Professional Experiences

From enterprise execution to enterprise AI.

I spent four years helping complex organizations turn priorities into measurable results. Now I’m applying that systems perspective to build AI that strengthens decisions and execution at scale.

Approach

How I work

  1. 01

    Diagnose the system

    Use organizational data, stakeholder input, network analysis, and statistical methods to identify where alignment, information flow, or execution is breaking down.

  2. 02

    Design and implement change

    Translate the diagnosis into clearer priorities, operating rhythms, decision forums, accountability, and measurable action.

Liz with colleagues at McChrystal Group
McChrystal Group · Strategy & implementation

Selected impact

Enterprise outcomes

01

Fortune 500 Financial Services

Aligned senior leaders around enterprise growth priorities and mobilized targeted workstreams addressing an $8B performance gap.

30% increase in organizational prioritization clarity

02

Top 3 U.S. Hospital System

Designed and implemented a system-wide quarterly operating process connecting strategic priorities, cross-functional teams, and critical information.

Faster decisions within a 48-hour window · 50% reduction in operational backlogs

03

Multistate Public Health Network

Built and facilitated a cross-jurisdiction collaboration network during the COVID-19 response.

400+ public-health leaders across 45 states

Enterprise AI

What I bring to enterprise AI

Design around decisions and measurable outcomes

Start with the business decision, workflow, and result the AI system needs to improve.

Build for enterprise execution

Design ownership, information flow, feedback, and adoption alongside the model.

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