About
About Dr. Lisa
The 360-degree view
I've been on every side of the AI conversation. Technology executive. Buyer. Seller. Advisor. Researcher. And now author and founder. That view led me to a simple conclusion: Same AI. Different people. Different results.
360°
view of Technology
Executive
Ran the P&L
Buyer
Bought the tech
Seller
Sold the tech
Advisor
Guided CIOs
Researcher
Studied adoption
Founder
Builds AI-native
I've been on every side of the AI conversation. Technology executive. Buyer. Seller. Advisor. Researcher. And now author and founder. That view led me to a simple conclusion: Same AI. Different people. Different results.
Founder and CEO of Neurocollective — building the Six Sigma of AI Adoption.
Basel, Switzerland
Led 36 global bank CIOs through AI planning, ensuring trillions in cross-country money movement. Sponsor: US Federal Reserve CIO
AI Adoption Engineering™
Creator of the discipline: methodology, diagnostics, 4-level certification, and n.judgment (MCP server), available on Microsoft Copilot & Teams, Claude, ChatGPT, and Slack.
Doctorate in Artificial Intelligence (2023)
Practitioner-focused primary research, designed to inform practical action
Original research, not recycled takes
Doctoral research across 8 industries, studied firsthand. 11 expert reviewers validated.
Former Gartner VP
Executive Programs; hundreds of CIOs
Microsoft · Splunk
Former executive leader, sales and field strategy
Booking keynotes, board briefings, and advisory work for 2026-2027. Start a conversation
Why it matters
AI value isn't bought.
It's engineered.
The keynote shows the gap in five minutes. Closing it is the work.
Plenty of pilots. Some even drive business value. The results aren't repeatable.
Not a technology problem. Organizations haven't built the capability to drive adoption themselves.
That's the work. Partner on strategy, certify your talent, and scale AI success on your own.
The thesis
“Same AI. Different people. Different results.”
Proprietary IP
Named frameworks, not slideware
Trademarked, field-tested models built from primary research — not repackaged theory.
Bold AI Leadership Model™
A repeatable three-tier framework — Mindset, Strategy, and Action — for leading in the age of AI.
It equips C-suite leaders and board directors with the four Applied AI Guiding Principles to confidently sponsor, resource, and de-risk high-stakes AI initiatives.
AI Adoption Engineering™ Certification
A certification path that moves practitioners from “pilot purgatory” to operational performance.
This intensive program transforms adoption from an art into a repeatable discipline, teaching candidates to engineer stakeholder buy-in and outcome-driven execution.
5 AI Success Pillars™
The empirical, research-backed foundation behind enterprises that get results from AI.
Born from a qualitative study of real-world enterprise implementations across 8 industries, this framework translates five strategic priorities — Business Value Creation, Customer-Centricity, Collaborative Teams, Cultural Shifts, and Data as Strategic Asset — into a reliable blueprint for business value. Full definitions live at neurocollective.ai/faqs.
AI Performance Flywheel™
A self-reinforcing engine that turns AI potential into sustained business momentum.
Moving through four sequential phases — Foundation Momentum, Execution Momentum, Scale Momentum, and Innovation Momentum — it replaces linear, fragile IT roadmaps with a compounding cycle where innovation naturally feeds the next wave of foundation opportunities.
Visual Dartboarding™
An interactive co-creation method that aligns stakeholders on high-impact AI priorities, fast.
Grounded in the truth that “there is no desire greater than to alter someone else’s draft,” it uses rough, highly editable visual frameworks to bypass corporate hesitation and turn skeptics into co-creators.
AI Pace Style™
A behavioral diagnostic that aligns individual leadership styles with your organization’s position on the AI Performance Flywheel.
By mapping distinct PACE leadership profiles — Pioneer, Assembler, Conductor, and Evangelist — to their corresponding operational phases, it ensures that the right leaders are driving the right change at the right time.
Built on primary research, not opinion
In-depth doctoral primary research on organizations that both succeeded and failed with AI, validated by 11 external technology-practitioner reviewers.
How the study was conducted
- —In-depth explorations of enterprise AI leaders across 8 industries, selected for industry diversity, organization size, and direct involvement in AI decisions — in-depth doctoral primary research on organizations that both succeeded and failed with AI, validated by 11 external technology-practitioner reviewers
- —Full transcription, multiple listening cycles, keyword extraction, thematic coding, and case study synthesis
- —Data saturation reached
- —Validated by 11 external technology-practitioner reviewers
- —IRB-approved and committee-defended
- —Field-tested with enterprise clients