Leadership Operating System
Leadership Operating System
My leadership approach is shaped by years of guiding teams through large scale, technically complex, and AI enabled product ecosystems. I focus on building people, strengthening mechanisms, and elevating customer experience across portfolios. This is the operating system I use to help teams thrive in high ambiguity, high accountability environments.
Empathy anchors good leadership. I build trust by understanding the pressures, constraints, and motivations of designers, engineers, and product managers. When people feel understood, they collaborate more openly and take creative risks.
Example:
In high ambiguity AI modernization programs, I provided structure and clarity while giving designers room to explore. This helped them contribute confidently even when goals, requirements, or technology direction evolved weekly.
I encourage designers to look beyond the boundaries of a single team or service and understand how customer problems span multiple AWS offerings. This shifts designers from feature support to portfolio thinking.
Example:
Designers who once focused solely on EC2, Lightsail, SQS, or MQ began mapping modernization journeys that connected EC2, Migration Hub, .NET tooling, Messaging, and Backup. This broadened their strategic influence across organizations.
Influence at AWS depends on clear writing, structured thinking, and the ability to articulate customer value. I help designers grow their narrative leadership so they can guide product and engineering toward better decisions.
Mechanisms include:
Practicing narrative writing and problem framing.
Using CX risk documents to highlight customer impact.
Coaching designers to lead discussions, not wait for direction.
Example:
A Lightsail designer organized a session with service leaders to address Cloudscape adoption concerns. Their preparation and narrative clarity shifted the conversation, strengthened stakeholder trust, and preserved the integrity of the experience.
Fairness creates long term loyalty and high performance. I prioritize transparent decisions and support people even when it requires additional effort.
Example:
A designer preparing for promotion planned to transfer to another AWS org abroad. Moving early would have delayed promotion for a year. I negotiated a delayed start, completed the promotion process, and ensured recognition was not lost during the transition.
Not all workflows, especially AI assisted or modernization workflows can be validated through static prototypes. I help teams balance rigor with bias for action.
Example:
In Microservice Extractor, grouping accuracy and usability could not be judged in prototype form. We released a strong initial version in preview, studied customer behavior on real monoliths, and refined grouping logic and transparency models. This led to a significantly more aligned and trustworthy experience.
Large enterprise problems can feel overwhelming. I help teams move forward through small, meaningful steps that reduce cognitive load and keep momentum high.
Example:
During UX debt reduction efforts, the team treated each resolved item as measurable progress. This built morale, made debt more visible, and encouraged sustainable improvement instead of burnout.
I use 360 degree feedback to create tailored development plans. Designers grow fastest when they understand their strengths, blind spots, and opportunities.
Example:
Some designers thrived in ambiguity and strategy, while others were strongest at addressing complex technical debt. By aligning work to strengths, each designer delivered higher impact and felt more engaged.
People do their best work when they feel grounded. I help teams structure workflows that support balance and reduce unnecessary stress.
Example:
A unified list of personal and work tasks helped designers manage competing priorities, especially during peak workloads like re:Invent or large launches.
Effective teams rely on repeatable systems. I invest in mechanisms that bring clarity, consistency, and alignment across multiple service teams.
Examples of mechanisms I built:
Pre check reviews that strengthen judgment before Bar Raising Reviews.
Weekly WIP sessions that create shared ownership and cross learning.
CX office hours that provide UX guidance when teams lack a dedicated designer.
Hackathons that reduce legacy UX debt and spark cross functional collaboration.
Unified design repositories that prevent duplication and improve consistency.
My work leading AWS Transform, Microservice Extractor, and Migration Hub Strategic Recommendations gave me experience designing predictive, adaptive, and agentic workflows. I bring these lessons back to my team.
I help designers develop skills in:
Mapping customer intent and identifying steps that can be automated.
Designing transparency and trust in AI assisted decisions.
Clarifying when humans need oversight versus when automation is safe.
Designing anticipatory experiences that guide customers instead of reacting to them.
Example:
As the team learned from AI powered modernization work, they began applying similar intent driven thinking in other projects. They mapped cross service workflows, identified steps that could be simplified, and anticipated customer decision points. Since many olders services like EC2 are slow to adapt to AI workflows, designers use the learnings from AI projects to champion AI first approach with engineering and product partners.
My goal as a leader is to build teams that feel supported, confident, and empowered to deliver exceptional customer outcomes. By combining empathy, strong mechanisms, narrative leadership, and AI era thinking, I help designers operate effectively in complex, high impact environments. This operating system has shaped teams that are more strategic, resilient, and influential across AWS.