A triaxial organization is Raúl García Vega’s proposed way to describe organizations coordinated through three complementary axes: formal hierarchy, human networks, and an AI-enabled cognitive layer. The idea is to treat AI not merely as a tool assigned to existing roles, but as a possible layer of organizational work that can coordinate processes and support or perform decisions. García Vega presents this as a conceptual argument, not a model proven to outperform other structures.
What a triaxial organization means
García Vega describes organizational change as a shift in where the organization carries its “structural load”: first in formal authority, then increasingly in human coordination, and now also in cognitive operations supported or performed by AI. The three axes are not three competing org charts. They are different ways work, influence, and accountability can be coordinated.
| Axis | What it coordinates | What it contributes |
|---|---|---|
| Hierarchical-functional | Formal authority, roles, resources, and accountability | Stability, institutional control, and identifiable responsibility |
| Human network | Relationships and collaboration across formal boundaries | Exploration, innovation, and adaptation when work is uncertain |
| Cognitive | AI-supported or AI-executed coordination and decision work | Potential process stability and reduced distributed cognitive load |
The balance matters more than the labels. Formal reporting lines may remain essential for accountability while informal human networks help teams adapt and AI handles suitable parts of recurring work. In this framing, organizational design includes who controls decisions, resources, and access to priority-setting forums—not only where boxes appear on a chart.
How to decide where AI belongs
The practical question is not whether to make an entire department “AI-driven.” It is which processes can safely shift some coordination or judgment to AI, and which still require human authority. García Vega’s framework suggests evaluating work process by process rather than assuming a uniform operating model.
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- Stability and standardizability: repeatable, well-defined processes may be candidates for greater AI autonomy.
- Ambiguity and consequences of error: uncertain cases or decisions with significant impact call for more retained human judgment.
- Accountability needs: decide whether one owner should remain responsible end to end or whether specialist roles need to be separated.
- Oversight design: select the specific human and technical controls appropriate to the process rather than relying on a vague instruction to “keep a human in the loop.”
These are application questions derived from the author’s framework, not a validated scoring system. The article supplies no thresholds that determine when a process is safe to automate.
Designing cognitive friction and human review
García Vega uses “cognitive friction” for the human attention, intervention, and validation deliberately retained in an AI workflow. Friction is a design choice: minimize needless review in stable, standardized work, but retain stronger intervention where ambiguity, risk, or impact warrants it. The point is to specify where autonomy is allowed and where accountable human judgment and traceability remain necessary.
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| Form of friction | What it can look like | Design question |
|---|---|---|
| Temporal | A delay or validation window before an action proceeds | Should the system wait for review, or can it act immediately? |
| Scope | Limits on the system’s domain or decision thresholds | Which cases may the system handle, and which must be escalated? |
| Functional | Separate generation, validation, and authorization responsibilities | Should the same workflow generate an output and approve its use? |
| Technical | Controls for explainability, traceability, or manual intervention | Can people inspect, audit, and interrupt the system when needed? |
The categories are the author’s proposed design vocabulary, not an industry standard. A review mechanism should connect to an accountable person and a defined decision; adding approval steps without clarifying ownership can create delay without meaningful oversight.
How the proposed implementation would unfold
Phase 1: Establish unified AI strategy leadership
The proposed first phase brings together architectural foundations, data and model governance, end-to-end process redesign, and organizational transition under unified AI strategy leadership. The intent is to connect technology choices with changes to how work is performed, rather than treating deployment as a separate technical project.
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Instead of maintaining permanent functional coverage for every AI initiative, García Vega proposes temporary, lifecycle-oriented squads that form to transform processes and dissolve once operations stabilize:
- Analysis-and-design squads identify process needs and shape the target workflow.
- Deployment-and-transition squads put the redesigned process into operation and support the changeover.
- Domain-specialized build squads develop capabilities for particular areas of work.
Phase 2: Redesign affected operating areas
As AI capabilities enter operations, affected areas can move toward outcome-oriented work and cognitive control while retaining necessary functional accountabilities. García Vega expects hybrid operating modes to be common, with the mix depending on the process, its degree of standardization, and its risk. This is a proposed direction, not a prescribed reorganization for every department.
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Assign process-level human responsibilities
The article names three human roles for AI-enabled operations. They are proposed roles, not a universal staffing standard; one organization may combine them, while another may separate them according to workload and risk.
- Process Owner: accountable for end-to-end outcomes and system performance.
- AI Output Supervisor: validates outputs and monitors quality, bias, compliance, and security.
- AI Operator: orchestrates agents and workflows, handles exceptions, and improves behavior in production.
Defining these responsibilities at the process level helps keep the operating model legible: technical deployment, output review, and outcome accountability are related, but they are not automatically the same job.
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What happens to the CIO, COO, and Chief AI Officer?
García Vega argues that AI-era operations may bring the CIO’s remit for decision capabilities and cognitive platforms closer to the COO’s responsibility for making AI operational. He also suggests a Chief AI Officer can help organize early adoption but risks becoming a silo if treated as a standalone, permanent answer. These are the author’s assessments; the article does not provide independent outcome data showing which executive arrangement works best.
The structural test is whether authority for platforms, process outcomes, and organizational transition is coordinated. A new title alone does not settle who controls resources, who can change a process, or who is answerable when an AI-supported workflow fails.
Five design rules—and one rule to treat cautiously
- Make strategic work structurally important. If an area is strategic, give it decision power, resource control, and access to priority-setting forums.
- Choose deliberately between continuity and specialization. Unify work when continuity is needed; separate it when specialist focus is needed, and make the trade-off explicit.
- Give each responsibility one identifiable owner. Shared contribution can be useful, but accountability should not disappear into a group.
- Read persistent coordination as a signal. Constant coordination may indicate decisions or responsibilities are poorly located in the structure.
- Do not treat seven direct reports as a proven threshold. García Vega recommends that no executive have more than seven direct reports, but the article supplies no cited study, original data, publisher, or year for that number. It should be understood as his recommendation, not an established scientific limit.
What the proposal does—and does not—establish
García Vega’s CIO opinion article, published March 26, 2026, refers to work associated with Alfred D. Chandler, Henry Mintzberg, James D. Thompson, George A. Miller, and John Kotter. It does not provide full bibliographic references for all of the claims or demonstrate that these scholars validated the triaxial framework. The article is conceptual analysis, not an empirical comparison of organizational structures.
For leaders, the framework is most useful as a way to ask sharper design questions: where formal authority belongs, where human networks need room to adapt, which cognitive work AI can take on, and what human oversight must remain. Whether a triaxial arrangement produces better outcomes than another design is not established by the article.
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