Empowering the AI-native executive
AI-native systems are led
by AI-native leaders.
We put AI to work in your own week, read where every team stands, and plan the next step with your owners, so AI shows up in the work and in the results.
45% of leaders who use AI day to day in their own work lead an AI-mature organization, against 19% of those who rarely or never do. BCG, September 2026
Hi, I’m Albert.
HALDEMAN’s AI assistant. How can I help you today?
Albert · AI assistant
How we help
Three ways in, in any order.
From your own Monday to every team you lead.
AI training
Your own work, led from the front. Monday Morning AI works one to one, or with up to six leaders, so your team sees you lead it before you ask it of them.
Your teams02AI maturity
Where each team stands, and the one constraint holding the rest back. Three to five insights your owners act on, with a score out of 100 behind them.
The work03AI roadmap
Your roadmap to AI-native, one step up at a time. Ninety days with owners and gates, run by your team.
Our view
AI-native has a bar, and every team you lead can meet it.
- Absent
- Individual
- Pockets
- Standard
- Managed
- Improving
Individual and Pockets are real progress. Standard is the bar, with Managed and Improving above it.
We read readiness against this bar: where each organization is ready for AI, and where it isn't yet.
Hover any line for the detailTap any line for the detail
Read this figure
- 0 · Absent
- Nothing is there
- 1 · Individual
- Exists as personal effort; it leaves when the person does
- 2 · Pockets
- Some groups have it; no shared standard
- 3 · Standard
- Defined, written, and actually followed across the organization
- 4 · Managed
- Measured against targets and reviewed on a cadence
- 5 · Improving
- The system upgrades itself; what is learned becomes the new standard
Ask Albert
Start with the decision in front of you.
Albert answers the way we work: the question to ask, the evidence to look for, and one first move.
Constructed example
A CFO with an AI renewal on the table.
- The question to ask
- What changed in the work because of the tool, not how many people log in.
- The evidence to look for
- One workflow with a named owner and a measure that moved.
- The first move
- Before the vendor meeting, agree in writing what result would justify renewing.
- Where we come in
- If you need that read across every team, that is our AI maturity read.
Notes
Short arguments on making AI pay off.
Logins measure access. Changed work predicts results.
The cost of waiting belongs in every AI decision.Doing nothing has a price. Put it on the page.
Agree on the evidence before debating the number.Set the rules first. The argument gets smaller.
An AI-native executive leads from the front.It’s a practice, not a title. Your teams can see it.
Find the changed work before you count the return.A result counts when the work behind it changed. Say how firm it is.
Renew where the work changed.Agree what would justify renewing before the numbers arrive.
Let the constraint set the order, not the biggest number.Two teams, two AI priorities. Agree the rule before you pick.
Let’s get you there.
Start with a conversation about where you and your teams stand today.