Executives gathered at the New York Stock Exchange on October 1 for Fortune’s first AIQ Summit. They didn’t talk about flashy pilots or theoretical productivity gains. They described something more fundamental. Rigid reporting lines are fading. Static job descriptions no longer fit. And the workers who succeed will first have to forget much of what they once knew.

Stacy Dillow, executive vice president and chief people officer at HPE, put the challenge plainly. The hardest part isn’t experimenting with new technology. It’s rethinking how work gets done and dismantling the structures built around old ways of operating. Her comments, captured in Fortune, echoed across the room.

Danielle Gonzalez, chief people officer at Palo Alto Networks, has watched the shift play out in real time. The cybersecurity firm, ranked No. 49 on the 2026 Fortune AIQ 75 list, rolled out tools such as NotebookLM, Claude and Gemini for broad experimentation. A custom AI agent now handles workflows across HR, finance and legal. It cut IT tickets by 83 percent. That success forced immediate questions about who owns which decisions when software performs tasks once reserved for humans.

Traditional org charts assumed fixed roles and clear handoffs. AI collapses those boundaries. One agent can span departments. Knowledge that once sat with specialists now flows through models. The result looks less like a pyramid and more like a network that reconfigures around problems. But few companies have redrawn their boxes and lines to match.

Only 9 percent of organizations have made meaningful progress building complex autonomous workflows, according to ServiceNow data cited at the summit. Most still treat AI as a point solution rather than a force that rewires coordination itself. The gap explains why so many deployments stall after promising early tests.

Drew Holler, chief human resources officer at homebuilder Lennar, offered a direct message to employees. “We’re going to give you all the tools, but it’s your responsibility to upskill yourself as well. We’re going to give you trainings, but you, as an individual, have to upskill.” His words, reported by Fortune, underscore a cultural pivot. Companies will provide access. Workers must adapt or risk obsolescence.

That adaptation often starts with unlearning. Old habits around sequential approvals, siloed expertise and linear career paths get in the way. Employees who thrive treat AI as a collaborator that amplifies judgment rather than a replacement for effort. They learn to prompt effectively, interpret outputs critically and iterate quickly. These behaviors don’t appear on most job descriptions yet.

Palo Alto Networks adjusted its hiring to test exactly those behaviors. Instead of asking candidates to list past accomplishments, recruiters run observable interviews and hackathons. They watch how applicants approach problems with and without AI assistance. The company wants evidence of adaptability over polished resumes. It signals a deeper change in what talent looks like when intelligence is distributed between people and machines.

Broader data reinforces the trend. An IBM study released this week found that 76 percent of organizations now have a chief AI officer, up from 26 percent a year earlier. The surge, detailed in BW People, shows AI moving from experimental side project to core business driver. Companies with dedicated AI leadership scale more initiatives and achieve better alignment across functions.

Yet titles alone don’t solve the structural problem. HFS Research noted that leaders expect traditional hierarchy to drop from 47 percent to 13 percent of operating models within three years. Dynamic teams that form around tasks and dissolve afterward will take their place, alongside AI-coordinated workflows. The research, published just weeks before the summit, highlights how few firms have defined the human-AI operating model they claim to pursue.

ServiceNow, co-host of the AIQ Summit, positioned itself as the control tower for exactly this transition. Its Enterprise AI Maturity Index informed the Fortune AIQ 75 ranking, which expanded to 75 companies this year with 36 newcomers. JPMorgan Chase topped the list. The methodology rewards measurable impact over activity. That focus explains why conversations at the event stayed grounded in workflow redesign rather than model size or compute spend.

Across industries, the pattern repeats. Middle layers compress as AI handles coordination and routine analysis. Entry-level roles shift from data gathering to oversight and exception handling. Even C-suite responsibilities blur. Chief people officers now work closely with technology leaders on talent strategies that account for agentic systems. The org chart of 2030 may show fewer boxes and far more connections to external models and internal agents.

Lennar’s approach at the $19 billion homebuilder illustrates the personal dimension. Employees receive tools and training. Success still depends on individual initiative to master them. This balance avoids both over-reliance on corporate programs and complete abandonment of workers. It treats AI fluency as a core competency rather than a nice-to-have skill.

The summit also surfaced caution. Rapid deployment without governance creates risk. Palo Alto Networks’ experience with its custom agent shows the value of tight integration with existing processes. Reducing tickets by 83 percent matters only if the remaining human work scales safely and compliantly. Cybersecurity itself becomes more complex when AI systems gain autonomy.

Recent reports add context. Deloitte’s 2026 State of AI in the Enterprise survey, released October 1, emphasizes that leaders now fixate on ROI, safe practices and workforce readiness after years of experimentation. The findings align with summit discussions that successful companies move from ambition to activation by redesigning processes first.

IBM’s data on chief AI officers suggests governance is catching up. Organizations that created these roles report higher success rates in scaling initiatives. The jump from 26 percent to 76 percent in one year marks one of the fastest executive-level shifts in recent memory. It reflects board pressure for accountability as AI budgets grow.

Still, structure matters more than titles. Riviera Partners research from September found that unified technology organizations — where product, data, engineering, security and governance sit under coordinated leadership — achieve far higher AI execution maturity than siloed peers. Executive titles for AI varied widely. Organizational design predicted outcomes more reliably.

So what does the post-org-chart company look like? Fluid teams. Clear charters for AI agents. Managers who orchestrate humans and machines. Hiring processes that test collaboration with AI. And a workforce trained to unlearn outdated assumptions about fixed roles and linear advancement.

HPE, Palo Alto Networks and Lennar represent different sectors yet reached similar conclusions. The technology exists. The harder task is changing minds, habits and structures built for another era. Those who treat AI as a prompt for operational reinvention pull ahead. Those who bolt it onto existing pyramids watch their advantage erode.

The Fortune AIQ 75 list and the summit conversations around it make one fact unavoidable. Companies generating real value from AI have stopped debating whether the technology will change work. They are figuring out how fast and in what form that change must occur to stay competitive. The org chart, long a symbol of stability, has become the first casualty.