The traditional organizational chart is dying. In its place, a new structure is emerging, one that’s inspired by an unlikely creature: the octopus.
An octopus’s anatomy is quite unusual. It has nine brains: one in its head and one in each arm. The eight arms use their individual brains to work in coordinated independence, each sensing and responding to the environment without waiting for central commands.
The octopus doesn’t operate according to a rigid organizational structure. It operates according to what needs to be done. It has a distributed intelligence system where work, not hierarchy, determines action.
This is precisely the approach that organizations need to embrace as AI reshapes work. For decades, we’ve relied on hierarchical boxes and lines to define how work flows. But those charts are part of an era when authority mattered more than agility, when knowledge flowed downward from the C-suite to the frontline, and when work could be neatly compartmentalized into static jobs.
AI is obliterating these assumptions. Companies clinging to org-chart thinking are trying to fit artificial intelligence into existing structures rather than reimagining how work actually gets done.
They’re dangerously unprepared for what’s ahead.
What should they do instead?
1. Organize Around Tasks
Recent research from Anthropic, based on over four million AI interactions, reveals a clear pattern: AI adoption is happening task by task, not job by job. Fewer than 4% of occupations are close to full automation, yet 36% of employees use AI for at least a quarter of their tasks.
Organizations that organize around jobs and roles will struggle to capture AI’s potential. But those that organize around discrete tasks and outcomes can deploy AI strategically where it creates the most value.
2. Think in Work Charts
One way to restructure around tasks is through “work charts”: dynamic, visual frameworks that map what needs to happen—tasks, workflows, goals—and the blended human-AI teams that will make it happen. It’s a fundamental reconception of how organizations operate.
Creating a work chart starts with being brutally honest. Start by reviewing each function and systematically specifying what work occurs in priority areas. The map should focus on the work itself, not on the humans who currently perform it.
Ask of each task: Could this be automated? Enhanced by AI assisting humans? What capabilities, both AI and skill-based, would be required to succeed if we were starting from scratch? With this rethinking of the process, who should be doing what?
This granular, task-level view reveals opportunities that organizational charts obscure.
A hospital’s marketing department, for instance, discovered that much of its creative team’s effort was focused on helping stakeholders in different medical specialties figure out what messages they wanted to convey. AI tools could help those stakeholders with that task quickly, providing sample campaign outputs to inform their decisions. Simultaneously, this freed the creative staff to focus on tasks that called for their full talent and capabilities.
This is a frequent theme: AI liberates people from distractions so they can focus on what they’re best at doing.
3. Define ‘Jobs to Be Done’
In AI-enabled organizations, the Jobs to Be Done Framework (JTBD)— the idea that customers “hire” products to get “jobs” done in their lives—is also essential. After asking, “What do people in this department do?” and “What tasks are vital?” organizations should ask, “What jobs need to be accomplished to deliver value?” These jobs exist independent of current technology, solutions, or organizational structure.
Take pricing configuration as an example. The job isn’t “a sales operations manager reviews pricing requests.” The job is “configure pricing to fit a customer’s needs while balancing willingness to pay against the cost to serve.”
When you define the work this way, suddenly new possibilities emerge. An AI could assess customer needs and price sensitivity. It could analyze cost structures in real time. Humans could focus on relationship dynamics, strategic exceptions, and final validation, which are all distinctly human elements that create a competitive advantage.
The Practical Path Forward
Several organizations are already demonstrating what all of this looks like in practice. The insurance giant Travelers, for example, is empowering frontline staff with AI-driven knowledge that allows them to own more day-to-day decision-making. By training large language models on specialized domains, staff can synthesize complex information and make faster decisions without waiting for approval from high up in the hierarchy.
The key is to think beyond using AI tools to redesigning how work flows. Travelers is redefining the concept of a manager from a gatekeeper to a contributor-coach—an expert who vets AI outputs and unblocks progress rather than controls every decision.
The Octopus Organization Emerges
Organizations that master this transition begin to resemble the remarkable octopus. The “arms” figure out what needs to get done, and they coordinate a way of executing it without the need for rigid orchestration.
Octopus organizations empower teams with information, tools, and authority to make decisions close to customers. They use AI not to control but to enable, facilitating the flow of information that allows distributed intelligence to flourish.
The companies moving fastest on this transformation recognize that the dawn of the AI Age is a catalyst for reimagining how work happens.
Stop drawing boxes around people. Start mapping the work that creates value.
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