Who This Is For
This work is for leaders experiencing growing friction around AI.
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AI efforts are spreading, but no one has a clear view of what's working
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Multiple pilots exist, but ownership and direction are unclear
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Leadership is being asked for answers on cost, results, and next steps
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Technical ambition is outpacing what the organization can operationalize
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Initiatives are scaling faster than structure, governance, or coordination can support
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Confidence is declining as visibility and control erode
What I Do
I work with leadership teams when AI initiatives begin to lose coherence - too many efforts, unclear ownership, and rising pressure to show results.
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I evaluate what is actually happening across the organization, where structure is breaking down, and where risk is being introduced.
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Then I define what must change before further investment or scale continues:
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which initiatives should continue, stop, or be restructured
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where ownership and accountability must be clarified
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what governance is required to sustain scale
This work is not about driving more activity.
It is about ensuring what exists is sound before more is added.
What This Looks Like in Practice
A large enterprise example (anonymized)
Situation
A large enterprise had established an AI policy and made tools available, but initiatives were not progressing. Access to tools, use case submission, and pilot activity were inconsistent across teams.
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What was breaking
Ownership was unclear. There were no defined criteria for what constituted viable use cases, how they would be evaluated, or who was accountable for decisions. Pilots were initiated but did not progress, and leadership lacked meaningful metrics to assess impact.
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What I identified
The issue was not access to tools or lack of interest. It was the absence of a governing structure.
There was no clear model for:
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how use cases were defined and approved
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how risk was evaluated
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how decisions were made and tracked across the organization
What changed
A governance framework was introduced to define ownership, evaluation criteria, and decision flow across the organization.
This included:
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clear pathways for submitting and approving use cases
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guidance on acceptable applications and risk levels
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structured support for teams identifying and developing use cases
While elements were adopted and resources were built, effectiveness continued to depend on consistent leadership ownership.
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This pattern is common when AI activity begins before structure, ownership, and governance are defined.
Where I Engage
Approach
I work upstream of execution, at the point where AI initiatives are in motion but not yet under control.
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I do not implement tools or run programs. I identify where direction is unclear, ownership is fragmented, and governance cannot support what's being built.
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In practice, AI exposes deeper issues across product, economics, and leadership. I work with organizations already in motion to address those gaps before they scale.​
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Clarify where AI creates advantage and where it does not
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Identify ownership, governance, and risk gaps
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Align on priorities, sequencing, and accountability
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Determine what to scale, change, or stop
The outcome is not more activity.
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It is direction leadership can operate against, and decisions they can stand behind under pressure.
“ While others are playing checkers, Wendy plays multi-dimensional chess, thinking through how the smallest detail can drive everything from strategy to operations. ”
Zachary S. Brooks, PhD, EMBA
Founder & CEO, UGenome Biotech