Frameworks & benchmarks

Tools for moving AI from pilots to practice.

Change methodologies I apply

AI adoption is a people change. I manage it with proven methods.

Research‑based change management (Prosci) combined with my AI IMPACT framework — moving teams from individual AI use to measurable business value.

Individual change

ADKAR® Model

Awareness → Desire → Knowledge → Ability → Reinforcement. I use it to find exactly where each team is stuck with AI, and fix that gap first.

Process

Prepare · Manage · Sustain

A structured 3‑phase change plan with clear owners, so AI rollouts don't stall after launch.

Diagnose

Change Impact Assessment

Map who is affected, how much, and in which processes, roles and tools — before redesigning work around AI.

Leadership

Sponsor activation (DREAM)

Sponsorship is the #1 success factor. I coach executives to be active, visible and build a coalition.

People

Resistance → opportunity

Anticipate resistance, find root causes and turn concerns into design input for better adoption.

AI adoption

10 workplace conditions for AI

Vision, experimentation, transparency, trust and role‑specific value: the conditions that make people excel with AI.

SkillsWorkforce planningAI fluency

AI Skill Mapping

Know which skills you have, which you need, and how to close the gap — before AI changes the work.

Fluency programs work best when they start from a skills map, not a course catalog.

1. Identify

Start from the business capabilities that make you different, break them into jobs and tasks, and flag which tasks AI will automate, augment or create.

2. Map

Build a simple skills taxonomy (AI fluency, domain, human skills) and score each role: current level vs. target level.

3. Develop

Point learning budgets at the biggest gaps first, using the role-based fluency paths, and track progress against business outcomes.

4. Mobilize

Fill gaps with a clear build / buy / borrow mix: reskill internal talent first, hire for the skills that matter, and use partners for the rest.

Output: a heatmap of skill gaps by team and a 90-day plan to close the critical ones.

AI adoptionOperating modelGovernance

AI IMPACT Framework

Six dimensions, one operating system: from scattered AI pilots to redesigned workflows, governed agents and value finance will sign off on.

Why AI programs stall: pilots that never scale, training that doesn't change how work gets done, governance that's a bottleneck (or missing), and value nobody can defend. The gap isn't the technology. It's the operating system around it.

The six dimensions

I — Intent & ambition: a North Star tied to the P&L, not a tech wish list.
M — Maturity & readiness: a measured baseline of people, leaders, data and controls.
P — Portfolio & prioritization: a scored use-case pipeline with clear fund / stop gates.
A — Adoption & capability: role-based pathways where credentials are earned by shipping.
C — CoE, controls & cost: risk-tiered governance and AI FinOps that enable, not police.
T — Transformation & scale: a venture studio that turns experiments into products.

The journey: Enable → Reframe → Reinvent

Enable (first 90 days): everyone safely productive with approved tools and role-based literacy.
Reframe (months 3–9): priority workflows rebuilt end to end, measured against a baseline.
Reinvent (9+ months): governed agents, AI-enabled offerings, a self-improving operating model.

90-day launch: three flagship workflows, not thirty pilots. Focus is the strategy.

Built and applied in a 4,000+ employee organization. It's the framework I use on myself.

AI literacyAI fluencyLearning

AI Fluency by Role

From AI awareness to AI fluency: four core competencies, three ways of working with AI, and learning paths sized to each role.

Literacy is knowing what AI is. Fluency is working with it — effectively, efficiently, ethically and safely.

The 4D competencies

Delegation — decide whether, when and how to use AI for a task.
Description — explain goals and context clearly so AI produces useful work.
Discernment — judge outputs for accuracy, relevance and bias.
Diligence — own what you do with AI: transparency, privacy, accountability.

Three modes of working with AI

Automation — AI executes a defined task.
Augmentation — AI as a thinking partner.
Agency — people configure AI agents to act on their behalf.

Learning paths by role

Everyone — foundations, safe use, prompting basics (role, context, examples, step-by-step, output format).
Individual contributors — redesign 2–3 daily workflows; build personal prompt libraries.
Managers — coach adoption, reset team goals, spot automation vs. augmentation opportunities.
Executives — set ambition, sponsor flagship use cases, govern risk and value.
Champions — peer coaching, office hours, sharing wins across teams.

Measured by behavior, not attendance: usage depth, workflows redesigned, time and quality gains.