October 22, 2026
Using AI well is a leadership skill, and it looks a great deal like delegating well
Written By Angelina Darrisaw
I founded C-Suite Coach, a leadership development consultancy, in 2015, in part because of the disconnect I saw between people and their leaders. That work has expanded to include AI readiness, teaching people to use artificial intelligence efficiently and ethically. So I paid close attention this year as companies went from ranking employees by how much AI they used to limiting how much they could use it based on spend.
As leaders, we were told adoption was the goal, and by March, The New York Times reported that employees at Meta and OpenAI were competing on internal leaderboards that tracked how much AI each person used. By May, some companies had spent a full year’s AI budget in three months—and spending limits followed, according to The Wall Street Journal. Any finance leader watching that happen would set limits, and I would have done the same as the spending outpaced expectations. Still, while a cap limits how much people use AI, the question of how well they use it remains.
The way we use AI has a cost outside the office. In April, the NAACP sued xAI, alleging that the company was running 27 gas turbines without an air permit to power its data center in disproportionately Black communities in the Memphis area. xAI argued it relied on Mississippi’s determination that no permit was required. The case is still being decided. According to a United Nations University report, 80% to 90% of AI’s energy goes to everyday requests. Each time we ask ChatGPT a question or create a prompt, we add a little to the demand that power plants meet. In a 2025 national survey by the nonprofit Jobs for the Future, 53% of Black respondents said they used AI tools daily or weekly, most often for their own learning, while 71% said they felt a need to gain new skills because of AI. Corporate training, however, has not kept up. In a November 2025 survey analyzed by economists at the Federal Reserve Bank of New York, 39% of employed respondents said they had used AI at work in the past year, while only about 16% said their employer offered any AI training. Most people using AI at work were never offered training, yet some companies still measured them by how much they used it.
Many companies bought these tools the way they buy other software licenses. They purchased seats, announced a transformation, and coached no one, so adoption was enforced and expected while their workers’ skills remained underdeveloped. You can see the gap in McKinsey’s 2026 global survey, where eight in 10 respondents said AI had improved their own productivity while only 37% said it had contributed to their organization’s earnings, about the same share as a year earlier.
Using AI well is a leadership skill, and it looks a great deal like delegating well. Picture someone who types “write a marketing plan” into a prompt and expects a masterpiece. A manager who expects people to read minds gets weak work back from both people and from AI. A good manager gives clear instructions, shares the context, shows what a good result looks like, and reviews the work before it goes out.
Delegating well also means giving feedback when the first draft comes back. When an answer from AI misses, many people abandon the chat and start a new one, which is what a manager does when they take an assignment back instead of providing insights into how it could improve. Strong leaders coach others by sharing what is working and what isn’t. That same practice produces better work from AI. How many times have we assumed an employee lacked competence when we had never delegated the work clearly?
Many of us are setting next year’s budgets this quarter, whether we run a division or own a business with 10 employees, which makes this a good moment to pair every AI spend with training requirements. In a small business, the waste shows up as lost time, rework, and mistakes that reach clients, so teaching people to use these AI tools well protects the margin directly. I would start by measuring how employees use AI today. We already rate buildings and appliances on how efficiently they use energy, and AI use at work deserves the same kind of rating, which is why my firm built one. One number I would track is output adoption, the share of AI-assisted work that people go on to use, and I would measure it again in 90 days. In a ten-person business, that can be as simple as asking each week which AI drafts were sent, which were reworked, and which were thrown away.
I would also ask the most skilled users on each team to teach the people around them. Most of all, I would ask owners and senior leaders to allow their teams to watch them review their own AI-assisted work, since teams use tools the way they see their leaders use them.
Imagine how different next year’s AI budget conversation would be if it started with what our people know how to do. What if we asked how much of the work AI produced last quarter was used? What if every spending limit came with an hour of coaching?
Paying for a tool and leaving people without the skills to use it, while the power it takes is generated in someone’s neighborhood, is negligence. The people on our teams are already using AI every day, and the least we owe them is to show them how to use it well.
Angelina Darrisaw is the founder and CEO of C-Suite Coach, a leadership development and executive coaching consultancy, and the creator of the AI Efficiency Score™, which assesses how effectively organizations and their employees use AI.
