Case studies
A selection of AI transformation work spanning readiness, capability building, workflow design, and measurable adoption.
FEATURED CASE STUDY
From scattered experimentation to systematic AI adoption across 100 employees
Global Financial Risk (Confidential)
Case Study | 2025
A Global Financial Risk Institute needed a clear view of its AI readiness and a strategy that would work across Legal, Finance, Certification, Marketing, and Member Services. Curiosity was high, but usage was uneven. Trust varied widely. Workflows did not yet support consistent adoption.
The Institute engaged me to assess organizational AI readiness, design a comprehensive adoption strategy, and guide implementation from initial training through sustained practice across all departments.
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Over 16 weeks, I led a full-cycle transformation combining behavioral research, targeted capability building, and workflow integration to move 100 employees from scattered experimentation to systematic AI use in daily work, delivering measurable impact:
CLIENT
Financial Risk (Confidential)
67%
daily AI use
up from 23% before intervention
30%
time savings
on routine tasks in pilot departments
5 min
contract research
reduced from 30 mins with AI workflows
We observed 3 critical barriers to AI adoption
Uneven
literacy
and readiness
No shared
best practices
and frameworks
Trust and verification
concerns
My approach to AI transformation
I start with discovery, before tools. I use behavioral research and workflow analysis to understand how people actually think about AI and work with it.
I combine top-down strategy with bottom-up learning, build psychological safety for experimentation, and design the structures that sustain adoption.
The transformation at a glance
A staggered approach
Weeks 1–2: Diagnosed readiness and identified four personas across departments
Weeks 3–6: Built foundational literacy through tiered training for 100 employees
Weeks 7–10: Conducted 85 ethnographic interviews to understand real post-training behavior
Weeks 11–14: Designed workflow playbooks and lightweight GPT agents aligned to tasks
Weeks 15–16: Launched cross-functional pilots delivering measurable efficiency gains
Key insight: Mental models predict sustained adoption more than training alone. Employees viewing AI as collaborator sustained 84% usage vs. 12% for those viewing it as search engine.
"I was resistant in the past, but once I saw how AI could help me in my job, I went all in."
– VP, Legal & Contracts, Global Financial Risk Institute
ADDITIONAL CASE STUDIES
citizenM Hotels | 2023-2024 | AI/ML Implementation
Led cross-functional initiative applying NLP and behavioral clustering to connect guest sentiment themes with actual booking patterns.
Consolidated 12 months of feedback data, developed journey-based taxonomy, built executive dashboard linking qualitative feedback to business metrics.
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Analysis uncovered specific correlations: sleep quality emerged as top driver of repeat bookings, while lower sentiment around room experience deterred conversion.
Surprisingly, room controls (despite significant investment) showed little influence on behavior, enabling strategic resource reallocation.
Impact
10% NPS growth · 20% increase in actionable insights · Strategic resource reallocation based on data
McKinsey & Company | 2017-2023 | Enterprise Transformation
Led year-long organizational transformation consolidating five fragmented analytics teams into unified function.
Mapped consultant workflows across data ingestion, transformation, modeling, and insight delivery.
Identified inefficiencies, facilitated leadership workshops, co-designed new operating model.
Unified teams with clear roles and governance, streamlined tooling and workflows, improved discoverability of resources and support.
Leveraged research as driver of organizational strategy.
Impact
$50M+ annual savings · 20% FTE reduction · Improved end-to-end data workflows · Enhanced tool adoption
Mid-size Infra company | 2025 | Adoption Readiness
Diagnosing why AI stalls in a mid-sized company
Mapped data, workflow, and behavioral gaps preventing early AI investments from delivering value.
Identified readiness bottlenecks, redesigned adoption pathways, and established measurable success criteria.
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Restored 85 percent team engagement within weeks by aligning tooling, workflows, and decision pathways to actual user needs.
Impact
85% team engagement restored · Clear adoption pathways · Sustainable momentum
Strategic Marketing Communications | 2025 | AI Integration
Designed an AI-assisted workflow to summarize and analyze 10–25 political newsletters daily, reducing editorial effort by 80 percent.
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Built structured prompts, tagging logic, and conflict-detection workflows, integrating LLM outputs into Slack and email.
Established a human-in-the-loop review process and feedback loops, enabling trust and reuse across teams.
Impact
80% reduction in editorial effort · Repeatable prompting framework · Faster review cycles
I knew AI could help us, but now we have a plan the whole team can act on, far faster than I expected.
– CFO, Mid-size U.S. services company
What we’ve learned across organizations
What works
AI adoption succeeds when we start with workflow analysis, define success metrics, and design human–AI collaboration instead of replacement.
What doesn't
Technology-first approaches consistently stall. Over-promising AI capabilities creates resistance. Skipping change management prevents pilots from scaling.
Pattern recognition
Organizations that scale AI have three things in place: clear use cases, data infrastructure readiness, and a culture that favors learning over perfect solutions.