From individual use to shared practice
In one organization, daily AI use rose from 23% to 80% after training. Restructuring how work was organized across teams is what made it stick. Both phases required a different approach.
AI scales through workflows
Across a global business development team, a two-week agency content cycle became a two-day turnaround once the workflow was documented and AI was built into it.
Finance rebuilt its recurring work with AI
An accounting team automated recurring reconciliation and VAT workflows with AI handling the first pass. Routine processing time dropped by 30% and the team absorbed new geographies without adding headcount.
CASE STUDY 01
Building the foundation for AI adoption in 8 weeks
A Global Financial Risk Institute
Fewer than a quarter of GARP's employees were using AI regularly, despite having access to LLM tools. What was missing was a structured path from tool availability to consistent practice.
The work started with baseline interviews across 14 teams to understand where people actually were. That shaped everything that followed: 12 organization-wide workshops, 70 individual enablement sessions, team use case sharing, an AI Coach GPT deployed org-wide, and custom GPTs built into daily accounting workflows.
Two months later, 67% of employees were using AI weekly. 94% had tried it.
The gap that remained was telling: individual capability was real. Shared workflows were not. Only 7% of what people had learned was making its way into repeatable team practice. That finding set the agenda for phase two.
DIAGNOSE & ENABLE
WHAT CHANGED
-
A legacy policy created uncertainty around whether AI use was allowed
-
Interviews showed this was a structural and cultural barrier, not a skills issue
-
Workshops built shared understanding and gave permission to experiment
-
Individual sessions helped teams apply AI to real work
-
The sentiment shifted from “should I use this?” to “how do I use it better?”
RESULT
80+%
daily AI use
up from 23%
30%
time savings
on routine tasks
25%
confidence increase
in lagging teams
AI adoption does not scale through exposure or training alone.
The real work is identifying what is actually blocking people, and building the conditions for consistent practice.
67%
daily AI use
up from 23% before intervention
30%
time savings
on routine tasks in pilot departments
8 wks
start to finish
reduced from 30 mins with AI workflows
INSIGHT
AI adoption does not scale through exposure or training alone.
It requires a shared understanding of how the system behaves, so people can use it consistently and with confidence.
I never realized how much of what we do individually
is actually the same work.
VP, LEGAL
FOUR CASE STUDIES
From individual use to shared capability
AI enablement to level-set and raise to floor on AI capability baseline
1A. CASE STUDY
MENTAL MODEL
AI adoption starts with a shared baseline of how AI works
AI enablement at at Global Financial Risk Institute
Most employees did not have a stable mental model of AI. It was used like search, or expected to return correct answers by default. When it did not, usage stopped.
At the same time, a small group of users had already built effective workflows, but entirely in isolation. Capability existed, but it did not transfer.
The work focused on establishing a shared baseline first. Not fluency, but alignment: how AI behaves, where it breaks, and what responsible use looks like in practice.
WHAT CHANGED
Structured interviews to map starting points and usage patternsTeam workshops to establish a common model of AI behavior and limitsOne-on-one sessions applying AI to real workflowsIntroduction of a shared coaching layer and initial governance
RESULT
Adoption increased across the organization, but more importantly, usage became more stable and intentional.
INSIGHT
AI adoption does not scale through exposure or training alone.
It requires a shared understanding of how the system behaves, so people can use it consistently and with confidence.
INSIGHTS
READINESS
Open mindset is the strongest predictor of AI adoption
The employees who adopt fastest are already comfortable navigating uncertainty and unfamiliar systems. The same mindset that drive innovation.
SYSTEMS
The individual-to-organization shift is where transformation happens
Individual AI use is relatively easy to achieve. Systematic capability, workflows that hold when people leave, that transfer across teams, requires something different. That gap is where the real work is.
BEHAVIOR
Clear expectations accelerate adoption. Ambiguity slows it
Where people understand what is allowed and what good looks like, AI use becomes consistent and self-reinforcing. The organizations that moved fastest were not the most permissive. They were the most explicit.
THE HUMAN DYNAMICS THAT SHAPE ADOPTION
Adoption succeeds when people, workflows, and teams align
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 or a training problem.
Success comes from understanding how people learn, where AI fits workflows, and how teams support each other.
People understand the system
01
Literacy that goes beyond feature training to build genuine confidence and accurate mental models.
Workflows support AI use
02
AI embedded in how work actually gets done, not bolted on after the fact.
Teams reinforce each other
03
Champions networks and peer learning that sustain momentum long after the formal engagement ends.