CyberArk's CIO On Designing A Cohesive Enterprise OS That Orchestrates Humans And AI

Legacy enterprise architecture was never built for artificial intelligence. Enterprise AI is forcing a rewiring of the organization, with outcomes at the center and governed systems linking humans, models, and agents into one cohesive operating system. Rigid chains of command are giving way to orchestration graphs: dynamic networks where intelligent components constantly adapt and move in sync.

We spoke with Omer Grossman, Global CIO at CyberArk. With years of experience leading large-scale military technology and cyber defense units, Grossman brings an expert perspective on system design and governance, shaped by a career of building resilient structures under constant pressure. That background shapes how he views the rewiring now underway inside global enterprises.

"In the past I would imagine myself as the ship captain setting the direction. Today, leadership looks more like the architect drawing the blueprints. Once the ship is in the water it runs almost autonomously. You can stand on the bridge, but there is no one left to command," said Grossman.

• The supervision constraint: As execution becomes automated, the bottleneck on organizational velocity moves up the chain of command. The shift is creating what Microsoft has termed the "Frontier Firm", where traditional management hierarchies are replaced by more dynamic structures. "Execution will be automated," Grossman explained. "The different layers of management will still need to supervise, and that will become the constraint. The bottleneck is no longer the execution."

Grossman offered a pragmatic four-point plan for leaders, rooted in the idea that an enterprise must be orchestrated like a system rather than managed as a hierarchy. Each principle is about designing the conditions—clear pilots, structural redesign, built-in governance, and outcome-driven metrics—that allow humans, models, and agents to operate in sync.

• Start small, design for scale: His first principle is to begin with a targeted pilot use case. "Don’t take a bigger bite than you can chew," he advised. "Start with a single use case. Pick a topic, draw the blueprint, map the workflow, put the right roles in place, and build from there with system enablement in mind." The aim is to establish a clear, repeatable framework that can confidently scale across the enterprise.

• An organizational redesign: Grossman’s second point reframed the entire endeavor in fundamental terms. "Leaders should treat this as an organizational design, not an IT deployment," he stated. "This is not an IT deployment task. This is an organizational design; treat it as such." Success hinges on reshaping the enterprise itself, not just the technology that supports it.

• Bake in governance: Drawing on his cybersecurity background, he stressed that trust cannot be an afterthought. "You can't just bolt it on," he warned. "You need to bake in trust, auditability, and security from day one." For Grossman, governance is not a safeguard to add later but the bedrock that determines whether enterprise AI will scale securely or collapse under its own weight.

• Measure outcomes, not activity: Finally, he urged leaders to fundamentally rethink their metrics. Instead of measuring productivity inputs, focus on business outcomes like customer satisfaction. "Calibrate measurement toward outcomes. If you're measuring small details in specific activities, throw it out the window. Think about the outcome and redesign the process to meet the outcome with an intelligent system enablement mindset."

This model cannot function without a governance approach that balances autonomy with control. Grossman stressed that as enterprises orchestrate humans and agents into a single system, the rules of engagement must be designed as carefully as the workflows themselves. Governance is what determines whether autonomy accelerates the organization or exposes it to unacceptable risk. He broke it down into two complementary components.

• Guardrails and guiding principles: The first approach is to set high-level policies. "This can be as broad as ethical constraints or as specific as designating a single document as a north star," he said. "For example, you can instruct an AI agent, 'Use this document, which contains our company policy and values, as the basis for every decision you make.' That becomes the guiding principle, and the system operates within those guardrails."

• Intervention choke points: The second approach is to establish tactical checkpoints for high-stakes actions that require human intervention. Grossman pointed to CRUD operations as an example. "Reading data is generally safe, and creating new records may be acceptable if aligned with policy, but updating or deleting existing records should trigger human-in-the-loop approval as a governance control."

Grossman predicted this shift will redefine the economic and structural foundations of companies, creating two very different futures for AI-native startups and traditional enterprises. "I believe there's a new AI-native startup born in the last year that will reach a $1 billion valuation with a single-digit number of people," he said. "You can achieve a huge outcome with four people. That was simply not possible in the past." For established enterprises, the change will be more gradual, marked by flatter org charts and the rise of temporary, ad hoc 'tiger teams' that assemble around goals and disband once achieved.

"I don't have a crystal ball. It's hard to predict the pace of change," he admitted. Yet that uncertainty only amplifies the urgency. This is not a change happening in a decade, but in the next one to five years. For leaders, the message is clear, and Grossman left no room for ambiguity. "The one that doesn't adapt probably won't be here for the long run."