Selected enterprise outcomes

AI transformation measured in changed work.

Four anonymized examples from Vic’s enterprise experience—showing the operating problem, the intervention, the controls, and the measurable result.

Evidence standardWork → Product → OperationContext, intervention, control, and outcome—not a highlight reel.

89 workflowsClassified across 20 People functions

50+ productsManaged as a portfolio, not a collection of demos

12+ agentsBuilt into operating workflows

Public technology company · People function

From scattered experiments to an AI product portfolio

The situation

AI activity was growing, but the function needed ownership, a roadmap, measurable value, and an operating model that could move beyond isolated pilots.

The work

Built an eight-person People AI team, classified 89 workflows across 20 functions, established a $2.4 million roadmap, and managed a portfolio of more than 50 products and 12 agents.

$5.7M in tracked value
Adoption increased from 15% to 85%
20,000 service tickets removed annually

Global enterprise · Employee service

An assistant that changed the service model

The situation

Employees depended on repetitive manual support for routine questions, while the team needed clear boundaries for sensitive cases and human escalation.

The work

Designed the assistant as a governed workflow: trusted source material, explicit response boundaries, measurable resolution, and escalation when judgment or protected context was required.

68% of inquiries resolved
Lower avoidable support volume
Human review preserved for sensitive work

Large regulated workforce · Transformation portfolio

Automation tied to workforce outcomes—not activity

The situation

A complex workforce needed faster, more consistent operations without losing the human judgment required for consequential people decisions.

The work

Combined workforce analytics, workflow redesign, automation, and leader adoption with explicit outcome tracking and executive governance.

45,000 hours removed annually
500+ regrettable exits prevented
$15M+ in cost avoided

Enterprise talent and performance · Internal products

AI embedded where the decision happens

The situation

Hiring, performance, and onboarding workflows had visible friction, uneven completion, and long cycle times.

The work

Applied AI inside the workflow rather than beside it—pairing product design with adoption measures, decision points, and accountable ownership.

Time-to-fill reduced 22%
Performance completion increased 67% → 94%
Onboarding reduced 90 → 45 days

Organizations and identifying project details are anonymized to protect confidential context. Figures reflect Vic’s professional experience record; results are context-specific and are not a guarantee of future outcomes.

Bring the same discipline to your highest-value workflow.

Start with the situation, the people affected, and the result the organization actually needs.

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