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<\/p>\n<\/p>\n
The new divide between AI users and AI leaders<\/p>\n
getty<\/p>\n
Those who learn to run dependable teams of AI agents will pull ahead of comparable peers who continue using AI one chatbot at a time.<\/p>\n
Seven months ago, I am not sure I would have put it quite so confidently. But something has changed. Access to powerful AI models is becoming more widespread, so the advantage is moving to the people who can organize specialist agents, get them working at the same time, allow them to challenge one another, and then return the decisions that require human judgment.<\/p>\n
I do not think the advantage will belong to the person with the most agents or most subscriptions. That person may simply create more noise. It will belong to the person who can give them direction, set standards, provide useful context, and create the necessary boundaries.<\/p>\n
This is becoming a skills issue, not simply an access issue. In fact, I think it will become one of the defining divides in AI use over the coming years.<\/p>\n
I\u2019ve Experienced It First Hand<\/p>\n
Since the beginning of this year, I have been building and repeatedly updating an agentic team, sometimes known as an agentic workforce.<\/p>\n
I give the individual AI assistants names. I give them job descriptions and specialist responsibilities. On paper, it looks like an organization. But it has taken seven months for it to properly behave like one.<\/p>\n
For a long time, most of the activity still began with me. I opened the chatbot, supplied a task, waited for an answer, and then manually moved on to the next stage. If I did not start the work, nothing happened.<\/p>\n
In the last few weeks, through experimentation and quite a lot of correction, that has changed. The agents have started to discover what needs doing next and complete some of it without waiting for me.<\/p>\n
It\u2019s getting scarily good. My agentic team has access to the organizational knowledge we keep in Google Drive and other apps, and it works within a detailed set of rules. For example, every year, we host an online AI summit for educators. Weeks before this year\u2019s summit, the agents began coordinating a task list, a Kanban board, and a Gantt chart.<\/p>\n
The crazy thing is that they did not make those things for me. They made them for themselves and then started completing the tasks.<\/p>\n
I found out through the daily briefing from my AI chief of staff.<\/p>\n
His name is Brent, after David Brent from the U.K. version of The Office. All of the agents are named after characters from The UK Office. By design, Brent is the only member of the agentic team who communicates with me. He speaks to the wider team, filters what comes back, and tells me which decisions I need to make.<\/p>\n
This is so that I don\u2019t spend my day coordinating agents or reading reports from all of them. That would defeat the point. Brent knows what I need to hear, when I need to hear it, and what needs to come back to me.<\/p>\n
Anything consequential still does. If an email needs to be sent, money spent, or a commitment made in the real world, I decide. But much of the work leading up to that decision may already have happened before I even realized it needed doing.<\/p>\n
The big realization for me is that I am now prompting AI very, very little. I am learning how to lead it.<\/p>\n
Beyond Using AI One Turn At A Time<\/p>\n
I have used generative AI since before the arrival of ChatGPT in 2022. It has been fascinating to watch the phases of use develop.<\/p>\n
The first mass phase followed a simple rhythm. We asked, the model answered, we corrected it and asked again. If you ask most people how they use AI, the chances are they will describe some version of this. That\u2019s not a bad thing. It is reactive and sequential. <\/p>\n
The difference is that an agent can pursue a goal across several steps. It can choose tools, inspect what has happened, and decide what to do next within the boundaries it has been given. A coordinating agent like my chief of staff, Brent, can divide work among specialists, allow several strands to move at once, and then assemble what comes back.<\/p>\n
In simple terms: An assistant responds to a request. An agent pursues an outcome. An agent team coordinates several capabilities around that outcome.<\/p>\n
As powerful models become widely available, simply having access to one becomes less of an advantage. The advantage moves from the model itself to the operating system a person builds around it.<\/p>\n
Brent usually has access to a powerful model. He then decides which models the other agents should use based on the work and the capability needed. Some jobs require an expensive reasoning model. Others do not. The model does not dictate the workforce. The workforce dictates the model.<\/p>\n
Microsoft\u2019s 2025 Work Trend Index<\/a> drew on responses from 31,000 workers in 31 countries, alongside Microsoft 365 and LinkedIn data. It describes three phases<\/a>: individual AI assistants, humans and agents working as a team, and operations directed by humans but carried out by agents. Globally, 82% of leaders expected to use digital labor to expand their workforce capacity within 12 to 18 months.<\/p>\n My team is a small and slightly eccentric example of that wider change.<\/p>\n What Changes For The One Human?<\/p>\n Think about two equally capable people using the same AI model.<\/p>\n The first asks it to carry out research and waits. They read the response, ask for analysis, wait again, and then ask for a draft or a set of notes. From my experience, this is still how a lot of people use AI. It is also how I catch myself using it when I am in a rush.<\/p>\n The second person gives a clear outcome to a coordinating agent. It sends specialists away to research different parts, brings in another agent to challenge the findings, and asks a fact checker to test the claims. Some of that work takes place at the same time.<\/p>\n The second person receives a synthesized result and the decisions that require human judgment. They are not necessarily working harder. They have designed more leverage into the work.<\/p>\n There\u2019s also a third person. Well, it\u2019s the second person after they\u2019ve developed their AI team. This person starts by reviewing a brief from their agent team and then progresses their agency through decision-making. <\/p>\n Anthropic reported that its lead agent and research agents <\/a>outperformed a single agent by 90.2%<\/a> in an internal research evaluation. The advantage appeared when a question could be divided into independent lines of inquiry.<\/p>\n There was a cost. Anthropic also reported that its multiagent system used about 15 times as many tokens as an ordinary chat interaction. In other words, this can produce a large improvement on suitable and valuable work, but it is not free, and it is not sensible for every task.<\/p>\n It\u2019s important to note from experience that more agents do not always mean better work. They can repeat one another, burn through tokens and create confusion. It has taken months of iteration to work out which agents I need, what they should know, when they should challenge one another and, just as importantly, when they should stay out of the way.<\/p>\n The AI Entourage<\/p>\n Even before this latest wave of agent capability, one of the sessions I run with students involves asking them to build an AI entourage.<\/p>\n The analogy I use is Taylor Swift. She has people around her with specialist capabilities, such as marketing, social media, styling, choreography and business management. Those people expand what she can achieve, but she remains the creative director and the final decision maker. The entourage does not own the vision. It helps her realize it.<\/p>\n An AI entourage can work similarly. A researcher finds useful material. An analyst identifies patterns. A critic challenges assumptions. A fact checker tests claims. A builder turns a decision into something that works.<\/p>\n The human still chooses the goal. You apply your taste. You consider the consequences, and you own the outcome. The aim is not to remove you from the loop. It is to move you to the part of the loop where your judgment adds the most value.<\/p>\n This is also why the advantage does not come from paying for an agent platform or creating an impressive organization chart. It comes from learning how to direct a system.<\/p>\n What is the mission? Why does each specialist exist? What information does each one need, and what would only distract them? What can an agent read, create, change, send, or spend? What does good work look like? How will it be checked? When must uncertainty, risk, or consequence come back to the human?<\/p>\n Those are questions of leadership and management.<\/p>\n As I sit here midway through 2026, it seems obvious that a basic AI user starts each piece of work manually, whereas an agent leader builds reusable rules, context, routines, quality standards and feedback loops.<\/p>\n The first person gains a faster way to complete an individual task. The second gradually builds a system that can identify, distribute, complete, and review work. Each successful workflow becomes part of the next one. That advantage, when done right, can compound.<\/p>\n Education Is Still Teaching The Entry Skill<\/p>\n Deloitte surveyed 3,235 senior business and technology leaders across 24 countries<\/a>. Its later guidance reported that 74% of companies planned to deploy agentic AI, while only 21% had a mature governance model<\/a>.<\/p>\n That gap is important. Many people will gain access to agents. Far fewer will know how to make them dependable, focused, and safe.<\/p>\n Most AI education is still focused on writing a better prompt for one chatbot. It is stuck in 2023. Do not get me wrong, prompting remains useful and in many ways essential, but it is becoming the entry skill rather than the destination.<\/p>\n Schools, colleges and universities that want their students to stand out will need to help them break complex goals into parts and decide what can be delegated. Learners will need to curate context rather than dump everything into a model. They will need to set standards, inspect evidence, and verify what comes back. They will need to understand permissions, privacy, risk and accountability.<\/p>\n I would argue that they should also learn how to supervise digital collaborators before they have supervised another human being. Coordinating intelligence is becoming a form of literacy alongside research, collaboration and project management.<\/p>\n The entourage analogy can be powerful because the person at the center is you. It is your vision, your standards, and your ability to make decisions. Without those things, adding more specialists will probably create noise rather than direction.<\/p>\n After seven months of building my specialist AI team, the most important discovery was not a particular app. In fact, my agentic team created its own app. It was not a particular prompt or one specific AI agent role. The discovery was that AI is becoming something people organize, not simply something they ask.<\/p>\n The AI age will not be shaped by the people who can generate the most. It will be shaped by the people who can direct intelligence toward the right goals.<\/p>\n","protected":false},"excerpt":{"rendered":"The new divide between AI users and AI leaders getty Those who learn to run dependable teams of…\n","protected":false},"author":2,"featured_media":150430,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[179,7493,73576,73575,24,53,182,66380],"class_list":["post-150429","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agentic-ai","tag-agentic-ai","tag-agentic-artificial-intelligence","tag-agentic-team","tag-agentic-workforce","tag-ai","tag-anthropic","tag-claude","tag-grok-bot"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/150429","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=150429"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/150429\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/150430"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=150429"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=150429"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=150429"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}