Enterprises Explore A New Orchestration Framework For Success With Agentic AI

Enterprise AI is a watershed moment. On the surface, a frantic race to adopt generative AI is underway. Just beneath the hype, however, operational chaos is brewing. In this "pilot purgatory," most organizations wrestle with security and governance issues, and few AI projects ever reach production. Now, instead of model intelligence, leaders face a new challenge: orchestration.

Some see the current AI boom as entirely new. But Matan-Paul Shetrit, Director of Product Management at Writer, sees familiar patterns. An accomplished leader with over 16 years of experience building platforms at companies including Brex, Flexport, and Square, he views the chaos of AI adoption through an expert lens. In a market with no agreed-upon metrics for success, this deep, enterprise-first DNA is what gives him and his team the "privilege to be very opinionated on what is good," he said.

From Shetrit's position, the primary purpose of AI should be to augment the systems that make a business successful in the first place. By mapping what he called "orchestration and judgment graphs," organizations can create an explicit blueprint of their hidden operating systems. It's these unwritten rules and informal power structures that dictate how work actually gets done, he continued.

• Make good better: But AI isn't a silver bullet, nor is it meant to replace teams, Shetrit clarified. "AI's primary purpose is to make what your enterprise does really well even better by learning from how you do things and how you got to where you are." Still, most teams approach the problem from a technical perspective when the real value lies in a less obvious solution.

• Hidden in plain sight: The orchestration graph already exists, he explained. It's just hidden in plain sight. "If you want AI to be effective, you have to map that hidden organizational knowledge and tribal knowledge, so agents know how to navigate your organization effectively." Instead of hyper-focusing on the org chart itself, his approach is about discovering new forms of responsibility, accountability, risk management, governance, and more.

Success with AI is more about the human element than technical capacity, according to Shetrit. Beyond processes and tools, the real leverage comes from tapping into the invisible information systems already embedded across traditional hierarchies.

• Orchestration ≠ flat org: Enterprise decision-making tends to be more challenging when organizations are "flat," he explained. "Organizations that aren't transparent about the decision-making process create teams and accountability structures that are frustratingly fragile." Decision-making still happens, he pointed out. But without the ability to trace who decided to implement what and why, the process lacks transparency.

• Military leadership: In the military, service members learn regulations through incidents, Shetrit continued. The enterprise certainly isn't a battlefield, but a regimented approach can be practical here as well. "Otherwise, employees and leadership will forget why a regulation or guideline was implemented. They'll assume it's cumbersome and remove it, only for the same negative result to occur again."