Mike Simmons · Catalyst Book a Clarity Call

Skills > Tools

AI Readiness is not Execution.

Execution is where the results live.

You bought the tool. The CRM, the coaching tool, LLM access. And the team still works the old way.

It looks like a tooling problem. It's usually an execution problem. So we fix the execution: one job, one prototype built on the tool you already have, handed back working.

Start with one job →

One job to be done. A working prototype. Your tool, not ours.

01
Awareness
You see why AI matters. The tools are already in hand.
02
Capability
You build the skill on one real job to be done.
03
Execution
The job runs without you. The work changes.

Readiness is step one. Execution is step three.

95%

95% of enterprise AI pilots deliver no measurable impact on the P&L. Only 5% create real value. The difference is not the model. Everyone has the same tools. The 5% built the skill to use them. Readiness is not execution.

Source: MIT Project NANDA · The GenAI Divide: State of AI in Business 2025

What You're Seeing

You invested in the tool. You expected the behavior to change. It didn't.

So the next move feels obvious: you bought the wrong tool. You start shopping. A better platform. A smarter rollout. Another vendor.

Before you spend again, a hypothesis to test. Not an indictment of you or the team:

The tool may have run ahead of the skill. And nothing around it changed to make the new behavior possible.

What It Usually Is

The order is fixed: mindset, then skillset, then toolset.

A tool only creates leverage when the skills are already there. Buy the tools without the skills, and nothing moves. The tool just sits on top of the old behavior.

First
Mindset

Attitude, Belief, Clarity of Definition - What are we solving for?

Then
Skillset

The capability to get the work done, do the job, built and practiced.

Only then
Toolset

The tool, pointed at work the team is now ready to do.

Readiness assessments, trainings, "are you AI-ready?" That work matters. It builds the foundation: the skills and knowledge your team stands on. And still, readiness is not execution.

And this isn't only how I see it. In Notion's 2026 study of 6,000+ leaders and workers across ten markets, the share of leaders who say they're investing in AI faster than their people can use it climbs as companies mature, from 48% to 68%. The curve gets steeper, not flatter. And at the most advanced level, the skills gap is the single biggest thing still slowing them down, ahead of trust, governance, and integration. The tool got there first. The capability to use it never caught up.

Execution is the team actually working differently. And execution is where the results live. Speed to impact is more important than going fast.

To Leaders

Understand how AI is deployed in your business.

Before the team can execute, you need eyes on how AI actually entered the building. It usually happens one of three ways, and each one carries questions that rarely get asked:

Deployment 1
You upgraded to Copilot.

Great. How was it set up? What databases can it reach? Did IT build anything specific to your business, or check the default boxes? And are you good with that?

Deployment 2
You brought AI on-prem.

Connected to your systems of record, closed to the internet. Is there an AI policy document? An access and security policy? What happens when something goes wrong?

Deployment 3
You approved platforms one by one.

Per team, as requested. How is that budgeted? Which platforms are in, which are out? Where does the data come from, where do results get stored, and who is allowed to build automated agents?

These are a fraction of the questions that need answers before your people can be truly effective with AI. Answering them, writing the policy, training the team on it: that's readiness, and it's real progress. It builds the skills and knowledge foundation your team stands on.

Readiness is part of the path. It just isn't the destination. We take you the rest of the way: from readiness to execution, one job at a time.

What This Looks Like Done Right

I'm the proof.

7
Apps shipped
25+
Custom GPTs
8
Projects

Every one built for a specific job to be done, on tools that were already there. Not a lab. Not a rollout. One job at a time, until the job ran without me. I used AI to fire myself from work me and my team used to do by hand.

One of them is now retired. The job it was built for changed, so I shut it down, took the learning with me, and got the upkeep back. Knowing when to kill a tool is the same skill as knowing when to build one. Most AI stories can only show you what got added.

That's the exact motion we run with your team. Not "get ready for AI." We deconstruct one job, build the thing that does it, and hand it back working.

25 years of doing the work.

UPS Microsoft Intel MIT O'Reilly Disney

Who This Is For

This is for you if either is true.

You have this
You bought the tools and watched nothing change.

You're the one who made the call, signed the contract, and expected different behavior on the other side of it.

You care about the people hit by it
You're watching a team stay stuck.

Working harder for the same result, or quietly burning out, and "buy another tool" isn't going to fix it.

Revenue leader, ops leader, the person responsible for the people. Title doesn't matter. The relationship to the problem does.

AI Readiness to Execution

We build, and fire you out of one job.

Step 1
Deconstruct one job

We take a single job to be done and break it into the decisions and steps underneath it.

Step 2
Build a prototype

On the AI tool you already use. We test whether it can do the job, not whether it demos well.

Step 3
Iterate on feedback

Real work, real edge cases, until it holds up outside the demo.

Step 4
Fire the job

You keep a working solution that takes it off your plate, or the team's.

Start with one job →

We scope the first job on a short call. Your tool, your data, your job to be done.

What You Keep

What you walk away with.

Not a readiness plan. Not a deck. A thing that runs, on the tools you already pay for.

Start with one job →

Currently booking 1–2 weeks out

You don't need another tool. You need the team to execute.

And that starts with one honest conversation about where it's actually stuck.

In the same Notion study, "too many AI tools" is the fastest-rising complaint as companies mature, climbing from 11% to 25%. Another platform was never the fix.
Start with one job →