, but the distance between levels is already visible. Companies stuck at Level 1 will look increasingly slow and expensive. Those operating at Level 3 will look almost unnaturally efficient and cheap.

The question worth asking: Which level is your organization actually operating at, and what would it take to move up?