For more than a century, organizations have been designed around functions. Marketing owns growth, finance manages money, legal ensures compliance, and operations keep the machine running. This model worked well in a world defined by efficiency, repeatability, and scale. But AI changes the equation. The shift now underway is not just technological—it’s organizational.
Enterprises are moving from functions to archetypes. Instead of asking “what department does this person belong to?” the real question becomes “what behavioral role do they play in AI transformation?”
This is more than semantics. It is a fundamental redesign of how work, accountability, and innovation get distributed across the enterprise.
The traditional functional model suffers from three structural weaknesses when applied to AI adoption:
Silos constrain discovery. Marketing may experiment, but those insights often stay within marketing. Finance rarely learns from marketing’s failures, and legal is disconnected from the patterns emerging in operations.
Functions bias priorities. Finance emphasizes cost efficiency, marketing emphasizes growth, and legal emphasizes risk avoidance. While valid in isolation, these competing mandates often stall cross-functional AI initiatives.
Innovation struggles to scale. A brilliant experiment in one silo rarely makes it to enterprisescale, because the function that owns it lacks the mandate—or incentive—to industrialize it.
In short: AI demands horizontal integration, but traditional functions are vertical silos.
The New Way: Archetypes Over Functions
The alternative is to organize around behavioral archetypes—roles that cut across functions and directly map to the realities of AI transformation.
Explorers: The innovation engine. They experiment, test, and discover patterns others cannot see. Explorers thrive in ambiguity and push the boundaries of what’s possible.
Automators: The scale engine. They turn promising discoveries into repeatable, enterprise-grade systems. Automators thrive on structure, reliability, and throughput.
Validators: The quality engine. They ensure standards, compliance, and trust. Validators thrive on rigor, consistency, and accountability.
Every enterpriseneeds all three. But the balance—and placement—of these archetypes determines whether AI transformation stalls, succeeds, or compounds into long-term advantage.
Explorer Distribution: Discovery Everywhere
Explorers are not confined to marketing. Their role is to bring an innovation engine into every function:
Marketing runs controlled experiments to test new campaigns, content formats, or customer engagement strategies.
Finance uses AI to discover patterns in cash flow, risk modeling, or fraud detection.
Legal explores compliance boundaries, using AI to flag emerging risks before they escalate.
Success requires not only protecting exploration time (20–30% of capacity) but also ensuring failed experiments don’t get punished. Explorers surface possibilities for others—their value lies in creating raw material for Automators and Validators to build on.
Automator Scaling: From Experiment to Enterprise
Once an idea proves promising, Automators take over. Their role is to industrialize discovery:
DevOps scales deployment of AI models across production systems.
Finance automates recurring processes like reporting, forecasting, or reconciliations.
Sales deploys intelligence systems that scale across hundreds of reps.
Automators succeed when they design for adaptability, not rigidity. If Explorers generate novelty but Automators create brittle systems, the enterprise locks itself into short-lived advantages. Scalable transformation requires forward-looking automation that evolves with the business.
Validator Quality: Trust as a Competitive Edge
No AI transformation survives without Validators. They are the guardians of standards and compliance, embedding trust into every process:
Legal ensures alignment with regulatory requirements and ethical frameworks.
Finance validates models to ensure accuracy in reporting and auditing.
Validators don’t exist to slow things down—they exist to prevent small cracks from becoming systemic failures. Their proactive engagement accelerates adoption by reducing organizational fear. When leaders trust Validators, they are more willing to scale AI initiatives.
Explorer Distribution is universal. Innovation engines must operate in every function, not just marketing or R&D.
Automator Scaling drives systematic transformation. Breakthroughs mean little if they cannot be industrialized across operations.
Validator Quality sustains trust. Standards and compliance are not bottlenecks; they are enablers of sustainable adoption.
In practice, success comes when natural inclinations (some departments skew toward certain archetypes) are combined with strategic placement (ensuring balance across the enterprise) and organizational support (protecting each archetype’s role).
From Hierarchy to Balance
This shift doesn’t mean dismantling functions entirely. Finance will still manage money, and legal will still handle compliance. But instead of rigid silos, functions become contexts in which archetypes operate.
For example:
A finance team might be 70% Automator, 20% Validator, and 10% Explorer.
A marketing team might invert that balance: 50% Explorer, 30% Automator, 20% Validator.
IT might distribute evenly across all three.
This archetype balance matters more than headcount. Too many Validators in marketing stifle innovation. Too many Explorers in legal create risk. Competitive advantage comes from getting the balance right.
The Path Forward
To lead in AI transformation, enterprises should follow a three-step progression:
Assess current archetype alignment. Where are Explorers, Automators, and Validators under- or overrepresented?
Structure roles, metrics, and incentives around archetype-specific contributions, not just functional outputs.
Cultivate a culture where managers understand and leverage archetype differences instead of forcing conformity.
This roadmap transforms AI adoption from fragmented initiatives into a coherent organizational shift.
The Real Competitive Advantage
At its core, this shift creates compounding capability.
Explorers push boundaries. Automators scale what works. Validators ensure it endures. Together, they create a system that learns, adapts, and compounds value with every cycle of discovery, implementation, and refinement.
The question is not whether enterprises will move from functions to archetypes. AI will make this transition inevitable. The only question is: will your organization lead, or follow?
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
