{"id":564,"date":"2026-09-03T13:48:11","date_gmt":"2026-09-03T17:48:11","guid":{"rendered":"https:\/\/solutionsreview.com\/edtech\/?p=564"},"modified":"2026-09-03T13:49:29","modified_gmt":"2026-09-03T17:49:29","slug":"ai-will-change-the-managers-job-before-the-org-chart","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/edtech\/2026\/09\/03\/ai-will-change-the-managers-job-before-the-org-chart\/","title":{"rendered":"AI Will Change the Manager\u2019s Job Before the Org Chart"},"content":{"rendered":"
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Go 1’s Chris Eigeland offers insights on how AI will change the manager’s job before the org chart. <\/strong>This article originally appeared in Insight Jam<\/a><\/em>, an enterprise IT community that enables human conversation on AI.<\/span><\/strong><\/div>\n<\/div>\n<\/div>\n

\"\"<\/a><\/span><\/p>\n

Most companies measure AI adoption by the number of tools they’ve deployed and the hours those tools save. Both figures are important, but they only track part of the equation. They tell you whether individual tasks got faster, and nothing about whether the organization around those tasks changed. I’m an AI optimist, and I think the impact will land on the larger side of the forecasts, which is exactly why the deployment numbers worry me.<\/p>\n

The harder part to quantify is how AI changes team design, decision-making, and the role of the manager, and that’s the part that determines who benefits from the technology. A company can automate hundreds of tasks and still run on an operating model built for a slower era. Lasting adoption means capturing the short-term efficiency gains while redesigning how the business is managed for the long term.<\/p>\n

AI-native companies redesign the work, then pick the tools<\/strong><\/h4>\n

Most organizations add AI to the structure they already have. The org chart stays the same, approvals run through the same chain, planning happens on the same annual cycle, and every role keeps the boundaries it had before the tools arrived. That’s how you end up with Copilot licenses layered over job descriptions written five years ago, and it’s why so many adoption efforts feel incrementally helpful instead of changing how the company operates. The gains stall at the level of the individual task because nothing around the task was allowed to change.<\/p>\n

So what does an AI-native organization actually look like? It works in the opposite order. It asks what the technology changes about the work itself, then redesigns roles, team structures, and decision rights to match. If AI collapses the time a piece of analysis takes from a week to an hour, companies need to take a hard look at their org structure. Does the role that produced it still make sense as designed? The difference shows up as new role definitions, wider management spans, and shorter planning cycles.<\/p>\n

Roles are compressing and management spans are widening<\/strong><\/h3>\n

Some role displacement is coming, and I think that is undeniable, but the bigger shift is role compression. In an uncertain economy, the AI label often covers headcount decisions that were coming anyway. What’s actually happening inside companies is compression.<\/p>\n

Work that once sat across several discrete positions, say a user researcher, a designer and a product manager, is consolidating into fewer, more powerful roles supported by AI tools for research, prototyping and documentation. A single professional who combines design and product management skills can now deliver what the previous two-person team did. Delivery per person goes up, and the roles evolve to match.<\/p>\n

For managers, compression cuts both ways. Flatter organizations may well run with fewer managers overall, but each manager who remains becomes more important, carrying a wider span, more context and more consequential calls. Hiring data backs that up. Research published in the\u00a0Strategic Management Journal<\/u><\/a>, drawing on U.S. job postings from 2010 to 2022, found that firms adopting the first wave of AI changed what they hired managers for, moving away from routine administration like budgeting and planning and toward stakeholder management and creativity. That data predates generative AI, but the pattern has persisted.<\/p>\n

The new management work is validation, coaching, and judgment<\/strong><\/p>\n

A\u00a0Harvard Business Review\u00a0<\/u><\/a>\u00a0article published in June, based on interviews at two major consulting firms, found that AI\u2019s benefits are distributed unevenly across the hierarchy. Senior leaders capture strategic upside and junior employees report major productivity gains, while\u00a0middle managers<\/u><\/a>\u00a0absorb a layer of new responsibilities, validating AI outputs, catching errors, and coaching their teams on AI use, all under unchanged delivery pressure and with little formal support.<\/p>\n

I’ve watched this play out inside my own company. One of our teams circulated an AI-generated summary of an internal dataset that read as polished and complete, and it took someone with deep context to catch that it had flattened the nuances and was incorrect on two small but important points. The more capable the tools get, the more that judgment matters, alongside critical thinking, problem solving and clarity of communication. Managers will supply more of that judgment as AI-generated output multiplies across their teams. That’s the job now.<\/p>\n

Managers are also starting to oversee AI agents. In my company, agents resolve 10 to 20 percent of our Tier-1 support tickets, and our sales development representatives save three to four hours a week on prospect research.<\/p>\n

Coaching and communication matter just as much. With fewer touchpoints and higher output per person, feedback quality and context transfer decide how well a lean team performs, and both come from the manager. That time has to be defended, because hours spent validating AI output come out of the same week, and the apprenticeship that develops the next generation of leaders is usually what gets squeezed first.<\/p>\n

Decision cadence has to accelerate, and decision rights have to move<\/strong><\/h4>\n

The traditional leadership playbook is starting to break down. The three- to five-year strategic plans you table and update annually are becoming increasingly hard to defend. You’re operating on a yearlong horizon and learning as you go, and that requires leaders to be more hands-on, curious and experimental, closer to the details than the comfortable rhythm of annual planning ever allowed.<\/p>\n

Speed itself has become a strategic advantage. When AI compresses the time between question and answer, then routing every call up an approval chain built for a slower era squanders that advantage. Decision rights have to move down to the people closest to the work. Budget for 10 experiments to find one breakthrough, and accept that the other nine going nowhere is part of the process. Organizations that cling to risk aversion will stagnate.<\/p>\n

Human capability decides whether any of it works<\/strong><\/h4>\n

We’re facing the most significant acute need for upskilling and reskilling in a generation, perhaps ever. For me, AI fluency comes down to three things: prompting well, weaving the tools into daily workflows and knowing when to distrust an output. None of those come naturally, even to people who grew up online. Most employees at a technology company will pick them up quickly. At the other 99 percent of businesses, a manufacturer in the Midwest or a regional services firm, these skills have to be deliberately developed through structured training, time to practice and managers who model the behavior. Skip those steps and you get expensive tools and unchanged behavior.<\/p>\n

I’ll also say this: Too many layoffs branded as AI strategy are really cost decisions wearing a technology label. The people whose tasks AI absorbs carry domain knowledge that’s expensive to replace, and reskilling them into adjacent roles usually beats walking them out the door.<\/p>\n

The clearest sign of an AI-native company is a manager\u2019s calendar. Less time goes to approval queues and status reviews, and more goes to validating output, coaching people and making decisions at the speed the technology now allows. That is where AI adoption becomes real. It\u2019s not in the tools a company buys, but in the management system it is willing to rebuild.<\/p>\n","protected":false},"excerpt":{"rendered":"

Go 1’s Chris Eigeland offers insights on how AI will change the manager’s job before the org chart. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI. Most companies measure AI adoption by the number of tools they’ve deployed and the hours those tools save. Both figures […]<\/p>\n","protected":false},"author":1497,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[14],"tags":[88,87],"acf":[],"yoast_head":"\nAI Will Change the Manager\u2019s Job Before the Org Chart<\/title>\n<meta name=\"description\" content=\"Go 1's Chris Eigeland offers insights on how AI will change the manager's job before the org chart. 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