Summary / Net
Most senior executives do not have a productivity problem. They have an overload, attention, energy, relationship, and prioritization problem. The CHU Management System — CHUMS — is my personal operating system for getting shit done without letting the loudest, most urgent, or most emotionally reactive issue take over my life.
CHUMS is not a software platform, productivity app, or universal template. It is a weekly paper-and-Google-Sheet discipline I use to manage work, family, relationships, health, values, commitments, follow-up, and focus. The specific system is mine. The larger point is universal: every serious leader needs a system.
Why I Built CHUMS
I originally developed what became the CHU Management System during the seven years I founded and led The Loyalty Group, that created, launched and grew the AIR MILES Canada shopping reward program and database/ internet marketing business. Like many entrepreneurs and CEOs, I had too many priorities, too many people depending on me, too many ideas, too many meetings, too many relationships to maintain, and too many chances for something important to fall through the cracks.
Later, in 2016, I began using the system again while working at Year Up, where my work involved roughly 60 internal colleagues, more than 100 external partners, and 12 locations. That was the kind of operating environment where “I’ll remember that” is not a system. It is a fantasy. It also served me well when I used it in 2020 as Vice Chair of Alan Khazei’s congressional campaign in Massachusetts 4th District. I have described well-funded competitive political campaigns are like an “internet start-up on speed.” Personal management systems are a life saver in these environments.
CHUMS is heavily influenced by Stephen Covey’s The 7 Habits of Highly Effective People, David Allen’s Getting Things Done, Jim Loehr and Tony Schwartz’s The Power of Full Engagement, James Clear’s Atomic Habits, conversations with friends and colleagues, and a lot of agile trial and error. It is also influenced by a simple, humbling reality I have learned repeatedly in my life: when I use my system, things get done. When I don’t, things fall off my plate, and I drift into a growing pile of “urgent and important” crises.
The “triple-infinity” symbol represents the need for leaders to keep the most important things, actions, tasks, and priorities among your business, your family, and yourself in balance. This was something emphasized for me personally during the many years I was a member of The Young Presidents Organization, while CEO of The Loyalty Group in Toronto.
The name is slightly tongue-in-cheek: CHUMS stands for CHU Management System. But the point is serious. CHUMS is a personal operating system for self-leadership.
And for senior executives, self-leadership is not optional. If you cannot manage your own attention, energy, values, relationships, commitments, and follow-up, you will eventually transfer your internal disorganization to the organization you lead.
The Big Idea: Senior Executives Need a Personal Operating System
Most executives already have organizational systems. They have strategic plans, dashboards, CRM systems, board packets, financial models, Slack channels, calendars, and performance reviews. But many do not have an equally disciplined personal system for deciding what deserves attention this week, what must happen today, who needs care and connection, what values must be lived, and what can safely wait.
That gap is dangerous.
Without a personal operating system, leaders become reactive. They chase whatever is loudest. They answer the latest email. They respond to the most anxious stakeholder. They spend too much time in Covey’s “urgent and important” quadrant and not enough time on the important-but-not-yet-urgent work that actually builds a life, a team, a strategy, and an institution.
CHUMS is my attempt to solve that problem for myself.
It has 11 parts:
- Daily and weekly self-monitoring
- Personal must-dos
- Weekly to-do list
- Strategic priorities and relationships
- Calendar blocking
- Must do today before I sleep
- Rolling discussion lists, house list, and someday/maybe
- Live your values
- Feedback is a gift
- Weekly must-do / three priorities per day
- The 86,400 reminder
Here is how each part works.
1. Daily and Weekly Self-Monitoring
What is it?
The first section of CHUMS is a simple weekly tracker. It includes the people and practices I want to stay connected to each week.
For me, the initials and abbreviations on the left-hand side are personal:
J = Jordan, my oldest child.
M = Myles, my youngest child.
LEZ = my wife, Laura.
X = exercise.
S/G = spiritual practice and gratitude.
SSU = my sister Sharon.
CUF = my sister Cecilia.
This section is influenced by Stephen Covey’s “Sharpen the Saw” habit — the idea that we must renew ourselves across the physical, mental, emotional/social, and spiritual dimensions if we want to remain effective.
How do I use it in practice?
Each week, I track whether I have engaged with these people or practices. Did I connect with my kids? Did I connect meaningfully with Laura? Did I exercise? Did I do anything spiritual or gratitude-oriented? Did I check in with my sisters?
This is not meant to be a guilt machine. It is a visibility tool.
A senior executive can easily spend the week serving clients, employees, donors, investors, board members, political allies, or external stakeholders — and then realize that the people and practices that matter most personally received the least intentional attention.
This section makes that visible.
Why include it?
Because what gets tracked gets attention.
Executives are usually very good at tracking business metrics. Revenue. Margin. Retention. Pipeline. Fundraising. Cash runway. Employee engagement. Customer satisfaction. But we often fail to track the personal relationships and renewal practices that keep us grounded and human.
This section reminds me that productivity is not just doing more work. It is living the right life while doing the work.
If I say my family matters, my system should reflect that. If I say health matters, my system should reflect that. If I say spirituality, gratitude, or reflection matters, my system should reflect that.
Otherwise, my stated values and my calendar can be in conflict.
2. Personal Must-Dos
What is it?
This is the short list of the most important personal things I must get done this week.
Not someday. Not eventually. This week.
These are not necessarily the biggest strategic projects. They are the personal commitments that matter enough to deserve explicit weekly attention.
How do I use it in practice?
When I update CHUMS on Sunday night, I ask: what personal things must happen this week for me to feel I have honored my commitments to myself and the people closest to me?
That might include a family follow-up, a personal finance task, a health appointment, a home issue, a difficult conversation, a commitment to Laura, a call with one of my kids, or something I have been avoiding.
The key is that it gets written down in a place I will see all week.
Why include it?
Because personal obligations do not become less important because they are not professional.
Many senior leaders unconsciously privilege work commitments over personal commitments because work commitments come with meetings, deadlines, assistants, board pressure, customers, and money attached. Personal commitments are often quieter. They do not always send calendar invites.
But neglected personal commitments create stress, guilt, friction, and emotional drag. They also erode integrity. If I repeatedly fail to do what I told myself or my family I would do, I become less trustworthy to myself.
This section protects my personal credibility.
3. Weekly To-Do List
What is it?
This is the classic weekly capture list, heavily influenced by David Allen’s Getting Things Done. Allen’s GTD method emphasizes capturing what has your attention, clarifying what it means, organizing it where it belongs, reviewing it regularly, and then engaging with the right work. Allen advises against keeping a long daily to-do list, as spending time on most nights moving things that did not get done that day to the next can become energy sapping.
In CHUMS, this becomes a weekly written list of work and personal to-dos.
How do I use it in practice?
The list is organized along the right-hand side of the matrix roughly by my most important clients, projects, and people. CHUDOW – stands for CHU (to) Do Other Work related and CHUDOP – for CHU (to)Do Other Personal. IC/ AI is a bucket to make sure I build intellectual capital every week and AI to remind me to keep up with the latest developments in artificial intelligence.
The point is to avoid what I call the “four-year-old soccer problem”: every kid chases the same ball.
Organizations do this. Leaders do this. I do this. The urgent issue gets the ball, and suddenly every person, meeting, and ounce of energy is chasing it while other important areas are neglected.
During the week, whenever something comes up, I write it down. I do not rely on memory. I do not assume I will magically remember it after the meeting, after the bike ride, after walking Izzy, or after the next call.
On Sunday night, I update the system in Google Sheets, print it, and carry it with me. Yes, I am trying to eliminate paper from much of my life. But for this, paper still works. I can fold it in half and keep it in my jacket pocket. A few more folds and it fits in my jeans or cycling jersey pocket. If something occurs to me while walking the dog, riding my bike, sitting in a meeting, or thinking through a client issue, I can write it down quickly and get it out of my head.
Why include it?
Because the brain is a terrible office.
It is wonderful for creativity, empathy, pattern recognition, judgment, and strategy. It is not wonderful as a storage facility for 73 unrelated commitments.
David Allen’s core insight is that unprocessed commitments create mental residue. The more open loops we carry in our heads, the less fully present we are for the task or person in front of us.
For senior executives, this matters enormously. Your attention is one of the most valuable assets in the organization. If your mind is cluttered with unrecorded obligations, you are spending executive attention on memory management instead of judgment.
Writing things down is not clerical. It is strategic.
4. Most Important Strategic Relationships and Priorities
What is it?
The last two columns force me to list 1-3 relationships in appropriate areas of focus that I must invest in during the week and to whittle down the many to-do items under each to the 1-3 that simply must get done by the end of the week.
How do I use it in practice?
I use the categories to force a broader scan. The most important relationships and priorities in each category ensure that I narrow down and prioritize those actions and people that are most important for the period.
What needs attention in each major area? What am I ignoring? Which relationships need follow-up? Which strategic priority is quietly drifting? Which area has become too dominant? Which important work is not urgent enough to scream yet?
This section turns my week from a random pile of tasks into a portfolio.
Why include it?
Because executive work is portfolio work.
A CEO or senior leader is rarely responsible for one thing. You are responsible for strategy, people, capital, culture, customers, operations, governance, risk, external relationships, and often your own health and family system at the same time.
If you only manage the loudest category, you will underperform in the quieter ones.
This section creates balance. It forces me to ask: am I paying attention to the whole field, or am I chasing the ball?
5. Calendar Blocking
What is it?
I use this section to block out my calendar, identify major client and board meetings, presentations, and color-code travel dates. It gives me a high-level overview of the most important personal and business events that will require major prep work or personal planning over the next two months.
How do I use it in practice?
At the beginning of the week, I look at the major events and commitments coming up over the next 8 weeks and make sure the requisite pre-work is somewhere on the list above or elsewhere recorded in the system.
Why include it?
This section helps me connect intention to time. It also helps me see overcommitment before it becomes failure. If I have 30 hours of meetings and 50 hours of work I claim must get done, the math is not going to work. Pretending otherwise does not make me ambitious. It makes me irresponsible.
Calendar visibility is a form of honesty.
6. Must Do Today Before I Sleep
What is it?
This is the daily hard-stop section: what must I do today before I go to sleep? With all due respect to David Allen, this is my daily to do list. I use an erasable pen to fill it in each morning before starting my day and clear it by the end.
This includes any true Covey Quadrant I items — things that are both urgent and important. But it is not limited to crisis work. It can also include one or two actions that simply cannot be allowed to slip another day.
How do I use it in practice?
Each day, I identify the small number of things that must happen before I sleep. Not 20 things. Not everything. The real must-dos.
This is the section that confronts the day directly.
At the end of a long day, when I am tired, distracted, or tempted to postpone something uncomfortable, this section asks: what did I promise myself would get done today?
Why include it?
Because weeks are won or lost in days.
A beautiful weekly plan can still fail if there is no daily commitment mechanism. This section turns the system into action.
It also helps distinguish between “I was busy” and “I did what mattered.” Senior executives can be busy every minute of the day and still avoid the one conversation, decision, note, workout, apology, or follow-up that actually mattered most.
The phrase “before I sleep” creates useful pressure. It makes the commitment concrete.
7. Rolling Discussion Lists, House Perfect List, and Someday/Maybe
What is it?
The right side of the front page is a set of rolling lists.
I use it to track topics I need to discuss with team members, client contacts, consultants, family members, friends, and collaborators. Instead of immediately calling or texting every time a thought occurs to me, I write it down under the relevant person or project.
This section also includes ideas for articles, my “House Perfect List” — smaller or larger things I need to do around the house or yard — and a “Someday/Maybe” list, borrowed directly from David Allen’s Getting Things Done framework.
How do I use it in practice?
When I think of something I need to discuss with someone, I put it in that person’s box. Then, when we meet or talk, I have a ready-made agenda.
This improves the quality of meetings and reduces the number of interruptive one-off communications. Instead of spraying texts, emails, and half-formed thoughts throughout the week, I batch topics for the right moment.
For the Collaboration Evangelist articles, House Perfect List and Someday/Maybe list, the same principle applies: capture without overcommitting. Not everything belongs on this week’s must-do list. Some things simply need a trusted parking place.
Why include it?
Because not every thought deserves immediate action.
This is especially important for senior leaders. Executives generate a lot of ideas, questions, concerns, and follow-ups. If every thought becomes an immediate interruption for someone else, the leader becomes a chaos machine.
A rolling discussion list creates discipline. It respects other people’s attention. It makes meetings better. It lets me remember without reacting.
The Someday/Maybe list is equally important because it gives ideas a place to live without letting them hijack the week. That is a critical distinction. A good idea is not always a current priority.
8. Live Your Values
What is it?
The left side of the back page is my values reminder. It lists the personal values and commitments I am trying to live.
The emphasis is on trying.
This section is not a declaration that I am fully living these values every day. It is a reminder of who I want to be and how I want to show up.
For me, this includes being the best father I can be, being a good brother, honoring my parents by how I live and work, being respectfully and graciously honest, taking care of myself physically, emotionally, intellectually, and spiritually, using my experience and resources to help others, fighting for justice, being conscious of the impact of my words and actions, not judging others, being the best partner to Laura and best human to Izzy I can be, and passionately promoting what I believe in.
How do I use it in practice?
I keep it on the back page so I see it repeatedly. It is not a framed statement on a wall. It is part of the weekly system.
That matters. Values should not be separated from execution. They should shape execution.
When I review my week, I am not only asking, “What do I need to get done?” I am also asking, “Who am I trying to become while I get it done?”
Why include it?
Because productivity without values can become efficient selfishness.
Many senior executives are extremely productive by conventional measures. They clear inboxes, make decisions, hit numbers, raise capital, close deals, and move fast. But speed and volume are not the same as leadership.
This section forces me to connect action to identity. James Clear’s Atomic Habits argues that lasting behavior change is connected to identity — not merely what goal we want to achieve, but what kind of person we believe we are becoming.
For me, CHUMS is not just a task system. It is an identity system. It reminds me that the goal is not simply to get more shit done. The goal is to get the right shit done in a way that is consistent with the person I am trying to be.
9. Feedback Is a Gift
What is it?
This section captures some of the most important feedback I have received from supervisors, mentors, colleagues, friends, and role models.
It includes short reminders — phrases that mean something specific to me. Some are blunt. Some are cryptic to anyone else. Some are deeply personal. The point is not that the phrases would work for everyone. The point is that they work for me.
How do I use it in practice?
I keep the feedback visible so I cannot conveniently forget it.
Most of us say feedback is a gift. But in practice, we often treat feedback like a gift we would like to return, exchange, reinterpret, or bury in a drawer!
This section makes feedback operational. It turns lessons learned into recurring prompts.
Why include it?
Because leaders repeat avoidable mistakes when feedback is not converted into practice.
Senior executives often receive a lot of feedback, but not always honest feedback. The more senior you become, the more people manage you, flatter you, fear you, or soften the message. So, when you do receive real feedback, you should treat it as valuable data.
But insight fades. A hard lesson learned in March can be forgotten by June. This section keeps the lesson alive.
It also reinforces humility. No matter how experienced I am, I still have things to work on. The system reminds me of that every week.
10. Weekly Must-Do / Three Priorities Per Day
What is it?
The top right side of the back page can be used to list three priorities per day for the week. I have also used it as a blank note-taking space or as a place to list annual goals.
The three-priorities structure is intentionally simple.
How do I use it in practice?
For each day, I write the three things that would make the day successful.
Not 12. Not 27. Three.
This creates focus without pretending the day is clean, predictable, or fully controllable. It gives me a simple way to answer: if everything goes sideways, what are the few things that still matter?
Why include it?
Because constraints create clarity.
Executives are vulnerable to over-listing. We confuse ambition with volume. We put too much on the list and then feel behind before the day starts.
Three priorities force choice. Choice is the essence of strategy.
This section also creates a bridge between weekly planning and daily execution. The weekly system sets the field. The daily priorities define the play.
11. The 86,400 Reminder
What is it?
The final section is a visual reminder: 86,400; Be The Buffalo: Make The Next Best Choice; Proverbs 3:27.
There are 86,400 seconds in a day. Something I learned from my Pastor and Spiritual Entrepreneur friend Ray Hammond. For me, this symbol is a reminder to make the most of each day, confront challenges directly, not dwell endlessly on mistakes, and help others when I am able.
How do I use it in practice?
I do not use this as a productivity hack. I use it as a perspective hack.
Some days go well. Some do not. Some include progress. Some include mistakes. Some include conflict, disappointment, fatigue, or frustration.
The 86,400 reminder helps me reset. It says: this day is finite. Use it. Do not waste it in avoidance, resentment, self-pity, distraction, or fear.
Why include it?
Because time is the only non-renewable leadership resource.
Money can sometimes be raised. Staff can sometimes be hired. Strategy can sometimes be revised. Reputation can sometimes be rebuilt. But the day is spent once.
This does not mean every second should be optimized. That would be exhausting and inhuman. It means the day should be honored.
For a senior executive, the question is not merely “How do I get more done?” The deeper question is: “How do I make choices worthy of the time I have?”
Why the Whole System Works
CHUMS works for me because it integrates five things that are often separated:
- Tasks
- Time
- Energy
- Relationships
- Values
Most productivity systems over-focus on tasks. They help you capture, sort, and complete work. That is useful, but incomplete.
Senior executive effectiveness is not just task completion. It is sustained judgment, emotional regulation, strategic focus, relationship management, physical and mental energy, and values-based action under pressure.
That is why The Power of Full Engagement is such an important influence on CHUMS. Loehr and Schwartz argue that managing energy, not just time, is central to high performance. A leader with a perfectly organized calendar but depleted physical, emotional, mental, or spiritual energy is not operating at full strength.
CHUMS also works because it is reviewed weekly. A system that is not reviewed becomes clutter. A system that is reviewed becomes trusted.
My Sunday night practice matters. I update the Google Sheet, print the system, and carry it with me. That ritual turns CHUMS from a document into a discipline.
James Clear’s Atomic Habits emphasizes the power of small, repeatable systems. The point is not heroic motivation. The point is designing a structure that makes the desired behavior more likely. CHUMS does that for me. It makes remembering, reviewing, prioritizing, connecting, and acting easier.
It also creates accountability. Even when no one else sees the sheet, I see it. I know what I wrote down. I know what I said mattered. I know what I avoided.
That is powerful.
What Happens When I Do Not Use It
When I do not use CHUMS, my productivity does not collapse all at once. It degrades gradually.
First, I start relying on memory. Then I miss small things. Then I become more reactive. Then important-but-not-urgent work slips. Then relationships become less intentional. Then my day gets captured by other people’s priorities. Then I feel behind. Then I start chasing the crisis of the day. Or, at worst, fall into a state of depression or anxiety paralysis.
Eventually, I end up with exactly what Covey warned about: too much time in the urgent and important quadrant because I failed to invest enough time in preparation, prevention, relationships, renewal, and planning.
When I do not use CHUMS, I also become more likely to confuse motion with progress. I answer emails, create PowerPoint slides, attend meetings, respond to texts, and knock off visible tasks, but I may not be moving the most important priorities forward.
Worst of all, I become more likely to drift away from my values. Not because I stop believing in them, but because I stop seeing them.
That is how it happens for many leaders. They do not abandon their values dramatically. They simply get busy. Then they get reactive. Then they get tired. Then they rationalize. Then the urgent crowds out the important. Then, slowly, the life they are living stops matching the life they say they believe in.
CHUMS is my defense against that drift.
The System Is Mine. The Need Is Universal.
I do not recommend that everyone copy my exact system.
In fact, you should not. CHUMS is highly tailored to my priorities, family, relationships, passions, work, quirks, and flaws. Your system should be tailored to yours.
But I strongly believe every senior executive needs some kind of personal operating system.
It does not have to be paper. It does not have to be a spreadsheet. It does not have to have 11 parts. It does not have to use my language. It does not have to be called anything clever.
But it should help you answer these questions every week:
What matters most?
Who needs my attention?
What must get done?
What can wait?
What am I avoiding?
Where is my energy going?
Am I living my values?
What feedback am I trying to practice?
What must I do today before I sleep?
If your system cannot answer those questions, it is not yet a leadership system. It is just a task list.
A Note for CEOs and Senior Leaders
The higher you rise, the less anyone else can fully manage your attention for you.
An assistant can manage your calendar. A chief of staff can manage your meeting flow. A COO can manage execution rhythms. A board can help set accountability. A coach can ask hard questions.
But no one else can decide, week after week, what kind of leader you are trying to be.
That is your work.
CHUMS is one way I do that work.
It helps me get shit done. But more importantly, it helps me get the right shit done, with the right people in mind, for the right reasons, while trying — imperfectly but seriously — to live the values I claim to hold.
For me, that is the real productivity challenge.
Not just doing more.
Doing what matters.
Building Your Own GSD System
In my CEO coaching and productivity accountability partner (PAP) practice, this is one of the places where I believe I can be most helpful.
Not by handing leaders my system and pretending it will fit their lives.
But by helping them build their own.
A strong executive productivity system should be personal, practical, visible, reviewable, and honest. It should include the work, but not only the work. It should include relationships, energy, renewal, values, feedback, and follow-through. It should be simple enough to use when life gets busy, because that is exactly when the system matters most.
The goal is not to become a productivity robot.
The goal is to become a more intentional leader.
A leader who knows what matters.
A leader who writes it down.
A leader who follows up.
A leader who protects time and energy.
A leader who lives values in practice, not just in speeches.
A leader who gets shit done without losing himself or herself in the process.
That is what CHUMS does for me.
And that is why I still use it. To paraphrase my son Myles, when I don’t use it, “Shit goes left.”
Here’s a link to the front page google spreadsheet you can edit to start building your own system.
Endnotes
- Stephen R. Covey, The 7 Habits of Highly Effective People. CHUMS is especially influenced by Habit 7, “Sharpen the Saw,” and Covey’s distinction between urgent/important work and important-but-not-yet-urgent work.
- David Allen, Getting Things Done: The Art of Stress-Free Productivity. CHUMS draws heavily on GTD’s core practices of capturing commitments, clarifying next actions, organizing reminders, reviewing regularly, and maintaining a “Someday/Maybe” list.
- Jim Loehr and Tony Schwartz, The Power of Full Engagement: Managing Energy, Not Time, Is the Key to High Performance and Personal Renewal. CHUMS reflects the book’s core argument that sustainable high performance requires managing energy across physical, emotional, mental, and spiritual dimensions.
- James Clear, Atomic Habits. CHUMS reflects Clear’s emphasis on systems, small repeatable practices, identity-based behavior change, and designing structures that make desired behaviors more likely.
- Gail Matthews, Dominican University of California, research on goal achievement and accountability. The CHUMS presentation cites Matthews’ findings that written goals and weekly accountability increased the percentage of participants who accomplished at least half of their goals.
- Craig Underwood, CHU Management System aka CHUMS — Overview, updated June 12, 2026. The structure, 11-part framework, personal examples, and CHUMS descriptions are based on the author’s original presentation.
APPENDIX I – FULL FRONT PAGE (BEFORE FOLDING)
APPENDIX II – FULL BACK PAGE (BEFORE FOLDING)
FEELING UKRAINE WAR-FATIGUE? 5 THINGS YOU CAN DO TO GET BACK ON BOARD & SUPPORT FREE UKRAINE
CHU Recommends, Collaboration, News, PersonalSummary/ Net: 1. Keep smart, fight Russian disinformation and propaganda, and share with others. 2. Contribute to Operation Support the Front Lines in Free Ukraine. 3. Patronize Ukrainian artists and businesses. 4. Help Ukrainian refugees. 5. Write elected officials and candidates.
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The State AI Readiness Divide: Which States Are Actually Helping Their People Learn and Use AI?
AI, Best Practices, Collaboration, Feature, Featured CE Articles, Intellectual Leadership, Leadership, TechnologyThe State AI Readiness Divide: Which States Are Actually Helping Their People Learn and Use AI?
An Underwood Partners Collaboration Evangelist White Paper
By Craig Underwood in collaboration with ChatGPT
Artificial intelligence is often discussed as if it were a single national wave: ChatGPT appears, companies scramble, schools debate cheating, workers worry, and everyone tries to keep up.
But that is not what is happening.
AI adoption is not spreading evenly across the United States. It is being shaped by public choices — by whether states are giving government employees secure AI tools, helping educators understand how to teach with AI, investing in public higher education, and making AI training available to residents.
In other words, the next AI divide may not be just between people who use AI and people who do not.
It may be between states that help their people learn AI — and states that leave them to figure it out alone.
That was the hypothesis behind this research. I wanted to know whether states differ meaningfully in their investment in and support for AI, and whether Massachusetts — my home commonwealth — is at or near the top.
The short answer: yes.
The longer answer is more interesting.
State AI readiness evaluation criteria.
For this analysis, we ranked all 50 states plus Washington, D.C. on a 100-point AI readiness index. The index was not designed to measure private-sector AI activity alone. Silicon Valley venture capital, university research labs, and AI startups matter, but they were not the core question.
The question was civic and public:
Which states are helping their residents, students, educators, public employees, and institutions actually learn and use AI?
The ranking used four categories:
We gave the most credit for concrete implementation: secure AI tools, training programs, statewide public AI portals, public higher-ed credentials, K–12 AI guidance, workforce-development programs, and published use cases.
We gave much less credit for task forces, commissions, executive orders, or legislation unless they were tied to real funding, training, tool access, implementation, or public-facing programs.
We also distinguished among three policy postures:
That distinction matters. AI governance is not automatically anti-AI. In fact, the most impressive states are not choosing between innovation and responsibility. They are trying to do both.
The top 10 states and jurisdictions
The highest-ranked states are not merely “interested” in AI. They are putting AI into public systems.
Massachusetts ranks first because it appears to have the most complete public AI readiness strategy. The Commonwealth has launched a secure enterprise AI assistant for executive-branch employees, partnered with Google and the Massachusetts AI Hub to offer no-cost AI training to residents, and developed K–12 AI guidance and educator resources through the Department of Elementary and Secondary Education.[1]
Utah ranks second and may be the national leader in education-focused AI readiness. Utah has announced a statewide AI Workforce Credential for public college and university graduates and a statewide partnership to bring Gemini for Education to K–12 schools.[2]
New York ranks third because of the scale of its state workforce rollout. The state has expanded AI training and a secure AI tool, AI Pro, to more than 100,000 state employees, while also investing in Empire AI, a major public-interest AI research consortium.[3]
New Jersey ranks fourth because it has one of the clearest examples of practical public-sector AI implementation. Its NJ AI Assistant has been used by about 20,000 state employees, with more than 300,000 sessions and 1 million prompts as of February 2026.[4]
California ranks fifth because it combines a massive AI economy with state and local government training, K–12 guidance, higher-ed activity, and workforce-disruption planning. But California’s posture is more complex than the top four because it is also heavily focused on regulation and labor-market disruption.
The bottom 10 states
The bottom states should not be described as “anti-AI.” That would overstate the evidence. In many cases, they are better described as states with limited public evidence of broad statewide AI enablement.
This is an important methodological point. A low ranking does not prove that nothing is happening. It means I found less recent public evidence that the state is combining AI tool access, training, education strategy, workforce development, and responsible-use infrastructure into a coherent statewide approach.
That distinction matters because the AI landscape is changing fast. A state could move from the bottom tier to the middle quickly if it launched a statewide AI training portal, gave public employees secure AI access, published K–12 guidance, and used community colleges to scale workforce credentials.
The map tells the story.
The lead map shows a striking pattern: AI readiness is not evenly distributed.
Some states are already treating AI as a public-capacity project. They help people learn, practice, and use AI responsibly. Others are still closer to the study-and-watch stage.
National research supports this uneven picture. Code for America’s 2026 Government AI Landscape Assessment concluded that states are at different stages of AI adoption, ranging from readiness and piloting to implementation and impact.[5] The National Conference of State Legislatures has also tracked a surge in state AI activity, reporting that all 50 states, Puerto Rico, the Virgin Islands, and D.C. introduced AI-related legislation in 2025, while 38 states adopted or enacted around 100 measures.[6]
But legislation alone is not the same as readiness.
The leading states are doing more than passing bills. They are creating tools, training people, building public infrastructure, and giving educators and public employees permission to experiment within guardrails.
What the K–12 comparison adds
We also compared each state’s AI readiness rank with its K–12 education rank, using WalletHub’s 2026 public school system ranking as the K–12 comparison source.[7]
The result is one of the most interesting parts of the analysis.
Good schools help, but they do not automatically produce AI readiness.
Some states have both strong K–12 systems and strong AI readiness. Massachusetts is the clearest example: it ranked first in both our AI readiness index and the WalletHub K–12 ranking. New Jersey, New York, Maryland, Virginia, Connecticut, Rhode Island, Wisconsin, Pennsylvania, and Utah also appear in the high-education / high-AI zone.
These states are in the strongest strategic position. They have educational capacity and are beginning to convert that capacity into AI readiness.
But the scatterplot also reveals two other important patterns.
First, some states appear to be using AI as a leapfrog strategy. California, Georgia, Ohio, North Carolina, Colorado, Oregon, Arizona, Texas, and New Mexico do not rank at the very top on K–12 education, but they show stronger public evidence of AI support and investment than their education rankings alone would predict.
That may be strategically wise. AI training can scale faster than traditional education reform. Community colleges, public universities, workforce boards, libraries, and state agencies can help adults, small businesses, and public employees gain practical AI skills quickly.
Second, some states with relatively strong K–12 systems appear to be underleveraging that advantage. New Hampshire, Nebraska, North Dakota, Montana, Wyoming, Indiana, and Vermont all have educational strengths, but weaker public evidence of broad AI readiness.
That is the danger for high-education states: yesterday’s education advantage does not automatically become tomorrow’s AI advantage.
Four strategic positions for states
The AI readiness vs. K–12 education scatterplot creates four useful strategic categories.
These states have the strongest starting position. They have relatively strong education systems and are also investing in public AI readiness.
Strategic imperative: move from AI readiness to AI advantage.
These states should publish public AI use-case libraries, scale AI literacy expectations, train public employees, support educator professional development, fund public higher-ed credentials, and measure productivity and learning gains.
The risk is complacency. Strong schools and strong universities are not enough. AI advantage requires structured implementation.
These states may be using AI as a way to accelerate workforce development and public-sector modernization.
Strategic imperative: use AI as a human-capital accelerator.
These states should focus on community colleges, adult learners, small-business training, teacher productivity, student supports, and accessible AI credentials.
The opportunity is speed. A state does not need to wait 20 years to improve every K–12 metric before helping people build useful AI skills.
The risk is inequity. If AI programs primarily benefit already-advantaged residents, AI may widen internal gaps.
These states may have strong educational foundations but less visible statewide AI implementation.
Strategic imperative: convert education strength into AI readiness.
The playbook is straightforward: issue practical K–12 AI guidance, train educators, give public employees secure tools, create public AI learning portals, and use public universities and community colleges as AI training engines.
The risk is losing ground to states with weaker traditional education systems but stronger AI implementation strategies.
These states face the hardest strategic position.
Strategic imperative: start with practical, low-cost, high-leverage AI readiness moves.
They should create a statewide AI learning portal, train state employees in responsible AI use, publish K–12 AI guidance, partner with community colleges on short credentials, and build a public use-case library.
The risk is that AI becomes another driver of regional inequality.
The deeper point: AI readiness is public infrastructure.
The best states are not treating AI as a gadget. They are treating AI readiness as a form of civic infrastructure.
That means giving people:
A state employee who has access to a secure AI assistant can learn by doing. A teacher with practical guidance can move beyond fear and confusion. A community college student with an AI credential can enter the labor market with greater confidence. A small business owner with free training can experiment without hiring a consultant. A resident with access to a public AI learning portal can begin without needing to be part of the technology sector.
That is the difference between AI as a private advantage and AI as a public capability.
Why Massachusetts matters
Massachusetts is a particularly useful case because it already had advantages: great universities, strong K–12 performance, a dense innovation economy, world-class hospitals, and a highly educated workforce.
But those advantages alone are not what made Massachusetts rank first.
What matters is that Massachusetts appears to be converting its existing advantages into public AI readiness.
The Commonwealth is giving executive-branch employees secure AI access. It is offering residents no-cost AI training. It is building a public AI Hub. It is supporting K–12 educators with guidance and resources.
That combination is the key.
Massachusetts is not simply an AI-rich state. It is investing to become an AI-ready Commonwealth.
The policy lesson
The policy lesson is not that every state should copy Massachusetts exactly. States differ in size, economy, demographics, politics, education systems, and fiscal capacity.
But every state can do five things:
The first states to do these things well will have an advantage.
Not just a technology advantage. A civic advantage.
Final thought
AI adoption is often framed as an individual responsibility: learn the tools, keep up, do not fall behind.
But this research suggests that public leadership matters enormously.
Some states are making AI learning easier, safer, and more accessible. Others are leaving residents, teachers, workers, and public employees to navigate the AI era largely on their own.
That is the state AI readiness divide.
And it may become one of the most important competitiveness and equity issues of the next decade.
Endnotes
[1] Massachusetts state AI assistant materials; Massachusetts-Google free AI training announcement; Massachusetts AI Hub; Massachusetts Department of Elementary and Secondary Education AI resources.
[2] Utah System of Higher Education announcement of statewide AI Workforce Credential; Google announcement of Gemini for Education partnership with Utah K–12 schools.
[3] New York Governor’s Office announcement of AI Pro and AI training expansion to state workforce; Empire AI materials.
[4] New Jersey Innovation Authority, NJ AI Assistant project page and 2026 update.
[5] Code for America, 2026 Government AI Landscape Assessment.
[6] National Conference of State Legislatures, 2025 AI legislation summary and AI legislation database.
[7] WalletHub, 2026 public school system ranking.
Source links for the article endnotes
Massachusetts has an official enterprise AI assistant page, and Google announced no-cost AI training for Massachusetts residents through the Massachusetts AI Hub and Grow with Google. (Massachusetts Government)
Utah’s higher-ed system announced a no-cost AI Workforce Credential for more than 50,000 public college and university graduates, and Google announced Gemini for Education access for every Utah K–12 school beginning in the 2026–2027 school year. (Utah System of Higher Education)
New York announced expansion of AI education and AI Pro to more than 100,000 state employees, while New Jersey reported about 20,000 state employees using its AI Assistant, with more than 300,000 sessions and 1 million prompts as of February 2026. (Governor Kathy Hochul)
Code for America’s 2026 Government AI Landscape Assessment evaluates state AI adoption across readiness, piloting, implementation, and impact, while NCSL tracks AI legislation across states and reported that 38 states adopted or enacted around 100 AI-related measures in 2025. (Code for America)
For education context, Playlab reports that 36 states plus Puerto Rico had official K–12 AI guidance or policy frameworks as of May 2026, and FutureEd tracked 71 AI-in-education bills across 27 states during the 2026 legislative session. (learn.playlab.ai
CHU RECOMMENDS – Wahoo ACE Computer & Radar Detector Light, Noxgear Personal Speaker Bike Technology
AI, Best Practices, CHU Recommends, Personal, TechnologyLast weekend, while cycling the Newburyport, Massachusetts Bikes & Beers ride, I had the opportunity to try out two new Wahoo products – the larger Element Ace bike computer and the companion rear light/ radar detector device. I was highly skeptical about both devices, but quickly fell in love with them and was ready to give them a highly recommended rating by the end of the beautiful ride.
Wahoo Elemnt Ace Bike Computer
Wahoo Fitness TRACKR Radar Tail Light
The radar light has other cool features as well, including a self-dimming function that saves battery life when no cars are behind you and a “brake light“ feature that increases brightness when you slow down. Not surprisingly, it pairs easily with the ACE computer. $247 well spent, IMHO.
The Noxgear 39g Wearable Bluetooth Speaker
I hope these recommendations have been of value to others who follow the Velominati! Please share your most-loved bike technology purchases.
Your Organization Doesn’t Have One AI Divide. It Has Five: The New AI Digital Divides.
UncategorizedThe New AI Digital DividesTM
Why Access Is No Longer Enough – Why Senior Leaders Must Build Capability Before the Gap Becomes Permanent
A Collaboration Evangelist Underwood Partners White Paper
Craig Underwood | July 2026
I thought I understood the emerging divide around artificial intelligence. On one side were people and organizations using tools such as ChatGPT, Gemini, and Claude; on the other were people who were not using them, could not use them, or were not permitted to use them.
Then I began building agents.
At first, I used AI much as many professionals do: to research questions, improve writing, brainstorm, and make sense of complicated information. With training and experience, I learned to prompt more deliberately: provide context, define the task, request a format, ask the model to critique itself, compare options, and refine the output. That alone substantially changed the quality of the work.
But the bigger change occurred when I began building custom agents that could perform multi-step research and analysis workflows. Suddenly, I was not simply asking AI for a better answer. I was delegating meaningful parts of a workstream. The challenge began to shift from producing output to supervising, evaluating, synthesizing, and acting on far more output than I could previously create myself.
That experience caused me to rethink the phrase digital divide.
The original digital divide concerned access: who had a computer, who had broadband, who could participate in the internet economy. Access to AI remains important. But it is no longer the only divide, or perhaps even the most consequential one for organizations. A widening set of capability divides now separates people and enterprises that cannot or will not use AI from those that use it casually, those that use it skillfully, those that build agents to execute work, and those that coordinate suites of agents across workflows.
My hypothesis is simple:
The next competitive, workforce, and social-equity challenge is not one AI divide. It is a ladder of AI capability divides – and the distance between the rungs is beginning to matter.
The research published over the past two years strongly supports this hypothesis, with one essential qualification: these five levels are not neat, mutually exclusive demographic categories. Organizations may occupy several at once. A hospital might prohibit public tools for patient information while deploying secure AI in administrative work. A firm may have Level 2 employees and Level 5 technology pilots at the same time. This is not a scorecard for judging people. It is a leadership framework for recognizing capability gaps before they harden into inequality and competitive disadvantage.
Figure 1. The Five-Level AI Capability Ladder.
The five divides
This first category is more complex than “anti-AI.” Some people actively reject AI for philosophical, labor, environmental, artistic, or ethical reasons. Others want to learn but lack access, confidence, or opportunity. Still others work in organizations that appropriately restrict public AI tools because of privacy, confidentiality, regulated data, intellectual property, or safety concerns.
The distinction matters. A responsible restriction on feeding sensitive customer, patient, client, or proprietary information into a public model is not backward-looking. It is governance. But an organization that stops at prohibition – without providing approved alternatives, clear policies, and training in the responsible and productive use of AI – may unintentionally place employees at a capability disadvantage while also driving “shadow AI” use underground.
That is not a theoretical risk. The University of Melbourne and KPMG’s 2025 global study found that 70% of employees intentionally using AI at work accessed free, publicly available AI tools, while only two in five reported that their organization had a policy or guidance on generative AI use. Nearly half reported uploading sensitive company information or copyrighted material into public AI tools; strikingly, the behavior was most common among employees reporting that their organization had banned generative AI. Cisco’s 2026 Data and Privacy Benchmark Study, based on a September 2025 survey of more than 5,200 IT, technology and security professionals with data privacy responsibilities across 12 markets, adds an institutional view: 90% said AI had broadened the scope of their privacy programs, but only 12% described their AI governance committees as mature and proactive. The policy challenge is therefore not whether leaders should ignore risk. It is whether they can protect data while still enabling safe learning and useful experimentation.[1]
Figure 2. AI use is outrunning governance.
There is also a growing institutional access gap. The OECD reported in 2025 that AI adoption divides are forming along existing fault lines among places, sectors, and firms. Its analysis found that AI diffusion in 2023-24 was being driven more by leaders “escaping the pack” than by laggards catching up.[2] Put differently: as AI adoption accelerates, previous disadvantages in capital, skills, digital infrastructure, and organizational capacity may be magnified rather than erased.
This level captures the vast middle of current AI adoption: employees use AI to locate information, consolidate documents, generate ideas, summarize meetings, rewrite emails, or improve drafts. This is valuable. It can save time, reduce friction, and introduce people to a powerful technology.
But it is still mostly individual, task-level augmentation rather than a redesign of work.
Gallup’s April 2026 study of 23,717 employed U.S. adults found that half use AI in their role at least a few times a year, while 28% use it at least a few times a week and 13% use it daily.[3] A prior Gallup analysis found that common uses were precisely the kinds of activities associated with Level 2: consolidating information, generating ideas, learning new things, and using chatbots or writing/editing tools.[4]
This is a genuine productivity opportunity. It is also a potential trap for leaders who confuse widespread experimentation with organizational transformation. In the same 2026 Gallup study, 65% of employees in organizations implementing AI said it had improved their productivity and efficiency; yet only about one in ten strongly agreed that AI had transformed how work gets done in their organization.[5]
Figure 3. AI adoption is broad; deep use is not.
The distinction is crucial. If employees become slightly faster at emails, summaries, and brainstorming while the organization continues operating through the same slow handoffs, redundant meetings, outdated processes, disconnected systems, and approval bottlenecks, the organization has captured convenience – not transformation.
At Level 3, AI is no longer simply a search box or writing assistant. People have learned enough to work with it deliberately. They can frame a problem, supply context and source material, specify the desired deliverable, challenge weak outputs, verify claims, ask for alternatives, iterate toward a stronger result, and understand when not to rely on the model.
I use the phrase AI-literate knowledge work intentionally. Prompt engineering matters, but this level is broader than clever prompts. It includes judgment, verification, data protection, task selection, critical thinking, and the discipline to keep humans accountable for important decisions.
Research suggests this divide is already material. Boston Consulting Group’s 2025 global AI at Work survey found that only 36% of employees felt properly trained in the skills needed for AI transformation and only 25% of frontline employees reported receiving sufficient support from leadership on how and when to use AI. Yet regular AI use was closely associated with training intensity: 18% among employees receiving no training, 63% among those receiving one to five hours, 82% among those receiving five to ten hours, and 89% among those receiving more than ten hours.[6]
Figure 4. Training and leadership support lag behind adoption.
This is where the AI divide becomes a leadership issue. When executives offer a tool but do not provide training, permission boundaries, safe data practices, coaching, example workflows of AI prompts/ instructions that worked – and extremely important, those that didn’t work – or time to practice, they create an environment in which the confident and self-directed advance while others remain behind. In effect, they outsource capability building to personal curiosity, spare time, and professional privilege.
The stakes are economic as well. PwC’s 2025 Global AI Jobs Barometer, drawing on nearly one billion job advertisements across six continents, found a 56% wage premium for jobs requiring AI skills compared with similar jobs without those skills. It also found that skills are changing 66% faster in AI-exposed jobs than in other jobs, and that industries more exposed to AI experienced three times greater growth in revenue per employee.[7] This does not prove that prompting alone produces higher wages or productivity. It does strongly suggest that the capacity to work effectively with AI is becoming economically valuable – and rapidly so.
Importantly, AI literacy can sometimes narrow existing skill gaps rather than worsen them. A peer-reviewed 2025 study in The Quarterly Journal of Economics examined 5,172 customer support agents using an AI conversational assistant. Access to AI increased productivity by 15% on average, with the largest gains accruing to less experienced and less skilled workers; novice workers with AI reached performance levels comparable to far more experienced workers without it.[8] This is a hopeful finding: appropriately deployed AI can democratize expertise. But it also underlines the moral and organizational consequence of denying some workers the training and tools that allow them to benefit.
At Level 4, the user is not simply conversing with AI. The user creates or deploys an agent that can conduct a multi-step task: search defined sources, analyze data, prepare a first draft, compare options, produce a report, monitor a condition, or support a repeatable operational workflow.
This is the divide I only fully appreciated after beginning to build custom agents myself. There is a dramatic difference between asking an AI system, “What companies might sponsor this nonprofit event?” and designing an agentic workflow that methodically reviews industry lists, identifies relevant companies, researches corporate giving, checks foundation tax filings, ranks prospects, records findings, and produces a briefing for human review. The human remains accountable. But the scale and speed of possible work changes materially.
Early organizational examples illustrate this shift.
Moderna: After providing employees access to ChatGPT Enterprise, Moderna reported that within two months it had 750 custom GPTs across the company; 40% of weekly active users had created a GPT; and the average user conducted 120 ChatGPT Enterprise conversations per week. One pilot, Dose ID, was designed to review and analyze clinical data and support clinical study teams’ dose-selection analysis while preserving human judgment and review.[9] Moderna’s own description emphasizes that embedding AI requires not just technology but an “AI-focused culture” and on-the-job training.[10]
Morgan Stanley: In 2024, Morgan Stanley Research announced AskResearchGPT, an AI-powered capability intended to help client-facing teams find and synthesize insights from the firm’s research. The company described it as part of a growing suite that included AI @ Morgan Stanley Assistant and AI @ Morgan Stanley Debrief for wealth-management advisors and staff.[11] This is not an employee polishing an email. It is a firm creating controlled, domain-grounded AI capability embedded in professional work.
Small and midsized organizations: The opportunity is not reserved for global enterprises. In a representative survey of more than 5,000 small and medium-sized enterprises across seven OECD countries, 31% reported using generative AI. Among SME users, 65% said it had improved employee performance, 39% of those experiencing a skills gap said it helped compensate for that gap, and one-third reported reduced workload. Yet a third or fewer were taking measures to train staff, set internal guidance, or research legal and regulatory issues.[12] This combination – useful technology without commensurate capability building – is precisely how a new divide can widen.
Level 5: AI-orchestrated organizations
At the fifth level, organizations coordinate multiple agents, workflows, tools, data sources, and human decision makers. Humans set objectives, define permissions, supervise exceptions, evaluate results, protect values, and bear accountability. Agents may research, draft, schedule, classify, analyze, retrieve, monitor, escalate, or execute bounded tasks across a connected process.
This is still emerging. Leaders should be wary of hype and “agentwashing,” in which an ordinary chatbot is described as an autonomous agent. Yet the direction of travel is increasingly visible.
Microsoft’s 2025 Work Trend Index, based on survey data from 31,000 workers across 31 countries as well as labor-market and productivity signals, described an evolution from AI as assistant, to agents as “digital colleagues,” to humans directing agents that run whole workflows. It found that 81% of leaders expected agents to be moderately or extensively integrated into their AI
strategy within 12 to 18 months, and 46% said their companies were already using agents to fully automate workflows or processes.[13]
The report also reveals an agentic gap between leaders and employees. Sixty-seven percent of leaders reported familiarity with agents, compared with 40% of employees. Sixty-nine percent of leaders used AI regularly, compared with 45% of employees. And 36% of leaders expected to manage agents in their jobs, compared with 21% of employees. Most strikingly, 42% of leaders expected their teams to be building multi-agent systems to automate complex tasks within five years.[14]
Figure 5. The frontier is moving from use to orchestration.
Technology suppliers are now making this capability easier to deploy. Microsoft announced multi-agent orchestration in Copilot Studio in 2025, enabling agents to delegate tasks and share results across complex workflows.[15] Anthropic described how its own multi-agent research system uses a lead agent to coordinate parallel subagents, reporting that the multi-agent configuration outperformed a single-agent configuration by 90.2% on its internal research evaluation for breadth-first research tasks.[16] Gartner forecast in August 2025 that task-specific AI agents would be integrated into 40% of enterprise applications by the end of 2026, up from less than 5% in 2025; Gartner also explicitly warned against confusing AI assistants with agents.[17]
None of these findings means every organization should rush to automate every process. Agentic systems introduce new risks: security exposures, unreliable actions, poor source selection, errors at scale, loss of accountability, employee anxiety, and the possibility that leaders automate activity without improving outcomes. They require more governance, not less.
But they do reinforce the central hypothesis: organizations capable of designing, supervising, and safely coordinating agents may operate in a fundamentally different productivity environment from organizations still debating whether employees may use an AI writing assistant.
Why this is not merely a technology story
The new AI divides matter for at least three reasons.
1. They are a competitive and economic issue
The difference between Level 2 and Level 4 is not simply efficiency. It is leverage. A professional using AI to revise an email may save minutes. A professional managing a well-designed research agent may expand the number of opportunities evaluated, accelerate decisions, and produce capabilities that previously required a team or an outsourced engagement.
The evidence is not yet sufficient to calculate an exact productivity premium for each rung of this ladder. Indeed, Gallup’s research properly cautions that individual task gains have not yet translated into widespread transformation of organizational work. But that is precisely the point: organizations capable of redesigning workflows may separate themselves from organizations that never move beyond incidental personal usage.
2. They are a workforce development issue
A digital divide becomes socially consequential when the people most in need of capability are least likely to receive it. Frontline employees, workers in smaller organizations, professionals outside technology and finance, and communities with fewer learning resources risk becoming users of yesterday’s workflows while better-supported peers learn to direct AI-powered work.
I believe leaders have the responsibility – some would say the moral imperative – to help employees learn how to use AI responsibly and efficiently and to move toward higher-value work involving judgment, relationships, creativity, influence and accountability. This is especially important for nonprofit leaders of work-force development and college access/ success organizations, and those whose missions include preparing those they serve for jobs of the future. Imagine the different impression a young entry-level job or internship applicant would leave on a prospective employer when answering the question “Do you use AI?” between:
“[My training organization] has provided responsible AI literacy training and prompt engineering to all of its students.”
And
“I don’t use AI much at all.” Or even worse, “We are prohibited from using AI.”
The optimistic scenario is that AI distributes expertise: a new employee learns faster; a small nonprofit conducts analysis it could not afford; a local business competes more effectively; a person without elite credentials gains a powerful intellectual assistant. The darker scenario is that access to secure tools, training, agents, and integrated systems concentrates among already advantaged firms and professionals.
Leaders help determine which scenario occurs.
3. They are a leadership and governance issue
A ban is not a strategy. A license is not a strategy. A prompting webinar is not a strategy. An agent pilot is not a strategy.
An AI capability strategy requires leaders to answer hard questions: Which tools are approved? What data may be used? Which tasks should remain human-only? Which employees are receiving training? Who has access to agent-building capability? How are outputs checked? How are actions authorized, logged, and audited? What new measures of quality, equity, capacity, and risk will we use? Where are humans indispensable because judgment, trust, care, moral responsibility, or public legitimacy matter?
The ladder is therefore not a call to put every worker and task at Level 5. The appropriate level depends on the work. It is a call for every senior leader to understand the ladder, make intentional decisions, and ensure that safe opportunity is not limited to a small group of early adopters.
A practical leadership agenda: build bridges across all five divides
Leaders do not need perfect answers before they begin. They do need to stop treating AI adoption as a binary choice. A practical agenda might include seven moves:
The divide we can still choose to prevent
The most important question is not whether AI will be adopted. It already is. Stanford’s 2025 AI Index reported that 78% of surveyed organizations used AI in 2024, up from 55% a year earlier.[18] The U.S. Census Bureau reported in May 2026 that 17% to 20% of U.S. businesses had used AI in at least one business function during the preceding two weeks, with much higher rates among larger firms and in information and finance.[19] Different measurements produce different percentages, but the direction is unmistakable: AI is diffusing rapidly and unevenly.
The question is whether leaders will allow that unevenness to become a durable capability divide.
The first digital divide taught us that providing devices or internet access was necessary but not sufficient. People also needed affordability, training, support, relevant applications, trust, and the opportunity to convert access into improved lives.
The same is true now – with higher stakes and a faster clock.
Organizations that merely allow AI may trail those that teach people to use it well. Organizations that teach prompting may trail those that design agents to perform real work. Organizations experimenting with one agent may trail those that learn to orchestrate multiple agents responsibly across entire workflows. And organizations that restrict AI without building secure alternatives may unintentionally leave employees and communities unable to participate in the next stage of work.
This is why the new AI digital divide belongs on every senior leader’s agenda. It is simultaneously an economic question, a workforce question, a social-equity question, and a governance question.
The divide is not inevitable. But the ladder is already built.
Selected Bibliography
Anthropic. “How We Built Our Multi-Agent Research System.” June 13, 2025.
Boston Consulting Group. AI at Work: Momentum Builds, but Gaps Remain. June 2025.
Brynjolfsson, Erik, Danielle Li, and Lindsey Raymond. “Generative AI at Work.” The Quarterly Journal of Economics 140, no. 2 (May 2025): 889-942.
Cisco. Cisco 2026 Data and Privacy Benchmark Study. 2026.
Gillespie, Nicole, Steve Lockey, Tim Ward, Alex Macdade, and Greta Hassed. Trust, Attitudes and Use of Artificial Intelligence: A Global Study 2025. University of Melbourne and KPMG International, 2025.
Gallup. Kemp, Andy. “AI Use at Work Rises.” December 15, 2025.
Gallup. Kemp, Andy. “Rising AI Adoption Spurs Workforce Changes.” April 13, 2026.
Gartner. “Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025.” August 26, 2025; updated September 5, 2025.
Kergroach, Sandrine, and Julien Héritier. Emerging Divides in the Transition to Artificial Intelligence. OECD Regional Development Papers No. 147. OECD Publishing, 2025.
Microsoft. 2025 Work Trend Index Annual Report: The Year the Frontier Firm Is Born. April 23, 2025.
Microsoft. “What’s New in Copilot Studio: May 2025.” June 4, 2025.
Moderna. “Collaboration with OpenAI: Transforming the Way We Work and Innovate Through AI.” April 24, 2024.
Morgan Stanley. “Morgan Stanley Research Announces AskResearchGPT.” October 23, 2024.
OECD. Generative AI and the SME Workforce. November 2025.
OpenAI. “Moderna: Pioneering the Future of Medicine with ChatGPT Enterprise.” 2024.
PwC. The Fearless Future: 2025 Global AI Jobs Barometer. June 3, 2025.
Stanford Institute for Human-Centered Artificial Intelligence. 2025 AI Index Report. 2025.
U.S. Census Bureau. “AI Use at U.S. Businesses.” May 2026.
[1] Nicole Gillespie et al., Trust, Attitudes and Use of Artificial Intelligence: A Global Study 2025 (University of Melbourne and KPMG International, 2025), 70-76; Cisco, Cisco 2026 Data and Privacy Benchmark Study (2026), 3-4, 7. The Cisco survey was conducted in September 2025 among more than 5,200 IT, technology and security professionals with data privacy responsibilities across 12 markets.
[2] Sandrine Kergroach and Julien Héritier, Emerging Divides in the Transition to Artificial Intelligence, OECD Regional Development Papers No. 147, 2025.
[3] Andy Kemp, Rising AI Adoption Spurs Workforce Changes, Gallup, April 13, 2026. Based on a February 4-19, 2026 Gallup survey of 23,717 employed U.S. adults.
[4] Andy Kemp, AI Use at Work Rises, Gallup, December 15, 2025. Common uses were reported in Gallup’s Q2 2025 measurement and described in the December article.
[5] Andy Kemp, Rising AI Adoption Spurs Workforce Changes, Gallup, April 13, 2026. Based on a February 4-19, 2026 Gallup survey of 23,717 employed U.S. adults.
[6] Boston Consulting Group, AI at Work: Momentum Builds, but Gaps Remain, June 2025. Global survey of 10,635 employees; frontline analysis includes 3,537 employees.
[7] PwC, The Fearless Future: 2025 Global AI Jobs Barometer, June 3, 2025. PwC analyzed close to one billion job ads across six continents.
[8] Erik Brynjolfsson, Danielle Li, and Lindsey Raymond, “Generative AI at Work,” The Quarterly Journal of Economics 140, no. 2 (May 2025): 889-942.
[9] OpenAI, Moderna: Pioneering the Future of Medicine with ChatGPT Enterprise, 2024; the case study reports Moderna’s adoption figures and describes the Dose ID pilot.
[10] Moderna, Collaboration with OpenAI: Transforming the Way We Work and Innovate Through AI, April 24, 2024.
[11] Morgan Stanley, Morgan Stanley Research Announces AskResearchGPT, October 23, 2024.
[12] OECD, Generative AI and the SME Workforce, November 2025. Representative 2024 survey of more than 5,000 SMEs in Austria, Canada, Germany, Ireland, Japan, Korea, and the United Kingdom.
[13] Microsoft, 2025 Work Trend Index Annual Report: The Year the Frontier Firm Is Born, April 23, 2025. The report states that it draws on survey data from 31,000 workers across 31 countries, LinkedIn labor-market trends, and Microsoft 365 productivity signals.
[14] Microsoft, 2025 Work Trend Index Annual Report: The Year the Frontier Firm Is Born, April 23, 2025. The report states that it draws on survey data from 31,000 workers across 31 countries, LinkedIn labor-market trends, and Microsoft 365 productivity signals.
[15] Microsoft, What’s New in Copilot Studio: May 2025, June 4, 2025.
[16] Anthropic, How We Built Our Multi-Agent Research System, June 13, 2025. The reported performance comparison is an internal evaluation and should not be generalized to every multi-agent use case.
[17] Gartner, Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025, August 26, 2025, updated September 5, 2025.
[18] Stanford Institute for Human-Centered Artificial Intelligence, 2025 AI Index Report, 2025. Its organizational use statistic draws on a survey measure and should not be compared directly with the Census measure of recent U.S. business-function use.
[19] U.S. Census Bureau, AI Use at U.S. Businesses, May 2026, reporting Business Trends and Outlook Survey data collected December 14, 2025 through May 3, 2026.
Free AI Tool Access & AI Training Available to Massachusetts Residents
AI, Best Practices, CHU Recommends, Intellectual Leadership, Leadership, Technology, Uncategorizedby Craig Underwood | June 22, 2026
Massachusetts now has one clearly universal free AI training offer for all residents: the Massachusetts AI Hub + Grow with Google partnership, which provides no-cost access through December 31, 2027 to Google AI and career certificate programs. The clearest free AI tool-access benefit is narrower but still meaningful: learners who enroll in the Google AI Professional Certificate receive three months of no-cost access to Google AI Pro. Beyond that resident-facing offer, the Healey administration has built a broader AI support ecosystem including educator pilots, high school AI programming, state-employee AI tools and training, AI compute resources, data commons work, applied AI grants, and startup supports. The practical takeaway for clients is simple: residents and organizations should use the Google training now, while tracking which other Massachusetts initiatives are universal, targeted, or limited-capacity.
CLICK HERE TO GO REGISTER FOR FREE GOOGLE AI AND OTHER TRAINING
Executive Summary
Massachusetts now has one major statewide free AI training offer for all residents: the Massachusetts AI Hub + Grow with Google partnership, announced by Governor Maura Healey on February 26, 2026. It gives Massachusetts residents no-cost access, through December 31, 2027, to Google AI and career certificate programs, including the Google AI Professional Certificate, Google AI Essentials, Google Agile Essentials, and Google Career Certificates such as Data Analytics, IT Support, and Project Management.[1]
The most concrete free AI tool access currently available to the general public through this initiative is attached to the Google AI Professional Certificate: each learner who enrolls in that AI certificate receives three months of no-cost access to Google AI Pro.[2]
Beyond that resident-facing benefit, Massachusetts has built a broader AI support ecosystem through the Massachusetts AI Hub, launched in December 2024 after the Massachusetts AI Strategic Task Force and supported by the Mass Leads Act. The Hub’s mission is to connect, accelerate, and scale AI initiatives that advance innovation, economic growth, workforce development, and public good across Massachusetts.[3]
The practical bottom line: for ordinary Massachusetts residents, the free Google training partnership is the clear ‘go do this now’ opportunity. For educators, public employees, students, startups, nonprofits, universities, and employers, there are additional targeted opportunities, but eligibility and access vary.
Figure 1. Massachusetts AI Investment & Support Timeline
1. What Is Available to All Massachusetts Residents?
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A. Grow with Google + Massachusetts AI Hub: free AI and career training
The most direct statewide resident benefit is the Grow with Google partnership, administered through the Massachusetts AI Hub. The state announcement says the partnership offers all Massachusetts residents access to artificial intelligence and career certificate training programs at no cost through Grow with Google.[1]
The MA AI Hub’s Grow with Google page says Massachusetts residents receive no-cost access until December 31, 2027, to the Google AI Professional Certificate, Google AI Essentials, Google Agile Essentials, and Google Career Certificates, including examples such as Data Analytics, IT Support, and Project Management.[1]
Programs listed by the Massachusetts AI Hub
The most important AI-tool access detail
The Google AI Professional Certificate is not just a training program. The MA AI Hub page states that every learner who enrolls in the AI Certificate also receives three months of no-cost access to Google AI Pro. That appears to be the most concrete free AI tool access currently available to all Massachusetts residents under the Healey-Google partnership.[2]
What the Google AI Professional Certificate covers
The MA AI Hub describes the Google AI Professional Certificate as going beyond the basics with 20+ hands-on activities designed to build AI fluency. [4]
What Google AI Essentials covers
Google AI Essentials appears to be the better starting point for residents who want a shorter, less technical introduction. The MA AI Hub page lists five modules: Intro to AI; Maximize Productivity With AI Tools; Discover the Art of Prompting; Use AI Responsibly; and Stay Ahead of the AI Curve.[5]
This is likely the best starting point for residents who are not yet sure they want a full certificate.
How residents access it
The MA AI Hub page describes a four-step process: residents express interest through a short form, begin the chosen online course at their own pace, complete the program and earn a Google credential, and may share their success story.[1]
2. What Is Available by Audience Segment?
Figure 2. Who Gets What? Massachusetts AI Access by Audience
A. Individual Massachusetts residents
Most relevant benefit: no-cost Grow with Google training through the MA AI Hub. This includes AI training and career certificates through December 31, 2027, plus three months of Google AI Pro for those enrolling in the Google AI Professional Certificate.[1][2]
Best starting point: Google AI Essentials for AI beginners; Google AI Professional Certificate for people who want deeper fluency, a credential, portfolio-style activities, and the included three months of Google AI Pro.
B. Students
Students can use the statewide Google training if they are Massachusetts residents. The partnership serves students, employees, and small-business owners across the Commonwealth.[1]
There is also a targeted Massachusetts High School Summer Career Academy in AI, launched in summer 2025 as a pilot involving 50 high school students from eight sending districts, with priority for Gateway City schools or regional consortia. Students in grades 9-12 learned AI fundamentals, how AI is used in generative AI, health, autonomous vehicles, and business decisions, and also worked with Python, AI ethics, bias, group projects, mentorship, career workshops, and a showcase event.[6]
C. Educators and schools
The most specific educator initiative is Future Ready: AI in the Classroom, a professional-development pilot launched in June 2025 in partnership with Project Lead The Way. The state made a $135,000 investment supporting teachers in 45 classrooms and it is estimated it would reach more than 1,600 students.[7]
Future Ready is a 50-hour professional-development pilot for high school STEM educators, launched with the Massachusetts STEM Advisory Council and Project Lead The Way. It is described as the first-in-the-nation pilot of PLTW’s AI curriculum and is intended to help educators bring AI into STEM classrooms responsibly and practically.[8]
D. Nonprofits
Nonprofit staff who are Massachusetts residents can use the no-cost Grow with Google resident training.[1]
The MA AI Hub’s Additional AI Training Resources page points nonprofits toward Microsoft AI training pathways, where AI-powered solutions can help nonprofits transform operations, improve agility, increase transparency, and improve measurable outcomes.[10]
The AI Hub’s infrastructure and ecosystem programs are also relevant to nonprofit institutions involved in research, education, workforce development, public-interest technology, or applied AI projects. For example, the Artificial Intelligence Compute Resources program dedicates at least 40% of compute time will be dedicated to startups, entrepreneurs, and partner institutions including non-member colleges and nonprofits statewide.[11]
E. Small businesses and employers
The Google partnership is designed to provide every resident and small business with AI and tech skills needed to succeed in the digital economy.[1]
For small businesses, one of the most useful immediate pathways is for owners and staff to enroll in Google AI Essentials or the Google AI Professional Certificate; use the three months of Google AI Pro included with AI Professional Certificate enrollment; and then apply AI to productivity, customer communications, research, content creation, data analysis, planning, and lightweight app-building.[1][2]
For AI startups and AI-enabled companies, the AI Hub’s compute resources, applied AI grants, startup accelerator ecosystem, and job board support these ventures. The Open Accelerator is a Boston initiative to empower Massachusetts AI founders with networks, knowledge, skills, workspace, mentorship, GPU access, hackathons, and community.[12]
F. Public-sector workers
Massachusetts is also deploying AI inside state government. On February 13, 2026, Governor Healey announced a ChatGPT-powered AI Assistant for the state workforce. The executive branch includes nearly 40,000 employees. The tool is intended to provide a safe, secure environment that protects state data.[13]
WBUR reported that the ChatGPT-powered assistant would be phased in across the almost 40,000-employee executive branch, starting with the Executive Office of Technology Services and Security, and that the state was also rolling out optional training programs for employees using the assistant.[14]
Separately, EOTSS formed a partnership with InnovateUS to deliver generative AI training for its employees. The training is available to all TSS staff, free of charge, and consists of five modules.[15]
3. Broader AI Infrastructure and Innovation Supports
Figure 3. Massachusetts AI Support Flywheel
A. Massachusetts AI Hub
The MA AI Hub was launched in 2024 as a key recommendation of the Massachusetts AI Strategic Task Force. Its mission is to connect, accelerate and scale AI initiatives that advance innovation, economic growth, workforce development, and public good across Massachusetts.[3]
The AI Hub’s program areas include Applied AI Models Innovation Challenge, Artificial Intelligence Compute Resources, Data Commons Collaborative, Sector Spark, The Open Accelerator, Grow with Google, Additional AI Training Resources, Future Ready, and Summer Career Academy in AI.[16]
B. Artificial Intelligence Compute Resources
In May 2025, Governor Healey announced a $31 million state grant to expand access to sustainable high-performance computing for AI innovation through the Massachusetts Green High Performance Computing Center in Holyoke.[17]
The MA AI Hub describes the Artificial Intelligence Compute Resources project as a $31 million grant to develop AICR at MGHPCC, operated by a consortium including Boston University, Harvard, MIT, Northeastern, UMass, and Yale. The project is intended to make high-performance GPU and compute infrastructure more accessible to universities, startups, businesses, and communities across Massachusetts and beyond. The AICR cluster is the first phase of a planned $120 million public-private investment, with at least 40% of compute time dedicated to startups, entrepreneurs, and partner institutions including non-member colleges and nonprofits statewide.[11]
C. Data Commons Collaborative
The MA AI Hub’s Data Commons Collaborative is a flagship initiative to unlock responsibly governed, high-quality shared data to fuel AI innovation. Initial focus sectors include life sciences, health care, robotics, financial services, advanced manufacturing, climate tech, and education.[18]
D. Applied AI Models Innovation Challenge
In October 2025, the Healey-Driscoll Administration and MassTech announced $2,882,219 for seven projects through the Massachusetts AI Models Innovation Challenge. The program supports domain-specific AI model projects in sectors including health care, climate resiliency, robotics, and advanced manufacturing, and the funded projects brought more than $950,000 in matching funds from industry and academic partners.[19]
The Challenge had launched in February 2025 with the goal of positioning Massachusetts as a leader in applied AI innovation, emphasizing domain-specific models that are more energy-efficient and easier to commercialize than generalized AI systems.[19]
E. The Open Accelerator
The Open Accelerator is a Boston initiative involving the MA AI Hub, Red Hat, and IBM Ventures. It is designed to empower Massachusetts AI founders with networks, knowledge, skills, mentorship, workspace, GPU access, hackathons, and community.[12]
4. Practical Recommendations
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For an individual resident
Start with Google AI Essentials if you are new to AI. It is structured, practical, and includes prompting and responsible use. Move to the Google AI Professional Certificate if you want deeper fluency, a credential, portfolio-style activities, and the included three months of Google AI Pro.[1][2]
For a nonprofit leader
Have all senior staff and program staff complete Google AI Essentials, then identify a smaller group to complete the Google AI Professional Certificate. Use the MA AI Hub’s additional resources page to explore Microsoft and Anthropic learning paths for nonprofits.[10]
For a school or youth-serving organization
Use Google AI Essentials as a baseline for adult staff, track DESE guidance and Future Ready expansions, and consider adapting the High School Summer Career Academy model into a local pilot. DESE’s December 2024 recommendations are especially useful for building safe-use policies, educator training, privacy guardrails, and AI literacy standards.[6][7][9]
For small businesses
Use the Google AI Professional Certificate as a practical business-productivity training program. The course sequence covers planning, research, writing, content creation, data analysis, and app-building, all of which are directly relevant to small-business operations.[4]
For public-sector workers
State executive-branch employees should watch for agency rollout of the ChatGPT-powered AI Assistant and associated training. Public servants outside that rollout can also explore InnovateUS’s public-sector AI courses, which are free and oriented toward responsible use of generative AI in government.[13][15]
Endnotes
[1] Governor Healey and Google announced on February 26, 2026 that all Massachusetts residents would have access to AI and career certificate training programs at no cost through Grow with Google; the MA AI Hub identifies the specific programs and says no-cost access runs through December 31, 2027. Source: MA AI Hub, ‘Governor Healey and Google Announce New Statewide Partnership to Provide Free AI Training to Residents’; MA AI Hub, ‘Grow with Google.’
[2] The MA AI Hub Grow with Google page states that learners who enroll in the Google AI Professional Certificate receive three months of no-cost access to Google AI Pro. Source: MA AI Hub, ‘Grow with Google.’
[3] The Massachusetts AI Hub describes its mission as connecting, accelerating, and scaling AI initiatives for innovation, economic growth, workforce development, and public good. Source: MA AI Hub, ‘About MA AI Hub.’
[4] The MA AI Hub states that the Google AI Professional Certificate includes 20+ hands-on activities and courses in AI fundamentals, brainstorming and planning, research and insights, writing and communicating, content creation, data analysis, and app-building. Source: MA AI Hub, ‘Grow with Google.’
[5] The MA AI Hub states that Google AI Essentials includes modules on AI basics, productivity with AI tools, prompting, responsible AI, and staying ahead of AI developments. Source: MA AI Hub, ‘Grow with Google.’
[6] The Summer Career Academy in AI page states that the summer 2025 pilot engaged 50 high school students from eight districts and included AI fundamentals, Python, ethics and bias, career workshops, mentorship, and group projects. Source: MA AI Hub, ‘MA High School Summer Career Academy in AI.’
[7] The Future Ready initiative was launched in June 2025 as a $135,000 professional-development pilot in partnership with Project Lead The Way, supporting teachers in 45 classrooms and estimated to reach more than 1,600 students. Source: MA AI Hub, ‘Healey-Driscoll Administration Launches Future Ready: AI in the Classroom for Educators.’
[8] The MA AI Hub describes Future Ready as a 50-hour professional-development pilot for high school STEM educators launched with the Massachusetts STEM Advisory Council and Project Lead The Way. Source: MA AI Hub, ‘Future Ready.’
[9] DESE’s December 2024 AI recommendations call for AI literacy resources, curated AI tools, enterprise-level AI tools, no-cost professional learning for educators, student data privacy training, sample district policies, integration into educator preparation, and incorporation of AI literacy into Massachusetts curriculum frameworks. Source: Massachusetts Department of Elementary and Secondary Education, ‘Integrating Artificial Intelligence: Massachusetts AI Task Force Recommendations.’
[10] The MA AI Hub’s Additional AI Training Resources page includes Microsoft and Anthropic resources and a nonprofit-focused Microsoft AI training pathway. Source: MA AI Hub, ‘Additional AI Training Resources.’
[11] The Artificial Intelligence Compute Resources page states the AICR project is a $31 million grant at MGHPCC, the first phase of a planned $120 million public-private investment, and that at least 40% of compute time will be dedicated to startups, entrepreneurs, and partner institutions including non-member colleges and nonprofits statewide. Source: MA AI Hub, ‘Artificial Intelligence Compute Resources.’
[12] The Open Accelerator describes itself as a Boston initiative involving MA AI Hub, Red Hat, and IBM Ventures to support Massachusetts AI founders with mentorship, GPU access, hackathons, and community. Source: The Open Accelerator website.
[13] The February 2026 state announcement describes a ChatGPT-powered AI Assistant for the executive branch, covering nearly 40,000 state employees in a phased rollout. Source: Mass.gov, ‘Governor Healey Announces Massachusetts to Become First State to Deploy ChatGPT Across Executive Branch.’
[14] WBUR reported that the ChatGPT-powered assistant would be phased in across the nearly 40,000-employee executive branch and that optional training programs would be rolled out for employees using the assistant. Source: WBUR, ‘Massachusetts launching ChatGPT assistant across executive branch.’
[15] EOTSS announced a November 2025 partnership with InnovateUS to provide generative AI training to Technology Services and Security staff. Source: Mass.gov, ‘EOTSS Announces Workforce Training Partnership with InnovateUS.’
[16] The MA AI Hub homepage lists program areas including Grow with Google, Applied AI Models Innovation Challenge, Additional AI Training Resources, Future Ready, and other AI Hub initiatives. Source: MA AI Hub homepage.
[17] The May 2025 MA AI Hub update announced a $31 million state grant for AI compute resources, the hiring of the AI Hub’s first director, and exploration of an IBM/Red Hat startup accelerator. Source: MassTech, ‘Governor Healey Advances State’s AI Leadership with Major Investments in Massachusetts AI Hub.’
[18] The MA AI Hub’s Data Commons Collaborative is intended to unlock responsibly governed, high-quality shared data to fuel AI innovation in sectors including life sciences, health care, robotics, financial services, advanced manufacturing, climate tech, and education. Source: MA AI Hub, ‘Data Commons Collaborative.’
[19] The October 2025 AI Models Innovation Challenge awards totaled $2,882,219 for seven projects, with more than $950,000 in matching funds. Source: MassTech, ‘Healey-Driscoll Administration Announces $2.9 Million in Awards through Massachusetts AI Hub.’
Bibliography / Sources Researched
Accessed June 16, 2026. Sources listed here include those cited directly and related sources reviewed for context.
Self-Leadership: Get Shit Done Without Losing Yourself. CHUMS, The CHU Management System.
Best Practices, CHU Recommends, Feature, Featured CE Articles, Intellectual Leadership, Leadership, Personal, ProductivitySummary / Net
Most senior executives do not have a productivity problem. They have an overload, attention, energy, relationship, and prioritization problem. The CHU Management System — CHUMS — is my personal operating system for getting shit done without letting the loudest, most urgent, or most emotionally reactive issue take over my life.
CHUMS is not a software platform, productivity app, or universal template. It is a weekly paper-and-Google-Sheet discipline I use to manage work, family, relationships, health, values, commitments, follow-up, and focus. The specific system is mine. The larger point is universal: every serious leader needs a system.
Why I Built CHUMS
I originally developed what became the CHU Management System during the seven years I founded and led The Loyalty Group, that created, launched and grew the AIR MILES Canada shopping reward program and database/ internet marketing business. Like many entrepreneurs and CEOs, I had too many priorities, too many people depending on me, too many ideas, too many meetings, too many relationships to maintain, and too many chances for something important to fall through the cracks.
Later, in 2016, I began using the system again while working at Year Up, where my work involved roughly 60 internal colleagues, more than 100 external partners, and 12 locations. That was the kind of operating environment where “I’ll remember that” is not a system. It is a fantasy. It also served me well when I used it in 2020 as Vice Chair of Alan Khazei’s congressional campaign in Massachusetts 4th District. I have described well-funded competitive political campaigns are like an “internet start-up on speed.” Personal management systems are a life saver in these environments.
CHUMS is heavily influenced by Stephen Covey’s The 7 Habits of Highly Effective People, David Allen’s Getting Things Done, Jim Loehr and Tony Schwartz’s The Power of Full Engagement, James Clear’s Atomic Habits, conversations with friends and colleagues, and a lot of agile trial and error. It is also influenced by a simple, humbling reality I have learned repeatedly in my life: when I use my system, things get done. When I don’t, things fall off my plate, and I drift into a growing pile of “urgent and important” crises.
The “triple-infinity” symbol represents the need for leaders to keep the most important things, actions, tasks, and priorities among your business, your family, and yourself in balance. This was something emphasized for me personally during the many years I was a member of The Young Presidents Organization, while CEO of The Loyalty Group in Toronto.
The name is slightly tongue-in-cheek: CHUMS stands for CHU Management System. But the point is serious. CHUMS is a personal operating system for self-leadership.
And for senior executives, self-leadership is not optional. If you cannot manage your own attention, energy, values, relationships, commitments, and follow-up, you will eventually transfer your internal disorganization to the organization you lead.
The Big Idea: Senior Executives Need a Personal Operating System
Most executives already have organizational systems. They have strategic plans, dashboards, CRM systems, board packets, financial models, Slack channels, calendars, and performance reviews. But many do not have an equally disciplined personal system for deciding what deserves attention this week, what must happen today, who needs care and connection, what values must be lived, and what can safely wait.
That gap is dangerous.
CHUMS is my attempt to solve that problem for myself.
It has 11 parts:
Here is how each part works.
1. Daily and Weekly Self-Monitoring
What is it?
The first section of CHUMS is a simple weekly tracker. It includes the people and practices I want to stay connected to each week.
For me, the initials and abbreviations on the left-hand side are personal:
J = Jordan, my oldest child.
M = Myles, my youngest child.
LEZ = my wife, Laura.
X = exercise.
S/G = spiritual practice and gratitude.
SSU = my sister Sharon.
CUF = my sister Cecilia.
This section is influenced by Stephen Covey’s “Sharpen the Saw” habit — the idea that we must renew ourselves across the physical, mental, emotional/social, and spiritual dimensions if we want to remain effective.
How do I use it in practice?
Each week, I track whether I have engaged with these people or practices. Did I connect with my kids? Did I connect meaningfully with Laura? Did I exercise? Did I do anything spiritual or gratitude-oriented? Did I check in with my sisters?
This is not meant to be a guilt machine. It is a visibility tool.
A senior executive can easily spend the week serving clients, employees, donors, investors, board members, political allies, or external stakeholders — and then realize that the people and practices that matter most personally received the least intentional attention.
This section makes that visible.
Why include it?
Because what gets tracked gets attention.
Executives are usually very good at tracking business metrics. Revenue. Margin. Retention. Pipeline. Fundraising. Cash runway. Employee engagement. Customer satisfaction. But we often fail to track the personal relationships and renewal practices that keep us grounded and human.
This section reminds me that productivity is not just doing more work. It is living the right life while doing the work.
If I say my family matters, my system should reflect that. If I say health matters, my system should reflect that. If I say spirituality, gratitude, or reflection matters, my system should reflect that.
Otherwise, my stated values and my calendar can be in conflict.
2. Personal Must-Dos
What is it?
This is the short list of the most important personal things I must get done this week.
Not someday. Not eventually. This week.
These are not necessarily the biggest strategic projects. They are the personal commitments that matter enough to deserve explicit weekly attention.
How do I use it in practice?
When I update CHUMS on Sunday night, I ask: what personal things must happen this week for me to feel I have honored my commitments to myself and the people closest to me?
That might include a family follow-up, a personal finance task, a health appointment, a home issue, a difficult conversation, a commitment to Laura, a call with one of my kids, or something I have been avoiding.
The key is that it gets written down in a place I will see all week.
Why include it?
Because personal obligations do not become less important because they are not professional.
Many senior leaders unconsciously privilege work commitments over personal commitments because work commitments come with meetings, deadlines, assistants, board pressure, customers, and money attached. Personal commitments are often quieter. They do not always send calendar invites.
But neglected personal commitments create stress, guilt, friction, and emotional drag. They also erode integrity. If I repeatedly fail to do what I told myself or my family I would do, I become less trustworthy to myself.
This section protects my personal credibility.
3. Weekly To-Do List
What is it?
This is the classic weekly capture list, heavily influenced by David Allen’s Getting Things Done. Allen’s GTD method emphasizes capturing what has your attention, clarifying what it means, organizing it where it belongs, reviewing it regularly, and then engaging with the right work. Allen advises against keeping a long daily to-do list, as spending time on most nights moving things that did not get done that day to the next can become energy sapping.
In CHUMS, this becomes a weekly written list of work and personal to-dos.
How do I use it in practice?
The list is organized along the right-hand side of the matrix roughly by my most important clients, projects, and people. CHUDOW – stands for CHU (to) Do Other Work related and CHUDOP – for CHU (to)Do Other Personal. IC/ AI is a bucket to make sure I build intellectual capital every week and AI to remind me to keep up with the latest developments in artificial intelligence.
The point is to avoid what I call the “four-year-old soccer problem”: every kid chases the same ball.
Organizations do this. Leaders do this. I do this. The urgent issue gets the ball, and suddenly every person, meeting, and ounce of energy is chasing it while other important areas are neglected.
During the week, whenever something comes up, I write it down. I do not rely on memory. I do not assume I will magically remember it after the meeting, after the bike ride, after walking Izzy, or after the next call.
On Sunday night, I update the system in Google Sheets, print it, and carry it with me. Yes, I am trying to eliminate paper from much of my life. But for this, paper still works. I can fold it in half and keep it in my jacket pocket. A few more folds and it fits in my jeans or cycling jersey pocket. If something occurs to me while walking the dog, riding my bike, sitting in a meeting, or thinking through a client issue, I can write it down quickly and get it out of my head.
Why include it?
Because the brain is a terrible office.
It is wonderful for creativity, empathy, pattern recognition, judgment, and strategy. It is not wonderful as a storage facility for 73 unrelated commitments.
David Allen’s core insight is that unprocessed commitments create mental residue. The more open loops we carry in our heads, the less fully present we are for the task or person in front of us.
For senior executives, this matters enormously. Your attention is one of the most valuable assets in the organization. If your mind is cluttered with unrecorded obligations, you are spending executive attention on memory management instead of judgment.
Writing things down is not clerical. It is strategic.
4. Most Important Strategic Relationships and Priorities
What is it?
The last two columns force me to list 1-3 relationships in appropriate areas of focus that I must invest in during the week and to whittle down the many to-do items under each to the 1-3 that simply must get done by the end of the week.
How do I use it in practice?
I use the categories to force a broader scan. The most important relationships and priorities in each category ensure that I narrow down and prioritize those actions and people that are most important for the period.
What needs attention in each major area? What am I ignoring? Which relationships need follow-up? Which strategic priority is quietly drifting? Which area has become too dominant? Which important work is not urgent enough to scream yet?
This section turns my week from a random pile of tasks into a portfolio.
Why include it?
Because executive work is portfolio work.
A CEO or senior leader is rarely responsible for one thing. You are responsible for strategy, people, capital, culture, customers, operations, governance, risk, external relationships, and often your own health and family system at the same time.
If you only manage the loudest category, you will underperform in the quieter ones.
This section creates balance. It forces me to ask: am I paying attention to the whole field, or am I chasing the ball?
5. Calendar Blocking
What is it?
I use this section to block out my calendar, identify major client and board meetings, presentations, and color-code travel dates. It gives me a high-level overview of the most important personal and business events that will require major prep work or personal planning over the next two months.
How do I use it in practice?
At the beginning of the week, I look at the major events and commitments coming up over the next 8 weeks and make sure the requisite pre-work is somewhere on the list above or elsewhere recorded in the system.
Why include it?
This section helps me connect intention to time. It also helps me see overcommitment before it becomes failure. If I have 30 hours of meetings and 50 hours of work I claim must get done, the math is not going to work. Pretending otherwise does not make me ambitious. It makes me irresponsible.
Calendar visibility is a form of honesty.
6. Must Do Today Before I Sleep
What is it?
This is the daily hard-stop section: what must I do today before I go to sleep? With all due respect to David Allen, this is my daily to do list. I use an erasable pen to fill it in each morning before starting my day and clear it by the end.
This includes any true Covey Quadrant I items — things that are both urgent and important. But it is not limited to crisis work. It can also include one or two actions that simply cannot be allowed to slip another day.
How do I use it in practice?
Each day, I identify the small number of things that must happen before I sleep. Not 20 things. Not everything. The real must-dos.
This is the section that confronts the day directly.
At the end of a long day, when I am tired, distracted, or tempted to postpone something uncomfortable, this section asks: what did I promise myself would get done today?
Why include it?
Because weeks are won or lost in days.
A beautiful weekly plan can still fail if there is no daily commitment mechanism. This section turns the system into action.
It also helps distinguish between “I was busy” and “I did what mattered.” Senior executives can be busy every minute of the day and still avoid the one conversation, decision, note, workout, apology, or follow-up that actually mattered most.
The phrase “before I sleep” creates useful pressure. It makes the commitment concrete.
7. Rolling Discussion Lists, House Perfect List, and Someday/Maybe
What is it?
The right side of the front page is a set of rolling lists.
I use it to track topics I need to discuss with team members, client contacts, consultants, family members, friends, and collaborators. Instead of immediately calling or texting every time a thought occurs to me, I write it down under the relevant person or project.
This section also includes ideas for articles, my “House Perfect List” — smaller or larger things I need to do around the house or yard — and a “Someday/Maybe” list, borrowed directly from David Allen’s Getting Things Done framework.
How do I use it in practice?
When I think of something I need to discuss with someone, I put it in that person’s box. Then, when we meet or talk, I have a ready-made agenda.
This improves the quality of meetings and reduces the number of interruptive one-off communications. Instead of spraying texts, emails, and half-formed thoughts throughout the week, I batch topics for the right moment.
For the Collaboration Evangelist articles, House Perfect List and Someday/Maybe list, the same principle applies: capture without overcommitting. Not everything belongs on this week’s must-do list. Some things simply need a trusted parking place.
Why include it?
Because not every thought deserves immediate action.
This is especially important for senior leaders. Executives generate a lot of ideas, questions, concerns, and follow-ups. If every thought becomes an immediate interruption for someone else, the leader becomes a chaos machine.
A rolling discussion list creates discipline. It respects other people’s attention. It makes meetings better. It lets me remember without reacting.
The Someday/Maybe list is equally important because it gives ideas a place to live without letting them hijack the week. That is a critical distinction. A good idea is not always a current priority.
8. Live Your Values
What is it?
The left side of the back page is my values reminder. It lists the personal values and commitments I am trying to live.
The emphasis is on trying.
This section is not a declaration that I am fully living these values every day. It is a reminder of who I want to be and how I want to show up.
For me, this includes being the best father I can be, being a good brother, honoring my parents by how I live and work, being respectfully and graciously honest, taking care of myself physically, emotionally, intellectually, and spiritually, using my experience and resources to help others, fighting for justice, being conscious of the impact of my words and actions, not judging others, being the best partner to Laura and best human to Izzy I can be, and passionately promoting what I believe in.
How do I use it in practice?
I keep it on the back page so I see it repeatedly. It is not a framed statement on a wall. It is part of the weekly system.
That matters. Values should not be separated from execution. They should shape execution.
When I review my week, I am not only asking, “What do I need to get done?” I am also asking, “Who am I trying to become while I get it done?”
Why include it?
Because productivity without values can become efficient selfishness.
Many senior executives are extremely productive by conventional measures. They clear inboxes, make decisions, hit numbers, raise capital, close deals, and move fast. But speed and volume are not the same as leadership.
This section forces me to connect action to identity. James Clear’s Atomic Habits argues that lasting behavior change is connected to identity — not merely what goal we want to achieve, but what kind of person we believe we are becoming.
For me, CHUMS is not just a task system. It is an identity system. It reminds me that the goal is not simply to get more shit done. The goal is to get the right shit done in a way that is consistent with the person I am trying to be.
9. Feedback Is a Gift
What is it?
This section captures some of the most important feedback I have received from supervisors, mentors, colleagues, friends, and role models.
It includes short reminders — phrases that mean something specific to me. Some are blunt. Some are cryptic to anyone else. Some are deeply personal. The point is not that the phrases would work for everyone. The point is that they work for me.
How do I use it in practice?
I keep the feedback visible so I cannot conveniently forget it.
Most of us say feedback is a gift. But in practice, we often treat feedback like a gift we would like to return, exchange, reinterpret, or bury in a drawer!
This section makes feedback operational. It turns lessons learned into recurring prompts.
Why include it?
Because leaders repeat avoidable mistakes when feedback is not converted into practice.
Senior executives often receive a lot of feedback, but not always honest feedback. The more senior you become, the more people manage you, flatter you, fear you, or soften the message. So, when you do receive real feedback, you should treat it as valuable data.
But insight fades. A hard lesson learned in March can be forgotten by June. This section keeps the lesson alive.
It also reinforces humility. No matter how experienced I am, I still have things to work on. The system reminds me of that every week.
10. Weekly Must-Do / Three Priorities Per Day
What is it?
The top right side of the back page can be used to list three priorities per day for the week. I have also used it as a blank note-taking space or as a place to list annual goals.
The three-priorities structure is intentionally simple.
How do I use it in practice?
For each day, I write the three things that would make the day successful.
Not 12. Not 27. Three.
This creates focus without pretending the day is clean, predictable, or fully controllable. It gives me a simple way to answer: if everything goes sideways, what are the few things that still matter?
Why include it?
Because constraints create clarity.
Executives are vulnerable to over-listing. We confuse ambition with volume. We put too much on the list and then feel behind before the day starts.
Three priorities force choice. Choice is the essence of strategy.
This section also creates a bridge between weekly planning and daily execution. The weekly system sets the field. The daily priorities define the play.
11. The 86,400 Reminder
What is it?
The final section is a visual reminder: 86,400; Be The Buffalo: Make The Next Best Choice; Proverbs 3:27.
There are 86,400 seconds in a day. Something I learned from my Pastor and Spiritual Entrepreneur friend Ray Hammond. For me, this symbol is a reminder to make the most of each day, confront challenges directly, not dwell endlessly on mistakes, and help others when I am able.
How do I use it in practice?
I do not use this as a productivity hack. I use it as a perspective hack.
Some days go well. Some do not. Some include progress. Some include mistakes. Some include conflict, disappointment, fatigue, or frustration.
The 86,400 reminder helps me reset. It says: this day is finite. Use it. Do not waste it in avoidance, resentment, self-pity, distraction, or fear.
Why include it?
Because time is the only non-renewable leadership resource.
Money can sometimes be raised. Staff can sometimes be hired. Strategy can sometimes be revised. Reputation can sometimes be rebuilt. But the day is spent once.
This does not mean every second should be optimized. That would be exhausting and inhuman. It means the day should be honored.
For a senior executive, the question is not merely “How do I get more done?” The deeper question is: “How do I make choices worthy of the time I have?”
Why the Whole System Works
CHUMS works for me because it integrates five things that are often separated:
Most productivity systems over-focus on tasks. They help you capture, sort, and complete work. That is useful, but incomplete.
Senior executive effectiveness is not just task completion. It is sustained judgment, emotional regulation, strategic focus, relationship management, physical and mental energy, and values-based action under pressure.
That is why The Power of Full Engagement is such an important influence on CHUMS. Loehr and Schwartz argue that managing energy, not just time, is central to high performance. A leader with a perfectly organized calendar but depleted physical, emotional, mental, or spiritual energy is not operating at full strength.
CHUMS also works because it is reviewed weekly. A system that is not reviewed becomes clutter. A system that is reviewed becomes trusted.
My Sunday night practice matters. I update the Google Sheet, print the system, and carry it with me. That ritual turns CHUMS from a document into a discipline.
James Clear’s Atomic Habits emphasizes the power of small, repeatable systems. The point is not heroic motivation. The point is designing a structure that makes the desired behavior more likely. CHUMS does that for me. It makes remembering, reviewing, prioritizing, connecting, and acting easier.
It also creates accountability. Even when no one else sees the sheet, I see it. I know what I wrote down. I know what I said mattered. I know what I avoided.
That is powerful.
What Happens When I Do Not Use It
When I do not use CHUMS, my productivity does not collapse all at once. It degrades gradually.
First, I start relying on memory. Then I miss small things. Then I become more reactive. Then important-but-not-urgent work slips. Then relationships become less intentional. Then my day gets captured by other people’s priorities. Then I feel behind. Then I start chasing the crisis of the day. Or, at worst, fall into a state of depression or anxiety paralysis.
Eventually, I end up with exactly what Covey warned about: too much time in the urgent and important quadrant because I failed to invest enough time in preparation, prevention, relationships, renewal, and planning.
When I do not use CHUMS, I also become more likely to confuse motion with progress. I answer emails, create PowerPoint slides, attend meetings, respond to texts, and knock off visible tasks, but I may not be moving the most important priorities forward.
Worst of all, I become more likely to drift away from my values. Not because I stop believing in them, but because I stop seeing them.
That is how it happens for many leaders. They do not abandon their values dramatically. They simply get busy. Then they get reactive. Then they get tired. Then they rationalize. Then the urgent crowds out the important. Then, slowly, the life they are living stops matching the life they say they believe in.
CHUMS is my defense against that drift.
The System Is Mine. The Need Is Universal.
I do not recommend that everyone copy my exact system.
In fact, you should not. CHUMS is highly tailored to my priorities, family, relationships, passions, work, quirks, and flaws. Your system should be tailored to yours.
But I strongly believe every senior executive needs some kind of personal operating system.
It does not have to be paper. It does not have to be a spreadsheet. It does not have to have 11 parts. It does not have to use my language. It does not have to be called anything clever.
But it should help you answer these questions every week:
What matters most?
Who needs my attention?
What must get done?
What can wait?
What am I avoiding?
Where is my energy going?
Am I living my values?
What feedback am I trying to practice?
What must I do today before I sleep?
If your system cannot answer those questions, it is not yet a leadership system. It is just a task list.
A Note for CEOs and Senior Leaders
The higher you rise, the less anyone else can fully manage your attention for you.
An assistant can manage your calendar. A chief of staff can manage your meeting flow. A COO can manage execution rhythms. A board can help set accountability. A coach can ask hard questions.
But no one else can decide, week after week, what kind of leader you are trying to be.
That is your work.
CHUMS is one way I do that work.
It helps me get shit done. But more importantly, it helps me get the right shit done, with the right people in mind, for the right reasons, while trying — imperfectly but seriously — to live the values I claim to hold.
For me, that is the real productivity challenge.
Not just doing more.
Doing what matters.
Building Your Own GSD System
In my CEO coaching and productivity accountability partner (PAP) practice, this is one of the places where I believe I can be most helpful.
Not by handing leaders my system and pretending it will fit their lives.
But by helping them build their own.
A strong executive productivity system should be personal, practical, visible, reviewable, and honest. It should include the work, but not only the work. It should include relationships, energy, renewal, values, feedback, and follow-through. It should be simple enough to use when life gets busy, because that is exactly when the system matters most.
The goal is not to become a productivity robot.
The goal is to become a more intentional leader.
A leader who knows what matters.
A leader who writes it down.
A leader who follows up.
A leader who protects time and energy.
A leader who lives values in practice, not just in speeches.
A leader who gets shit done without losing himself or herself in the process.
That is what CHUMS does for me.
And that is why I still use it. To paraphrase my son Myles, when I don’t use it, “Shit goes left.”
Here’s a link to the front page google spreadsheet you can edit to start building your own system.
Endnotes
APPENDIX I – FULL FRONT PAGE (BEFORE FOLDING)
APPENDIX II – FULL BACK PAGE (BEFORE FOLDING)
Want to Get Started with AI? Learn How to Ask Better Questions. Three free or publicly accessible ways for students, educators, nonprofit leaders, and executives to learn the basics of AI prompt engineering.
AI, Best Practices, CHU Recommends, Collaboration, Intellectual Leadership, Leadership, Personal, Productivity, TechnologyOne of the most common questions I hear from nonprofit leaders, educators, students, and executives is deceptively simple:
How do I get started with AI?
My answer: start with prompt engineering.
That phrase may sound technical, but the basic idea is very practical. Prompt engineering is simply the skill of designing and refining instructions so AI tools can produce more useful responses.1
For students, it can mean learning how to use AI to prepare for interviews, improve writing, explore career paths, or summarize complex material.
For executives, it can mean using AI to draft memos, pressure-test strategy, analyze donor or customer segments, prepare for meetings, test a hypothesis, or turn rough ideas into clearer plans.
The good news is that no one needs to spend thousands of dollars to learn the basics. Here are three online resources I recommend; IBM SkillsBuild and Microsoft Learn provide free/publicly available learning resources, while the OpenAI Academy collection is publicly accessible and designed around practical workplace AI skills.2
You can also start by reviewing my article GOOGLE’S 9 HOUR AI PROMPT ENGINEERING COURSE – IN 1 CARTOON!, summarized in this cartoon:
Use the right resource for the right audience: students, executives, or time-constrained workplace learners.
1. IBM SkillsBuild: Prompt Engineering – Shaping Better AI Responses
This is my top recommendation for students and workforce-development programs.
IBM SkillsBuild offers a structured prompt-engineering module. The course page says learners identify applications of different prompt types used in generative AI, explore prompt-engineering techniques, select techniques suited to specific tasks, and review best practices for effective prompts.3
IBM SkillsBuild also describes its broader AI learning resources for adult learners as offering free access, practical AI courses, and opportunities to earn credentials.4
For youth-serving organizations, career programs, and adult-learning settings, this is probably the strongest standalone option.
2. Microsoft Learn: Create Effective Prompts for Generative AI Training Tools
This is my top recommendation for busy executives, educators, and organizational leaders who want a fast, accessible introduction.
Microsoft Learn lists this as a beginner module with seven units, and identifies relevant audiences that include business users, K-12 educators, higher education educators, and school leaders. The module covers basic prompt-engineering concepts, elements of an effective prompt, and prompting best practices.5
For executives, I would use this as pre-work before a 60- to 90-minute AI workshop. It gives everyone a shared vocabulary without overwhelming them.
3. OpenAI Academy: ChatGPT at Work
OpenAI Academy offers a ChatGPT at Work collection that includes workplace-focused videos such as Introduction to Prompt Engineering, Advanced Prompt Engineering, ChatGPT for Data Analysis, Deep Research, ChatGPT Search, and other work-related ChatGPT topics.6
This is not a full course in the traditional sense, but it is a terrific executive appetizer. The videos are short, practical, and focused on real work: writing, analysis, research, data, reasoning, and productivity.
For leaders who are curious but time-constrained, this may be the easiest entry point.
How I Would Use These Resources
For a student program, I would assign the IBM SkillsBuild course and then run a hands-on session where students use AI to improve a resume, prepare for an interview, research a career, or summarize a reading.
For executives, I would assign the Microsoft Learn module and a few OpenAI Academy videos before a live workshop focused on real organizational use cases: preparing a board memo, drafting a strategy document, analyzing donor prospects, or creating a first version of an implementation plan.
For both audiences, I would add one essential lesson: AI is powerful, but it is not magic. Users still need to check facts, protect sensitive information, and apply human judgment.
Additional CHU Tips
A few tips I have learned recently through other courses I have taken:
Please let me know your best prompting insights as we all travel this journey of discovery together.
The Bigger Point
Prompt engineering is not just a technical skill. It is quickly becoming a basic workplace and learning skill.
The people who learn how to collaborate effectively with AI will have a major advantage. They will be able to think faster, draft faster, test ideas faster, and learn faster.
That does not mean AI replaces human judgment. In fact, the opposite is true. The better the human judgment, the better the AI collaboration.
So if you are a student, educator, nonprofit leader, or executive wondering where to begin, start here:
Learn how to ask better questions.
That is the first step toward getting better answers.
Endnotes
Suggested for web publishing: convert the superscript numbers in the article to bracketed links that jump to these notes, or keep the notes as a simple source list at the bottom of the post.
Bibliography / Links
Accessed June 5, 2026. All graphics in this draft are original graphics created for this article; no third-party images were used.
FOMAT & THE AI PRODUCTIVITY TSUNAMI: WHY AI ISN’T MAKING SOME OF US WORK LESS
AI, Best Practices, CHU Recommends, Featured CE Articles, Intellectual Leadership, Leadership, Personal, Productivity, TechnologyFOMAT & THE AI PRODUCTIVITY TSUNAMI: WHY AI ISN’T MAKING SOME OF US WORK LESS
By Craig Underwood | collaborationevangelist.com
Artificial intelligence can absolutely make us more productive. But for executives, consultants, entrepreneurs, nonprofit leaders and other ambitious “power users,” increased productivity does not necessarily mean increased leisure. It can mean more ideas to evaluate, more opportunities to pursue, more reports to read, more decisions to make and more AI “staff” to manage.
The leadership challenge is no longer simply learning how to use AI. It is learning how to manage the tsunami of valuable output AI can create — without sacrificing judgment, relationships, focus or our lives outside work.
Figure 1. FOMAT — Fear of Missing Agent Time! A humorous beginning to a very real AI-era management problem.
A few months ago, I created a cartoon with AI called FOMAT: Fear of Missing Agent Time! In the cartoon, I awaken in a panic at 2:47 a.m. with a horrifying thought: “OMG, my agent doesn’t have anything to work on!”
OK, the joke is bit absurd. But, for those of us who have learned how to use generative AI well — through iterative prompt engineering, deep research and custom agents — it may also feel disturbingly familiar.
I use ChatGPT, Gemini and, most prominently, Claude Cowork, where I have begun creating my own custom agents. I also use a Plaud device to record, transcribe and summarize meetings. These tools have not made me less engaged in my work. They have increased my energy and engagement and expanded what I believe I can accomplish.
That is exhilarating. It is also a little exhausting.
Because once you discover that an AI agent can research a market, analyze a strategic question, summarize a meeting, develop a donor profile, create a first draft, recommend next steps or identify ten new opportunities before you have finished your coffee, your problem changes.
The question is no longer: “How can I get this work done?” The new question becomes: “How do I possibly keep up with all the valuable work my AI tools can now help me generate?”
Welcome to the AI Productivity Tsunami.
AI IS MAKING MANY OF US MORE PRODUCTIVE. THAT DOESN’T MEAN WE ARE WORKING LESS.
There is increasingly strong evidence that generative AI can improve productivity.
In a controlled experiment involving professional writing tasks, researchers Shakked Noy and Whitney Zhang found that people using ChatGPT completed tasks approximately 40% faster while producing work rated 18% higher in quality.[1]
In another major workplace study, researchers Erik Brynjolfsson, Danielle Li and Lindsey Raymond examined the use of a generative AI assistant by more than 5,000 customer-support agents. Workers using AI resolved approximately 15% more customer issues per hour.[2]
Research conducted with Boston Consulting Group consultants found similar benefits — but also an important warning. Consultants using GPT-4 performed substantially better on tasks within AI’s capabilities, but performed worse when they relied on AI for tasks outside what researchers called its “jagged technological frontier.”[3]
In other words, AI can help us do more, faster and sometimes better. But productivity does not automatically turn into free time. For ambitious people, productivity frequently creates more ambition.
A ten-hour research project that once seemed impractical may now become feasible. A strategic analysis that once required weeks may now be developed in days. A promising idea that previously remained on the “someday” list can suddenly become an active project. And every new project creates more material to read, test, edit, prioritize, validate, and act upon.
Figure 2. Separate studies find meaningful productivity and quality gains from generative AI; they do not measure a single combined effect.
MY OWN EXAMPLE: AN AI AGENT THAT CREATES OPPORTUNITY — AND MORE WORK
I currently help lead fundraising work for Sportsmen’s Tennis & Enrichment Center (STEC), a Boston nonprofit that provides tennis, academic support, health and wellness programs and community opportunities for thousands of young people and their families.
One important assignment is identifying companies and foundations in Greater Boston that may be strong prospective donors or sponsors.
Historically, researching hundreds of companies from the Boston Business Journal’s Book of Lists would require enormous staff time or outsourced to a charitable consulting firm: identifying companies, researching corporate citizenship priorities, determining whether they give directly or through foundations, reviewing charitable contributions and tax filings when available, assessing connections to youth development or Boston communities, and prioritizing outreach.
Today, I have created an AI-enabled workflow to help research and evaluate more than 500 companies and prioritize those most likely to contribute to STEC.
That is a remarkable capability. But it does not mean I now have nothing to do. Quite the opposite.
The agent can help identify prospects, summarize evidence, assemble information and suggest rankings. But a human still must decide whether the information is accurate and current; which opportunities deserve immediate attention; what kind of contribution to request; who might know someone inside the company; and how to convert intelligence into genuine human connection and financial support.
The agent produces information. The leader must still exercise judgment. And when the information is excellent, the temptation is to pursue more of it. That is exactly how the tsunami begins.
Figure 3. The AI Agents’ Productivity Tsunami: when valuable output creates a new prioritization challenge.
RESEARCH NOW SUPPORTS THE TSUNAMI HYPOTHESIS
My cartoon was intended to be humorous. Then I discovered research suggesting it may also be descriptive.
In February 2026, Harvard Business Review published an article by Aruna Ranganathan and Xingqi Maggie Ye of the University of California, Berkeley Haas School of Business. Based on an eight-month ethnographic study at a roughly 200-person U.S. technology company, their in-progress research found that generative AI did not simply free up time. Employees worked faster, broadened the scope of tasks they undertook and extended work into more hours of the day, often without being instructed to do so.[4]
The explanation is both simple and powerful: AI made doing more feel possible, accessible and rewarding. That is precisely the dynamic many AI power users are beginning to experience.
Figure 4. Recent Berkeley Haas research featured in Harvard Business Review closely mirrors the “productivity tsunami” hypothesis.
Microsoft’s 2025 Work Trend Index provides additional context. Its telemetry analysis found that the top 20% of Microsoft 365 users by ping volume received 275 meetings, emails or chats per workday — equivalent to one interruption approximately every two minutes during an eight-hour workday.[5]
Now add AI agents that can continuously create additional analyses, drafts, ideas, recommendations and action items. AI may help close the capacity gap. But unless leaders deliberately redesign how they work, it can also pour more water into an already overflowing ocean.
Figure 5. Microsoft finds severe input overload among its most highly pinged Microsoft 365 users.
To be clear, this is not everyone’s experience. OECD research on small and medium-sized enterprises using generative AI found that 32.7% reported decreased workload, while 11.8% reported increased workload.[6]
My hypothesis is narrower: for highly motivated leaders and professionals who become skilled AI users — especially those creating their own agents — AI may initially increase rather than decrease total work because it expands the frontier of worthwhile opportunity.
Figure 6. An important counterpoint: among SMEs using generative AI, more reported decreased workload than increased workload.
WHAT ABOUT JOB LOSS?
There is another side of this discussion that leaders should not ignore.
Some highly repetitive, process-oriented and entry-level roles are likely to shrink or change substantially as AI becomes better at routine research, summarization, document preparation, basic customer support, scheduling, data entry and administrative work.
The International Labour Organization has found that clerical occupations remain among those most exposed to generative AI transformation.[7] The World Economic Forum’s Future of Jobs Report 2025 similarly identified clerical and administrative positions — including data entry clerks, bank tellers, cashiers and administrative assistants — among the roles employers expect to decline most quickly through 2030.[8]
I believe leaders have the responsibility – some would say the moral imperative – to help employees learn how to use AI responsibly and efficiently and to move toward higher-value work involving judgment, relationships, creativity, influence, and accountability. This is especially important for nonprofit leaders of work-force development and college access/ success organizations, and those whose missions include preparing those they serve for jobs of the future. Imagine the different impression a young entry-level job or internship applicant would leave on a prospective employer when answering the question “Do you use AI?” between:
“[My training organization] has provided responsible AI literacy training and prompt engineering to all of its students.”
And
“I don’t use AI much at all.” Or even worse, “We are prohibited from using AI.”
THE SOLUTION: SURF. PRIORITIZE. IMPACT.
In the upper-left corner of my Productivity Tsunami cartoon is a simple phrase: Surf. Prioritize. Impact. I think this may be the practical leadership framework we need.
1. SURF: USE AI ENTHUSIASTICALLY — BUT PURPOSEFULLY.
The answer is not to run away from AI. It is too valuable. These tools can help leaders think better, research faster, communicate more clearly and identify opportunities that might otherwise go undiscovered.
But surfing a wave is different from letting it drown you.
Leaders should be explicit about what they want AI to help accomplish. Or, to borrow a phrase from Steven Covey’s The 7 Habits of Highly Effective People, “begin with the end in mind.” Create agents around important outcomes, not simply because creating another agent is interesting. Keep a running “AI opportunity parking lot” for intriguing ideas that do not deserve immediate action.
2. PRIORITIZE: SCHEDULE MEETING TIME WITH YOUR AI OUTPUT.
If you manage human colleagues, you do not expect them to slide dozens of excellent reports under your door every night and assume you will process them all by morning. The same discipline should apply to AI agents.
Schedule regular “meeting time” to review AI-generated work: a weekly (or daily) review of agent findings and recommendations; a clear decision rubric for what moves forward, what gets delegated, deferred or discarded; limits on active AI-enabled projects; and a requirement that all AI research and other generated content receive human verification and reflection.
The ability to generate more options makes disciplined prioritization more important, not less.
3. IMPACT: PROTECT THE WORK ONLY HUMANS CAN DO.
AI can produce a donor prospect analysis. It cannot build trust with a prospective sponsor over coffee. AI can summarize a leadership challenge. It cannot look a struggling colleague in the eye and ask the compassionate question that changes a relationship. AI can generate executive coaching frameworks and meeting notes. It cannot replace the human wisdom, accountability and courage required to make a difficult choice.
Leaders should protect time for relationships, reflection and judgment — the very human activities that make AI-generated intelligence meaningful. And we should place boundaries around our AI-enabled work lives. We must make sure that those of us who become highly productive through AI do not confuse increased output with increased impact — or silently turn every available evening, weekend, and quiet moment into additional time supervising our digital workforce. Given my passion for and excitement about “living on an AI vertical learning curve, “ my wife and I often have “AI discussion timeouts,” where I am not allowed to talk about it – much less, fire up Claude on my laptop to show her the latest cool thing we generated together.
Not every new research question needs to be launched tonight. Not every good idea requires immediate development. Not every idle agent needs a new assignment before bedtime.
Sometimes the most productive instruction we can give our AI agents is: “Good work. We will review this tomorrow.”
THE HUMAN STILL HAS TO CHOOSE THE SHORE
Artificial intelligence is not simply reducing the burden of work. For many of us, it is revealing a much larger universe of work that could be done.
That is not necessarily bad news. It means that a small consulting firm, nonprofit leader, entrepreneur or executive can now access analytical, creative and research capacity once available only to organizations with large staffs and large budgets.
But abundance creates a new leadership obligation. The best leaders in the AI era will not be the people whose agents generate the most reports, the most ideas or the most activity. They will be the people who know which waves to ride, which opportunities to ignore, which decisions require human judgment and when to step out of the ocean entirely.
Because the goal of AI is not to keep every agent busy. The goal is to help human beings create meaningful impact — while still leaving enough time and energy to be fully human.
Footnotes
[1] Shakked Noy and Whitney Zhang, “Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence,” Science 381, no. 6654 (2023): 187–192. Finding cited in the article abstract: average time decreased 40% and quality rose 18%.
[2] Erik Brynjolfsson, Danielle Li and Lindsey Raymond, “Generative AI at Work,” The Quarterly Journal of Economics 140, no. 2 (2025): 889–942. Published article reports a 15% productivity increase measured by issues resolved per hour.
[3] Fabrizio Dell’Acqua et al., “Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality,” working paper, 2023; subsequently published in Organization Science.
[4] Aruna Ranganathan and Xingqi Maggie Ye, “AI Doesn’t Reduce Work—It Intensifies It,” Harvard Business Review, February 9, 2026; Laura Counts, “AI Promised to Free Up Workers’ Time. UC Berkeley Haas Researchers Found the Opposite,” Haas News, February 18, 2026.
[5] Microsoft WorkLab, “Breaking Down the Infinite Workday,” June 17, 2025; Microsoft, 2025 Work Trend Index Annual Report. The 275-per-day figure is based on the top 20% of Microsoft 365 users by ping volume received; the two-minute figure is calculated over an eight-hour workday.
[6] Organisation for Economic Co-operation and Development, Generative AI and the SME Workforce: New Survey Evidence (2025), Figure 3.5.
[7] International Labour Organization, Generative AI and Jobs: A Refined Global Index of Occupational Exposure (2025).
[8] World Economic Forum, Future of Jobs Report 2025 (2025).
Research Bibliography
Brynjolfsson, Erik, Danielle Li and Lindsey Raymond. “Generative AI at Work.” The Quarterly Journal of Economics 140, no. 2 (2025): 889–942.
Dell’Acqua, Fabrizio, et al. “Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality.” Working paper, 2023; subsequently published in Organization Science.
International Labour Organization. Generative AI and Jobs: A Refined Global Index of Occupational Exposure. 2025.
Microsoft. 2025 Work Trend Index Annual Report. 2025.
Microsoft WorkLab. “Breaking Down the Infinite Workday.” June 17, 2025.
Noy, Shakked and Whitney Zhang. “Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence.” Science 381, no. 6654 (2023): 187–192.
Organisation for Economic Co-operation and Development. Generative AI and the SME Workforce: New Survey Evidence. 2025.
Ranganathan, Aruna and Xingqi Maggie Ye. “AI Doesn’t Reduce Work—It Intensifies It.” Harvard Business Review. February 9, 2026.
University of California, Berkeley Haas School of Business. “AI Promised to Free Up Workers’ Time. UC Berkeley Haas Researchers Found the Opposite.” February 18, 2026.
World Economic Forum. Future of Jobs Report 2025. 2025.
PMC 2025: The Spirit of Massachusetts, the Best of America, & Collaboration on Steroids!
CHU Recommends, Collaboration, News, PersonalPMC 2025: The Spirit of Massachusetts, the Best of America, & Collaboration on Steroids!
By Craig Hall Underwood, August 22, 2025
On Sunday morning, August 3rd, at 3:45 a.m., I stood in the pre-dawn glow watching hundreds of volunteers serve breakfast to thousands of riders before the final leg of the Pan-Mass Challenge (PMC). I had an epiphany. This scene — and the entire weekend’s event, where I completed my 11th PMC riding 170 miles from Wellesley to Bourne on Saturday and then on to Provincetown — revealed something powerful: The PMC embodies the Spirit of Massachusetts and the very Best of America, at a time when we desperately need both.
Read more
CHU RECOMMENDS: 4 Books, 1 Novel, & 3 Podcasts to Learn About & Keep Up with AI
AI, Best Practices, CHU Recommends, Collaboration, Featured CE Articles, Intellectual Leadership, Leadership, Productivity, TechnologyCHU RECOMMENDS: 4 Books, 1 Novel, & 3 Podcasts to Learn About & Keep Up with AI
Net/ Summary:
The Books:
The Novel:
The Podcasts:
More Details:
The Books:
The AI Driven Leader by Geoff Woods
This book blends strategic frameworks with real-world applications to help leaders understand and implement AI initiatives. Woods distills complex concepts into approachable tools for decision-makers.
Best for those wanting to translate AI potential into actionable leadership strategies.
AI First by Adam Brotman and Andy Sack
A practical playbook for leaders in any industry, AI First lays out the mindset and operating model needed to integrate AI into every level of an organization. The authors focus on customer experience, organizational design, and cultural transformation.
Best for those wanting to shift their business mindset to lead with AI from the ground up.
The Experimentation Machine by Jeffrey Bussgang
Bussgang, a venture capitalist and Harvard professor, explores how top startups use experimentation and data—especially with AI—to drive innovation. The book includes case studies from the tech world and frameworks for iterative learning.
Best for those wanting to understand how AI powers experimentation, especially in fast-moving startups and product teams.
Human + Machine by Paul R. Daugherty & H. James Wilson
This foundational book from two Accenture executives lays out how companies can combine human ingenuity with AI’s capabilities to reinvent business processes. It introduces the concept of “missing middle” jobs and augmented intelligence.
Best for those wanting to explore how AI augments—not replaces—human work across industries.
The Novel:
Coded Justice by Stacey Abrams
Abrams weaves a fast-paced legal thriller around a deepfake video and a high-stakes courtroom battle. Beneath the drama lies a thought-provoking look at AI, surveillance, and ethics.
Best for those wanting to grasp the real-world potential in health care and the risks of AI through a gripping, fictional lens.
The Podcasts:
The Most Interesting Thing in AI
Hosted by Nathan Benaich, this podcast offers sharp insights into the latest developments in AI, from research to policy. With guests from DeepMind, OpenAI, and VC firms, it’s both accessible and rigorous.
Best for those wanting to stay current on breakthroughs and big-picture trends in the AI ecosystem.
Beyond the Prompt – How To Use AI In Your Company
This podcast focuses on real-world implementations of AI tools across functions like sales, marketing, operations, and customer service. It’s geared toward business leaders and internal innovators.
Best for those wanting to learn practical, tactical ways to embed AI in daily business workflows.
Practical AI
Produced by Changelog, this long-running podcast brings together developers, data scientists, and entrepreneurs to discuss AI applications, tools, and ethics. Each episode balances technical depth with big-picture thinking.
Best for those wanting to understand how AI actually works—and how to build with it.
Please let me know your recommendations!
PS/ Disclaimer: One of the major debates about AI is “Does it/ can it/ or will AI make people lazy?” FYI, I have actually read all 5 books (and others I have not recommended) and listen to these podcasts every week, but after outlining the article, I asked ChatGPT to write the short summaries and “best for” recommendations for each bullet from the summary. I did not feel the need to edit what she wrote. Am I lazy or productive????
GOOGLE’S 9 HOUR AI PROMPT ENGINEERING COURSE – IN 1 CARTOON!
AI, Best Practices, CHU Recommends, Collaboration, Featured CE Articles, Intellectual Leadership, Productivity, TechnologySummary:
I recently completed Google’s Coursera course on prompt engineering to continue sharpening my use of AI—for both work and play. Like many of you, I want to get better, faster, and more reliable outputs from these powerful tools. To help me remember the core principles, I’ve adopted and modified a fun and memorable mnemonic (more on that in a second). But first, here’s the big idea:
To write effective prompts, you need to include seven core sections:
Teeny Tiny Crabs Riding Fat Enormous Iguanas
T = Task T = Tone C = Context R = References F = Format E = Engage I = Iterate
Include each of these elements in your prompt, and you’ll dramatically improve your results.
Where This Came From
Much of this is based on Tina Huang’s excellent YouTube summary, “Google’s 9-Hour Prompt Engineering Course in 20 Minutes.” (As someone once said: “If you steal from one person, it’s plagiarism. If you steal from many, it’s research.” I do a lot of research!)
The Google course itself is built around five inputs for effective prompt design:
Task, Context, References, Engage, Iterate
Their trainers introduced the mnemonic:
Thoughtfully Create Really Excellent Inputs
Tina found it hard to remember—and I agreed. She came up with a more vivid version:
Tiny Crabs Riding Really Enormous Iguanas
To that, I added two key concepts from my own experience—Tone and Format—to make it more complete.
The final version?
Teeny Tiny Crabs Riding Fat Enormous Iguanas
Why Add “Tone” and “Format”?
I’ve written hundreds of prompts across multiple platforms while using AI in professional consulting and nonprofit fundraising. Here’s why those two extra sections matter:
“I am the head of a boutique consulting firm that specializes in…”
This helps the AI match voice, audience, and style.
Gemini (Google’s AI) is particularly strong at formatting outputs into tables, and with one click, those can be exported into Google Sheets—saving tons of time.
A Quick Walkthrough of Each Section
“You are an expert content strategist writing a blog post on how to use AI in marketing research.”
This helps focus the AI’s responses.
Here’s a fun example:
I used ChatGPT to create a cartoon for a party invitation featuring my American Bully, Izzy. I uploaded a photo and asked for a cartoon of her in a chef’s hat next to a grill.
Iterating means providing clear, specific feedback. When the AI gets something wrong, tell it what and why. That’s how you teach it—and improve your results over time.
One Final Tip: Use Section Headers in Your Prompts
I have read several books and articles about AI and cannot remember where I learned this, but some recommend separating your prompts into the TTCRFEI sections and placing a “#” before and after the section title, e.g. #Task#. Experts suggest this will improve the quality of your outputs by adding clarity and structure, focus, and reducing ambiguity. By segmenting the prompt, you can make sure the AI knows what it is reading and how to use the information. For example, after reading # Task #, the AI knows the following text defines what it needs to do. After # Reference #, it understands that the text is supplementary information that it should consider. It also avoids or at least reduces misinterpretation.
Let’s Keep Learning Together
I hope this is helpful. If you’ve developed your own strategies for writing better prompts—or if you’ve tested this framework—I’d love to hear your thoughts. The more we share, the better we all get.
And Yes, I still recommend investing in the Coursera Google course.
One of my mantras as CEO of The Loyalty Group was “training without testing is little more than orientation.” My (and Tina’s) shortened version/cartoons notwithstanding, if you are relatively new to AI, I would recommend taking the Coursera course. It’s free, and if you have “fast ears” and can listen to the video portions at 1.5–2.0X speed, it takes far less than 9 hours. The two things I like best about it are: