Organizations are cognitive systems, not just social ones. They have perceptual fields, memory structures, attention allocation mechanisms, and reasoning processes. Most perform far below their potential because these cognitive functions are designed accidentally, if at all.
In modern organizations, responsibility often fragments across silos: leadership sets direction, a program management office coordinates execution, IT provides tools, HR manages talent. But the architecture that determines what the organization can notice, how quickly it can learn from what it notices, and whether it can coordinate action based on what it learns—that falls through the gap.
My work strengthens this layer.
The work
- Build for observability, not monitoring. In software, there has been a decisive shift away from static monitoring toward observability: ensuring work is traceable as it happens so new questions can be answered later, not just the ones anticipated in advance. That same shift is embedded—often unrecognized—in platforms like Asana and Slack. Data structure and tagging are paramount. When work is observable, executive oversight improves without micromanagement. Redundant status reporting disappears. The work itself becomes the system of record.
- Design organizations that rebalance themselves. Most operational functions—IT, HR, Legal—optimize for reliability and consistency. This works until strategy shifts or markets move. With granular data models and AI-enabled tooling, you can now design organizations that redistribute attention and rebalance priorities in response to changing conditions, navigating the trade-offs between efficiency, resilience, and adaptability dynamically instead of by default or force of will.
- Process over tools. AI can knit together previously isolated data stores, enabling fundamental reconceptualization of how work gets done. I help organizations move from tool-centric workflows to flow-centric ones, where ideas, tasks, and outcomes stay connected end to end. Given how fast AI tooling evolves, the wiser investment is in scalable processes while staying tool-agnostic.
- Make goals load-bearing. Most goal systems fail by degrees because they exist on a different representational plane than the work itself. Goals live in OKR documents; work lives in tickets and tasks. The gap between them is crossed manually and infrequently—if at all. This is a knowledge representation problem. The solution is designing a single data model where goals and tasks are different views of the same underlying structure. When priority shifts can propagate automatically, alignment becomes how we work rather than what we aspire to.
- Asana as cognitive infrastructure. There are many work management tools, but no real replacement for Asana. An Asana rollout is never just an uncontentious tooling project—it makes ownership, dependencies, and avoidance visible with a kind of ruthless clarity that surfaces cultural debt fast. I often serve as an acting Chief Asana Officer: designing and implementing Asana for performance management, revenue functions, and executive oversight, ensuring the system holds, then transferring ownership to internal teams as capability matures.
- Task alignment before team alignment. People coordinate primarily around shared work, not shared values. Yet organizations invest heavily in “teambuilding” and “culture” while leaving actual collaboration workflows undesigned. Cross-functional coordination usually hinges on a handful of specific handoffs: story banking between services and comms, CRM hygiene between marketing and sales, release coordination between product and marketing. Designing clear workflows for these use cases produces more reliable alignment than asking adjacent teams to understand each other better, or meet with each other as much as possible.
- Isolate signal. Every channel—external and internal—now carries more content than anyone can read. I design reporting and sampling systems that let leaders skim intelligently, surface leading indicators, and escalate what actually requires judgment.
- Emotional intelligence as a discipline. Emotions are not obstacles—they’re high-density data streams. My coaching and advisory work helps leaders appreciate finer-grained emotional perception—not to manage feelings instrumentally, but to let them circulate as the actionable intelligence they are.