The State AI Readiness Divide: Which States Are Actually Helping Their People Learn and Use AI?

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The 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:

Policy posture Meaning
Responsible AI enablement The state encourages AI adoption while providing guardrails, training, privacy protections, and human oversight.
Neutral risk management The state is studying or regulating AI but has limited public evidence of broad adoption support.
Restrictive or chilling policy The state’s actions appear more likely to discourage useful AI adoption than to enable it.

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.

Rank State / Jurisdiction Score Tier
1 Massachusetts 91 AI Readiness Leader
2 Utah 88 AI Readiness Leader
3 New York 86 AI Readiness Leader
4 New Jersey 84 AI Readiness Leader
5 California 82 AI Readiness Leader
6 District of Columbia 80 AI Readiness Leader
7 Maryland 78 AI Readiness Leader
8 Colorado 77 AI Readiness Leader
9 Ohio 76 AI Readiness Leader
10 Georgia 75 AI Readiness Leader

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.

Rank State Score Tier
42 West Virginia 42 Limited / Early
43 Wyoming 41 Limited / Early
44 New Hampshire 40 Limited / Early
45 Alaska 39 Limited / Early
46 North Dakota 38 Limited / Early
47 Louisiana 37 Limited / Early
48 Nebraska 36 Limited Public Evidence
49 Kansas 34 Limited Public Evidence
50 South Dakota 32 Limited Public Evidence
51 Arkansas 30 Limited Public Evidence

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.

  1. High K–12 / High AI: Compounding advantage

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.

  1. Lower K–12 / High AI: Leapfrog opportunity

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.

  1. High K–12 / Lower AI: Underleveraged education advantage

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.

  1. Lower K–12 / Lower AI: Double catch-up challenge

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:

  • access,
  • training,
  • permission,
  • examples,
  • guardrails, and
  • time to practice.

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:

  1. Give public employees safe AI access and training.
  2. Publish practical K–12 AI guidance.
  3. Use public higher education and community colleges to scale AI credentials.
  4. Create a public AI learning portal for residents and small businesses.
  5. Measure outcomes and publish use cases.

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

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Last 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

I was skeptical about the Ace for two reasons.  The first was its cost, with a list price of $624.99 – considerably more than the next-in-line Elemnt Roam.  The second was its size, measuring 4.9 inches long and 2.75 inches wide.  The price concern was partially alleviated by the Amazon sale price of $499.99, less than $100 more than the $424.99 Roam, which I needed to replace.  The significantly larger size turned out to be a “pro” throughout my ride, enabling highly readable multiple data fields during the ride.  The dual touchscreen and bottom buttons were also a plus during its test ride.  More importantly, the larger screen worked extremely well with the radar detector as you will read below.

Wahoo Fitness TRACKR Radar Tail Light

I was super skeptical about the radar taillight, which I knew was supposed to “alert” the rider when a car was approaching from behind.  I first heard of the Garmin version of this a few years ago when a friend bought the device.  Although I do not know how the Garmin device works, I envisioned it (and the newer Wahoo device as well) lighting up, flashing, or making a sound when a car was approaching from behind.  My first thought was “Why in the world would I need something like this? That’s what my ears are for!”  Priced at almost $250, this seemed like an unnecessary expense, not at all worth the price.  Once again, AI changed my mind and convinced me to try out the TRACKER Radar light.  When I was researching the ACE computer, Chat recommended the radar taillight, writing, “In my view, that’s the single biggest safety upgrade you can add to your bike, and it integrates seamlessly with the Wahoo display.”  Amazon returns are easy for me as I live only a mile from a UPS Store, so I decided to give it a try.

Boy, was my skepticism unfounded.  When I evaluated the ACE – TRACKR combination on my ride last weekend, I was wowed.  The first thing that impressed me was the way the radar lets you know of cars approaching from behind on the ACE screen.  On the left-hand side, a thin but completely visible line is present at all times when the radar is on.  With no cars approaching, it is green.  When a car approaches, the computer makes a distinct sound, and the bar turns yellow (approaching at moderate speed) or red (approaching faster).  With the ACE’s larger screen, this was easy to see out of my peripheral vision, even when I wasn’t looking directly down at the computer.  Even more impressive was a small rectangle or car icon that appears and moves inside the bar, showing you the distance between the car and your bike. The radar reads cars behind you starting at 150 yards (unless they are out of sight in a curve on the road).  I was surprised to realize that I can’t actually hear a car behind me until it is pretty close to my tail – even without headphones or music playing from an external speaker.  Before testing this setup, I never realized how close cars were to me before I was aware of the approaching danger.  Even more impressive was the radar’s ability to record and alert the rider to multiple oncoming vehicles.  Each vehicle has its own icon.  I am sure there have been times riding on a narrow street when I hugged the outside edge of the road after realizing a car was approaching or passing, then moved more to the center once I thought the danger had passed – oblivious to another car behind the one I was aware of.  At the risk of sounding hyperbolic, I truly believe this device could be life-saving.  Once again, AI was right to doubt my initial skepticism.

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

At $59, this is my least expensive recommendation.  I can’t recall how I stumbled upon this personal speaker a few years ago, but I truly love it.  The speaker is small and lightweight, and can either clip onto your bike jersey or be attached to your clothing with an integrated magnetic clasp. I don’t “feel” it on my shirt when riding.  The sound is excellent considering the speaker’s size and price, and loud enough that you can still hear your favorite jams even when going downhill at 35+ MPH.  Friends have also reported that when used as a speakerphone, they can hear me relatively clearly, and I can definitely hear them well.  If you are like me and get energized by the requisite class rock playlist (or Rocky soundtrack) when pushing up hills, I believe this is a no-brainer.  At least it is worth a test ride!

I hope these recommendations have been of value to others who follow the Velominati!  Please share your most-loved bike technology purchases.

 

Free AI Tool Access & AI Training Available to Massachusetts Residents

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by Craig Underwood | June 22, 2026

Net / Summary / Abstract
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?

CLICK HERE TO GO REGISTER FOR FREE GOOGLE AI AND OTHER TRAINING

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

Program What it provides Best fit
Google AI Professional Certificate Multi-course AI fluency certificate with hands-on activities and portfolio-building; includes three months of Google AI Pro for enrollees. Workers, students, jobseekers, small-business owners, executives, nonprofit staff
Google AI Essentials Introductory generative AI fundamentals, productivity, prompting, responsible use, and staying current. Beginners and busy professionals
Google Agile Essentials Agile project-management basics. Managers, team leads, project coordinators
Google Career Certificates Examples listed by MA AI Hub include Data Analytics, IT Support, and Project Management. Jobseekers, career changers, incumbent workers

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

CLICK HERE TO GO REGISTER FOR FREE GOOGLE AI AND OTHER TRAINING

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.

  1. Massachusetts AI Hub – Grow with Google. https://aihub.masstech.org/google-certificates Primary source for no-cost resident access, covered programs, Dec. 31, 2027 end date, course descriptions, and Google AI Pro access.
  2. MA AI Hub – Governor Healey and Google Announce New Statewide Partnership to Provide Free AI Training to Residents. https://aihub.masstech.org/news/governor-healey-and-google-announce-new-statewide-partnership-provide-free-ai-training Primary source for the February 26, 2026 announcement.
  3. Massachusetts AI Hub – About MA AI Hub. https://aihub.masstech.org/about-ma-ai-hub Source for mission and launch context.
  4. MassTech – Governor Healey Advances State’s AI Leadership with Major Investments in Massachusetts AI Hub. https://masstech.org/news/governor-healey-advances-states-ai-leadership-major-investments-massachusetts-ai-hub Source for $31 million compute grant, AI Hub director, and IBM/Red Hat accelerator exploration.
  5. Massachusetts AI Hub – Artificial Intelligence Compute Resources. https://aihub.masstech.org/artificial-intelligence-compute-resources Source for AICR, MGHPCC partnership, 40% compute allocation, and planned $120 million public-private investment.
  6. Massachusetts AI Hub – Additional AI Training Resources. https://aihub.masstech.org/additional-ai-training-resources Source for Microsoft AI Skills Navigator and Anthropic Academy references.
  7. Massachusetts AI Hub – Future Ready. https://aihub.masstech.org/future-ready Source for Future Ready pilot details.
  8. MA AI Hub – Healey-Driscoll Administration Launches Future Ready: AI in the Classroom for Educators. https://aihub.masstech.org/news/healey-driscoll-administration-launches-future-ready-ai-classroom-educators Source for $135,000 investment, 45 classrooms, and estimated 1,600 students.
  9. Massachusetts AI Hub – MA High School Summer Career Academy in AI. https://aihub.masstech.org/ma-high-school-summer-career-academy-ai Source for the summer 2025 pilot.
  10. Massachusetts AI Hub – Data Commons Collaborative. https://aihub.masstech.org/data-commons-collaborative Source for data-access strategy and priority sectors.
  11. MassTech – Healey-Driscoll Administration Announces $2.9 Million in Awards through Massachusetts AI Hub. https://masstech.org/news/healey-driscoll-administration-announces-29-million-awards-through-massachusetts-ai-hub Source for AI Models Innovation Challenge awards.
  12. Massachusetts Capital Budget – D050 Applied AI Hub. https://budget.digital.mass.gov/capital/fy25/beneficiary-agency/economic-development/eo-of-economic-development/d050/ Source for FY2025 and FY2025-FY2029 capital budget figures.
  13. Massachusetts DESE – Integrating Artificial Intelligence: Massachusetts AI Task Force Recommendations. https://www.doe.mass.edu/edtech/ai/integrating-artificial-intelligence.pdf Source for K-12 recommendations.
  14. Mass.gov – Governor Healey Announces Massachusetts to Become First State to Deploy ChatGPT Across Executive Branch. https://www.mass.gov/news/governor-healey-announces-massachusetts-to-become-first-state-to-deploy-chatgpt-across-executive-branch Source for public-sector AI Assistant announcement.
  15. Mass.gov – EOTSS Announces Workforce Training Partnership with InnovateUS. https://www.mass.gov/news/eotss-announces-workforce-training-partnership-with-innovateus Source for generative AI training for state technology staff.
  16. WBUR – Massachusetts launching ChatGPT assistant across executive branch. https://www.wbur.org/news/2026/02/17/massachusetts-healey-ai-chatgpt-contract Used to corroborate executive-branch AI Assistant rollout and employee training context.
  17. The Open Accelerator. https://the-open-accelerator.com/ Reviewed for AI startup ecosystem support, mentorship, GPU access, and community.
  18. CommCorp – Massachusetts Launches Free Online AI Training for Residents. https://commcorp.org/blog/massachusetts-launches-free-online-ai-training-for-residents Reviewed as workforce-system amplification of the Google AI training opportunity.
  19. MassHire Greater New Bedford – Free AI Training Now Available for Residents of Massachusetts. https://masshiregreaternewbedford.com/free-ai-training-now-available-for-residents-of-massachusetts/ Reviewed as MassHire dissemination of the Google training offer.
  20. MassHire Central Career Center – Free Google AI Training. https://masshirecentralcc.com/training/free-google-ai-training/ Reviewed as MassHire dissemination of the Google training offer.
  21. InnovateUS – Artificial Intelligence for the Public Sector. https://innovate-us.org/workshop-series/artificial-intelligence-for-the-public-sector/ Reviewed for free public-sector AI training context.
  22. Mass Open Cloud Alliance – $100 Million State AI Investment. https://massopen.cloud/100-million-state-ai-investment-leveraging-moc-alliance-infrastructure/ Reviewed for AI Hub/MGHPCC infrastructure context.
  23. Axios Boston – Free AI training is coming to all Massachusetts residents. https://www.axios.com/local/boston/2026/02/26/healey-google-ai-training-massachusetts Reviewed for media confirmation of Google partnership.
  24. Axios Boston – Massachusetts taps tech leaders for AI task force. https://www.axios.com/local/boston/2024/02/14/massachusetts-ai-task-force-members-chosen Reviewed for February 2024 AI Task Force context.

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.

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One 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:

  1. Before asking the AI engine to execute on your prompt, ask it to “Please ask me any clarifying questions to help you answer this prompt/ conduct this research/ create this image/ etc.” I predict you will be surprised at how good the questions are.  They almost always make me think deeper and more clearly about what I want from the prompt.
  2. If appropriate – and especially for larger research projects – you can ask the AI engine to “Convene a panel of experts to ask me questions about the prompt from different points of view.”
  3. To cut down on errors, mistakes, and “hallucinations,” include the following:
    1. “Do not make any thing up.”
    2. “Do not guess.”
    3. “Do not use any sources or research material older than [ __ ] months. (For AI related research, I never allow AI to go back any further than 6-12 months.)
    4. “Please footnote or endnote all references and facts and include a bibliography of sources used in your research, whether or not you actually used them in your response.”
    5. For all footnotes, endnotes and the bibliography, please make sure you include the date that source was published and not just the date you accessed the information.
    6. Please include links to the source documents whenever possible.
  4. Jeff Bussgang, who is the smartest person I know in this space, also recommends using “Please” and “Thank you” in our prompts, so I figure this can’t hurt!

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.

  1. IBM defines prompt engineering as writing, refining, and optimizing inputs to encourage generative AI systems to create specific, high-quality outputs. IBM also emphasizes that better prompts directly influence the quality, relevance, and accuracy of generative AI outputs. Source: IBM Think, “What Is Prompt Engineering?”
  2. IBM SkillsBuild describes its adult-learner AI resources as offering free access and courses in generative AI and related areas. Microsoft Learn makes the cited module publicly available. OpenAI Academy makes the cited ChatGPT at Work collection publicly accessible. Source: IBM SkillsBuild, “Artificial Intelligence”; Microsoft Learn, “Create Effective Prompts for Generative AI Training Tools”; OpenAI Academy, “ChatGPT at Work.”
  3. The IBM SkillsBuild course page for “Prompt Engineering: Shaping Better AI Responses” lists the course as eligible for registered learners, available in English, Arabic, Brazilian Portuguese, and Spanish, and four hours in duration. It says learners identify applications of different prompt types, explore prompt-engineering techniques, choose techniques suited to tasks, and review best practices for writing effective prompts. Source: IBM SkillsBuild, “Prompt Engineering: Shaping Better AI Responses.”
  4. IBM SkillsBuild describes its AI learning resources for adult learners as offering free access, AI courses, and opportunities to earn industry-recognized credentials. Source: IBM SkillsBuild, “Artificial Intelligence.”
  5. Microsoft Learn lists “Create Effective Prompts for Generative AI Training Tools” as a beginner module with seven units for audiences including K-12 educators, business users, higher education educators, and school leaders. Microsoft says the module covers the basic concepts of prompt engineering, the elements of an effective prompt, and prompting best practices. Source: Microsoft Learn, “Create Effective Prompts for Generative AI Training Tools.”
  6. The OpenAI Academy “ChatGPT at Work” collection is categorized under Workplace & Business, ChatGPT, Advanced & Builder Skills, and Work. The page lists videos including “Introduction to Prompt Engineering” (5:52), “Advanced Prompt Engineering” (8:50), “ChatGPT for Data Analysis” (4:46), “ChatGPT Search” (5:43), and “Deep Research” (7:06). Source: OpenAI Academy, “ChatGPT at Work.”
  7. All graphics in this draft are original graphics created for this article; no third-party images were used. Source: Original graphics created for Collaboration Evangelist draft, June 5, 2026.

 

Bibliography / Links

Accessed June 5, 2026. All graphics in this draft are original graphics created for this article; no third-party images were used.

  1. IBM Think. “What Is Prompt Engineering?” https://www.ibm.com/think/topics/prompt-engineering
  2. IBM SkillsBuild. “Artificial Intelligence.” https://skillsbuild.org/adult-learners/explore-learning/artificial-intelligence
  3. IBM SkillsBuild. “Prompt Engineering: Shaping Better AI Responses.” https://skillsbuild.org/college-students/course-catalog/prompt-engineering-shaping-better-ai-responses
  4. Microsoft Learn. “Create Effective Prompts for Generative AI Training Tools.” https://learn.microsoft.com/en-us/training/modules/create-prompts-for-generative-ai-training-tools/
  5. OpenAI Academy. “ChatGPT at Work.” https://academy.openai.com/public/collections/chatgpt-at-work-2025-02-14
  6. OpenAI Academy. “Introduction to Prompt Engineering.” https://academy.openai.com/public/videos/introduction-to-prompt-engineering-2025-02-13
  7. OpenAI Academy. “Advanced Prompt Engineering.” https://academy.openai.com/public/videos/advanced-prompt-engineering-2025-02-13

FOMAT & THE AI PRODUCTIVITY TSUNAMI: WHY AI ISN’T MAKING SOME OF US WORK LESS

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FOMAT & THE AI PRODUCTIVITY TSUNAMI: WHY AI ISN’T MAKING SOME OF US WORK LESS

By Craig Underwood | collaborationevangelist.com

Net/ Summary:

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.

 

CHU RECOMMENDS: 4 Books, 1 Novel, & 3 Podcasts to Learn About & Keep Up with AI

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CHU RECOMMENDS: 4 Books, 1 Novel, & 3 Podcasts to Learn About & Keep Up with AI

Net/ Summary:

The Books:

  • The AI Driven Leader by Geoff Woods
  • AI First by Adam Brotman and Andy Sack
  • The Experimentation Machine by Jeffrey Bussgang
  • Human + Machine by Paul R. Daugherty & H. James Wilson

The Novel:

  • Coded Justice, by Stacey Abrams

The Podcasts:

  • The Most Interesting Thing in AI
  • Beyond the Prompt – How To Use AI In Your Company
  • Practical AI

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!

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Summary:

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:

  • Tone is critical for anything involving writing. For example, when I want a professional yet conversational article (like this one), I say so in the prompt. Tone is often influenced by the Context section, especially when I open with something like:

“I am the head of a boutique consulting firm that specializes in…”
This helps the AI match voice, audience, and style.

  • Format becomes essential for large-scale research tasks. I’m currently using AI to research the charitable giving practices of 200+ companies to prioritize potential donors for several nonprofit clients. In this kind of work, it’s not enough to just get raw information—I need it delivered in tables, bulleted lists, or other clean formats that are easy to review and analyze.
    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

  • #Task#: Be as specific as possible. Clearly define what you want the AI to do. I often assign the AI a role or persona, like:

“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.

  • #Tone#: Set the mood—professional, playful, technical, academic, or conversational. If your writing has a target voice, state it upfront.
  • #Context#: Describe who you are, why you’re asking the question, and who the audience is. The better the AI understands your intent, the better its output.
  • #References#: Upload or include everything relevant: job descriptions, resumes, bios, organizational goals, website URLs—whatever provides grounding for the AI’s output. The more context it has, the smarter it behaves.
  • #Format#: Be clear about how you want the information delivered. Want a comparison table? A checklist? A three-paragraph summary? Tell it.
  • #Engage#: Don’t copy and paste the output blindly. Engage with it. Review it. Challenge it. AI tools still make plenty of hallucinations (mistakes), so check citations, dates, and factual claims before you use them. Ask for sources. Fact-check relentlessly.
  • #Iterate#: Rarely do you get the perfect response on the first try. In my experience, it takes 2–3 iterations to get what I really want.

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.

  • Version 1: Looked like Izzy, but she was wearing her leather biker jacket from the photo.
  • Version 2: I asked for a chef’s jacket—got that—but the dog didn’t look like Izzy anymore.
  • Version 3: I asked to combine the head from version 1 and the jacket from version 2—and nailed it!

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:

  1. Completing the course requires you to show actual prompts you have written, along with improvements you have made.
  2. The course has an exam at the end of each of the four major sections. If you score at least 80% on the exams, you receive this certificate:

CHU RECOMMENDS: PLAUD – THE ABSOLUTE COOLEST AI RECORDING DEVICE YET!

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CHU RECOMMENDS: PLAUD – THE ABSOLUTE COOLEST AI RECORDING DEVICE YET!

OK, I admit it.  I am obsessed with this thing called Plaud.

Last week, I did a trial run of the AI-powered recording device Plaud.  It blew me away.  Here’s what it is and (at least some) of what it can do:

  • About the size of a credit card length and width. About as thick as 3 credit cards.
  • Appears to be very well made – out of metal, doesn’t feel like cheap plastic. Buttons function well.
  • Comes in a magnetic carrying case that attaches it to the back of your phone – even works over my QUAD iPhone 16 Pro case I use for cycling.
  • Records your calls and meetings. Change between the 2 modes by moving the small button switch on the top up for calls and down for recording what’s in the room, including multi-person meetings, Zoom calls, etc.  When it is attached to your phone, you can use speaker phone, ear buds, or just listen to the call next to your ear.
  • Plaud transcribes your calls with extremely high accuracy, in my experience over the past week. I don’t always have the best diction, but so far, Plaud has interpreted my occasional “mumbles” flawlessly.
  • BUT THESE ARE TEENY WEENIE FEATURES COMPARED TO THE MAIN EVENT – Plaud uses AI to brilliantly convert your calls, meetings, etc. into highly accurate, incredibly well-formatted summary notes. I tested Plaud the first day I had it, using it to record and summarize two calls:
    • The first was a call with an executive from a solar panel company. During the call, we discussed the HOA Board’s challenges to install solar panels on my townhouse.  I also made a referral to the executive for a friend whose home is a much better candidate for solar power.  One feature of Plaud summaries I appreciate and am amazed by is its ability to capture the subject matter/ purpose of the call or meeting in the summary title (this is a screenshot of the actual notes):

    • The second was a 45-minute meeting with an executive from The Possible Zone (TPZ). TPZ is one of my favorite clients that offers after-school and summer programming to provide hands-on teaching and experience instilling entrepreneurial spirit and capabilities for underserved inner-city high school students. The discussion centered on a new project that Underwood Partners is working on for TPZ.  And again, Plaud did an excellent job both transcribing and especially summarizing the meeting.
  • Plaud’s AI transcription & summarization capabilities have been developed on GPT-4.1, o3-mini, Claude 3.7 Sonnet, and Gemini 2.5 Pro tools. It supports 112 languages.
  • The Plaud app and website offer professional templates tailored for various use cases. Transcriptions and summaries are easily shared with yourself or others.

An Important Caveat:

As I wrote in my article Overwhelmed by AI Ubiquity?  You’re Not Too Late To Get Started – Here’s How,  you cannot use AI as a “set and forget” tool.  As amazing as current AI capabilities are (and they get better every day), AI is far from infallible.  This applies to Plaud summaries as well.  Reading over the excellent summary of my TPZ call, I found one major error.  During the call, I asked the TPZ executive for feedback on our work to date.  One of the things she recommended was to always give the CEO a “read ahead” before presentations, even if they are in draft form.  Plaud inaccurately reported in the meeting summary that the CEO did not want to receive materials in advance of meetings.  Several AI experts have written that AI is usually 50-80% correct.  In my experience, AI – especially Google’s Gemini – is closer to 80%, but as this example demonstrates, AI can make BIG TIME mistakes.  Always, always, always check AI’s work and sources.

My Only Recommendation for Improving Plaud

I am sure this is an indictment of my personality and ego, but I am usually frustrated when asked for feedback on something and nothing comes to mind.  Fortunately, that is not the case with Plaud.  My only suggestion for making Plaud easier to use is to enable the device to be charged with the nearly ubiquitous USB-C cable.  Yes, unfortunately, Plaud comes with its own unique charging cable. Ugh!  It’s a small magnetic device that connects to the back of Plaud and can be easily used with the magnetic case on.  But it is yet another separate “single-use” charging cord I need to have in my office, backpack, car, etc., and will undoubtedly be lost!  I hate whoever recommended this “blades and razors strategy” to Plaud, slightly tarnishing an otherwise flawless customer experience!

How Much $$$ and Where to Buy

The Plaud device costs $160, and you can purchase it directly from the company at www.Plaud.ai or from Amazon.

Plaud provides transcription and storage services with a “freemium” model.  Plaud comes with 30-hour continuous recording, 60 days of standby, and 64GB of storage.  For the cost of the device, Plaud provides unlimited cloud storage and 300 hours of transcription and summary services per month.  For an additional $100/ year, the Plaud Pro-Plan provides 1,200 minutes of transcriptions and summaries monthly, or $150/ year provides unlimited transcriptions and summaries plus additional goodies.

HAVE FUN AND PREPARE TO BE OBSESSED!