Intent Management™

AI Moves Fast.
Leaders Set
the Direction.

Most AI adoption fails quietly — not from bad technology, but from unclear intent. Intent Management™ is the leadership discipline that keeps humans and AI working toward the same outcome.

Get the Book Schedule a Session

Intent Management™ orbital diagram — four elements: Outcome Clarity, Evaluation Criteria, Decision Authority, Constraint Boundaries

The Core Problem

Teams are deploying AI tools without defining what success looks like, who decides when output is good enough, or how to course-correct when the work drifts. That's not a technology problem. It's a leadership problem.

The Discipline

What Is Intent Management™?

Intent Management™ is a structured approach to leading AI-enabled work. It gives leaders a way to communicate expectations clearly enough that AI systems and the humans working with them can produce consistent, high-quality outcomes — without requiring constant supervision.

The framework has four elements. Together they define what good work looks like, who evaluates it, who decides when it's done, and how the team stays calibrated over time. When all four are operating, AI adoption stops being a gamble and starts being a practice.

Developed from research and real-world implementation with leadership teams across industries, Intent Management™ is practical by design. It doesn't require a transformation initiative. It requires leaders willing to be explicit about what they actually want.

Learn the Discipline →

Outcome Definition

Clarity about what success actually looks like before the work starts.

Evaluation Criteria

The specific standards used to judge whether output is good enough.

Decision Authority

Explicit boundaries around who approves, adjusts, or overrides AI output.

Constraint Boundaries

The limits that cannot be traded away regardless of competing priorities.

Self-Assessment

Find Your Breaking Point

When AI-enabled work goes sideways, the cause almost always traces back to one of four places. Eight questions. About sixty seconds. You'll know exactly where to focus.

Question 1 of 8

Question 01

Your Primary Gap

Outcome Definition

Your team is working hard toward a target that hasn't been made explicit.

The most common version of this: everyone understands the general direction, but no one has written down what "done" actually looks like — specifically enough to test. AI tools amplify this problem because they produce output faster than humans can notice the drift.

The fix isn't more communication. It's more precision. Before the next project starts, write down what success looks like in terms that your team could use to evaluate their own work — without checking with you.

Where to Focus First

Before any AI-assisted work begins, write a one-paragraph outcome statement — specific enough that two people would agree on whether the output meets it
Test it by asking a team member: "What would success look like?" If they can't articulate it back, the statement isn't clear enough yet
Review Chapter 3 of Alignment at Speed for the Outcome Definition framework
Try the Week 1 experiment in the First 90 Days guide — one decision, fully written out, before work starts

Outcome Definition is the foundation everything else builds on. Getting it right on one project this week is more valuable than reading about it for another month.

Schedule a Session to Work Through This
Read the First 90 Days Guide →

Your Primary Gap

Evaluation Criteria

Your team knows roughly what good looks like — but can't apply it consistently.

This shows up as: different reviewers applying different standards, long debates about whether output is "good enough," and AI tools getting blamed for quality problems that are really calibration problems. The output isn't wrong — the evaluation framework is missing.

The fix is making your quality standards explicit before output is produced, not after. When the criteria exist only in people's heads, consistency is impossible — and AI tools have no way to orient toward what you actually want.

Where to Focus First

For one current project, write down 3–5 specific criteria that separate acceptable output from unacceptable — concrete enough that two reviewers would reach the same verdict
When options compete, name the trade-off explicitly: speed vs. quality, completeness vs. simplicity — and decide in advance which governs
Review Chapter 4 of Alignment at Speed for the Evaluation Criteria framework
Share the criteria with your team before review, not during it

Most AI quality problems aren't AI problems. They're calibration problems. Getting your evaluation criteria explicit is the fastest path to consistent output.

Schedule a Session to Work Through This
Read the First 90 Days Guide →

Your Primary Gap

Decision Authority

Your team doesn't know who actually gets to say yes — so everything comes back to you.

Decision Authority is the most commonly overlooked element — and the one that creates the most friction when it's missing. When authority isn't explicit, teams default to escalation. Escalation creates bottlenecks. Bottlenecks slow AI adoption to the pace of one person's capacity.

The fix isn't trusting your team more. It's being explicit about what decisions they're authorized to make — and then honoring that authorization even when you would have decided differently.

Where to Focus First

For one current project, write down: what decisions can the team make without me, what requires my input, and what requires escalation beyond me
When you give someone decision authority, mean it — stepping in anyway teaches them they still need your approval
Review Chapter 5 of Alignment at Speed for the Decision Authority framework
Check whether your team believes they're allowed to use the authority you've given them — sometimes explicit permission needs to be restated

If you're the bottleneck for most decisions in an AI-enabled workflow, the constraint isn't the AI. It's the authority structure. That's fixable in a single conversation.

Schedule a Session to Work Through This
Read the First 90 Days Guide →

Your Primary Gap

Constraint Boundaries

Your team is operating without a clear map of what's off-limits.

Constraint Boundaries are the non-negotiable limits — regulatory requirements, budget ceilings, authority structures, organizational policies — that govern what AI-enabled work can and cannot do. When they're not explicit, teams discover them the hard way: by crossing them.

This is especially costly with AI tools, which can produce output that violates constraints efficiently and at scale. The fix is mapping the limits before work starts, not after the violation.

Where to Focus First

For one current project, name 3–5 things that would make the output unacceptable regardless of quality — regulatory limits, cost ceilings, stakeholder sensitivities
Write constraints down as explicit rules, not informal understandings — "we don't" is not a constraint; "outputs may not include X" is
Review Chapter 6 of Alignment at Speed for the Constraint Boundaries framework
Share constraints with the team and with any AI tools or prompts being used — if the constraint isn't in the system, the system will ignore it

Constraint Boundaries are the limits that can't be traded away. Getting them explicit before work starts is the difference between a preventable violation and one you're managing after the fact.

Schedule a Session to Work Through This
Read the First 90 Days Guide →

Your Primary Gap

Communication Cadence

Your intent was clear when you set it. It's drifted since then.

Communication Cadence is what keeps the other three elements calibrated over time. Intent drifts — priorities shift, conditions change, and AI-enabled work continues in the old direction until someone notices. The gap between when intent was set and when it gets refreshed is where most accumulated rework lives.

The fix isn't more meetings. It's structured checkpoints — deliberate moments where the team asks whether the original outcome, criteria, and constraints still apply given what's changed.

Where to Focus First

Build a lightweight intent review into your existing rhythm — weekly or at project milestones — specifically asking: does our original outcome still apply?
When conditions change significantly, treat it as a trigger to explicitly revisit intent rather than assuming the team will adjust on their own
Review Chapter 7 of Alignment at Speed for the Communication Cadence framework
Read the Beyond 90 Days guide for how to sustain the discipline as conditions evolve

Most teams set intent once and assume it holds. It doesn't — especially with AI tools moving fast. Building the cadence to refresh it is what separates a one-time practice from a lasting discipline.

Schedule a Session to Work Through This
Read the Beyond 90 Days Guide →

The Books

Read the Work

New Release

Alignment at Speed

Leading AI-Enabled Teams

AI is changing how work gets done — but most leadership frameworks were built for a world where humans produced every output. This book provides a practical model for leading when AI is doing significant portions of the work.

Alignment at Speed walks through the Intent Management™ discipline in full: how to define outcomes precisely, how to set evaluation criteria your team can actually use, how to structure decision authority in human-AI workflows, and how to keep communication tight enough that intent doesn't drift.

Written for leaders who are already dealing with AI adoption and need a framework that works in the real world — not a theory built for a future that hasn't arrived yet.

Buy on Amazon Book a Workshop See It in Practice →

Also by Keith Johnson

Shifting Into Leadership

Daily Habits that Build Clarity and Trust. The field guide for new managers navigating the shift from individual contributor to team leader. Practical habits, hard truths, and a clear path forward for the first ninety days and beyond.

Buy on Amazon →

Work With Me

Engagements

Every engagement is built around one goal: helping leadership teams get clear on their intent so AI adoption delivers real results. Available as keynotes, workshops, and working sessions.

Keynote

A high-impact session on Intent Management™ for leadership teams, all-hands meetings, or industry conferences. Adaptable from 30 minutes to a full hour.

Workshop

A working session where teams apply the four-part discipline to their actual workflows. Half-day or full-day format. Results leave with the team, not the facilitator.

Advisory Session

A focused 30- or 60-minute working session for leaders navigating a specific challenge around AI adoption, team alignment, or AI governance design.

Schedule a Conversation

In Practice

Intent Management™
in the Real World

How organizations across industries apply the discipline when AI-enabled work gets complex and the stakes for getting it wrong are high.

Leader standing still while the world moves fast

Medical Devices

When Tolerance Is Not a Number

A hearing aid manufacturer was 18 months into development when regulatory, hardware, and AI research teams were all working toward different versions of "done." Here's how that got resolved before it became a missed submission date.

Read the case study →

Automotive

The Right Answer Was Already There

Three teams building the same EV display platform from different directions for 14 months — until an AI design tool produced 40 non-compliant UI variants six weeks before integration lockdown.

Read the case study →

Enterprise HR Technology

Six Systems, One Source of Truth

Four executive sponsors co-owning an HRIS consolidation had never established whose requirements governed when they conflicted. An AI architecture tool was recommending integrations the CISO would have rejected.

Read the case study →

Hospitality & Construction

The View From the Water

A fixed budget, a set opening date, and a historic preservation society whose requirements the AI scheduling and cost estimation tools didn't know about. The scheduling tool was protecting the wrong variable.

Read the case study →

View All Case Studies

Insights

From the Blog

May 2025

Why AI Adoption Fails Without Clarity

Most organizations deploy AI tools before they can answer a basic question: what does good output look like? When that definition is missing, teams spend more time correcting AI work than benefiting from it.

April 2025

The Human Side of AI Adoption

Resistance to AI is rarely about the technology. It's about identity, autonomy, and trust. Leaders who understand that get much further than those treating it purely as a change management exercise.

March 2025

Which Element of Intent Management™ Is Breaking Down?

When AI-enabled work goes sideways, the cause is almost always one of four things. Knowing which one tells you exactly where to focus the fix.

View All Posts

Ready to Lead with Intent?

Whether you want to read the book, bring a workshop to your team, or talk through a specific challenge — the best place to start is a conversation.

Schedule a Session

AI Moves Fast. Leaders Set the Direction.

What Is Intent Management™?

Outcome Definition

Evaluation Criteria

Decision Authority

Constraint Boundaries

Find Your Breaking Point

Your team is working hard toward a target that hasn't been made explicit.

Where to Focus First

Your team knows roughly what good looks like — but can't apply it consistently.

Where to Focus First

Your team doesn't know who actually gets to say yes — so everything comes back to you.

Where to Focus First

Your team is operating without a clear map of what's off-limits.

Where to Focus First

Your intent was clear when you set it. It's drifted since then.

Where to Focus First

Read the Work

Alignment at Speed

Shifting Into Leadership

Engagements

Keynote

Workshop

Advisory Session

Intent Management™in the Real World

When Tolerance Is Not a Number

The Right Answer Was Already There

Six Systems, One Source of Truth

The View From the Water

From the Blog

Why AI Adoption Fails Without Clarity

The Human Side of AI Adoption

Which Element of Intent Management™ Is Breaking Down?

Ready to Lead with Intent?

AI Moves Fast.
Leaders Set
the Direction.

Intent Management™
in the Real World