Your AI adoption challenge
isn't technical, it's human
I help you diagnose behavioral barriers and design strategies that move your organization from scattered experimentation to sustained adoption
The human dynamics that shape AI adoption
Across industries, AI adoption follows the same pattern. It succeeds when people understand the system, workflows support it, and teams reinforce each other’s progress. It stalls when organizations treat it as a technical problem.
Success comes from understanding how people learn, where AI fits workflows, and how teams support each other.
A recent transformation: From 23 percent to 67 percent daily AI use
67%
daily AI use
up from 23% before intervention
100 employees at a global financial risk institute moved from early curiosity to sustained use in 16 weeks through maturity assessment, targeted AI literacy programs, workflow integration, custom AI agents, and a strong AI champions network.
What made the difference:
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Baseline assessment
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Tailored approach
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Workflow integration
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Champions network
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Cross-team sharing
Three ways I help organizations adopt AI
Organizational readiness assessment
Training & capability building
Workflow integration
Measuring impact
Four AI mental models
Behavioral readiness assessment
Adoption scaffolding methodology
Peer-driven enablement mode
Why adoption succeeds
What enables systematic adoption
How peer enablement drives change
Where workflow integration matters
Applying these methods across industries
These adoption patterns hold across industries including finance, hospitality, consulting, and communications. AI adoption follows familiar human dynamics: people lead, people follow, and momentum builds when teams see evidence from one another.
Once these foundations are in place, organizations scale AI two to three times faster.
Marianne drove impact and change professionally and creatively beyond expectations.
– Nana Maanu, Novo Holdings, ex-McKinsey
I saw her grow into an all-around transformation and change management expert in only a few months.
– Brian Bussing, Databricks, ex-McKinsey
Get in touch
I bring 14+ years of human systems and organizational transformation experience across global organizations. My work focuses on human-AI collaboration, AI-enabled workflows, and building the conditions that allow organizations to deploy and scale custom AI agents effectively.
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My approach combines assessment and discovery with practical implementation. I diagnose readiness, design interventions for different starting points, and track the behavioral and operational shifts that support long-term adoption and business performance.
AI readiness assessment
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AI opportunity portfolio and prioritization
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Adoption strategy and capability building
Workflow integration and pilot design
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Behavioral tracking and measurement​