Diversified USAExecutive summary
Diversified had AI tools, but adoption was fragmented and driven by individual experimentation. A pre-launch survey showed only 40% of employees were comfortable using AI, with concerns centered on job impact, skill gaps, and how AI fit their day-to-day work. The response was a workflow-based adoption strategy built on real business use cases, peer learning, leadership visibility, and measurable proof of value. Within four months, frequent AI use rose across every division, comfort climbed roughly 15 points company-wide, and 94 to 100% of respondents reported a positive impact on their work.
The business challenge
AI tools were available, but use was inconsistent and isolated to a few people.
Employees lacked confidence and practical examples of how AI fit their actual work.
Without a deliberate design, the effort risked the common traps: tool-centric training, mandated usage that fades, and activity that never reaches output.
Leadership wanted productivity and efficiency gains without creating fear or disrupting culture.
The goal: give employees modern AI tools and practical skills while building the trust and confidence that make adoption last.
Solution design
Design principles:
Anchor to workflow improvement and output, not AI usage for its own sake.
Build confidence before chasing adoption numbers.
Create visible proof of value.
Keep judgment and accountability with people.
Track behavioral change, not just tool access.
Key components:
Workflow-based learning. Weekly lessons taught Claude's features inside the real workflows people needed them for, sequenced so each built on the last and moved people from simple tasks toward redesigning how the work gets done.
Office hours. Weekly open sessions, around 25 attendees, spanning coordinators through the CEO, which made AI accessible and signaled it was safe to ask questions.
Learning Sprint cohort. A peer-learning group selected from 177 volunteers that created internal champions and spread proof stories horizontally across teams.
Leadership pulse. An ongoing feedback loop that steered content and priorities on real adoption signals rather than a fixed plan.
Governance. A five-level maturity framework for shared language, plus a usage policy and connector governance that kept speed safe and human review in place.
Actions Taken
Defined the adoption strategy and operating model
Recognized that access to AI tools alone would not drive meaningful adoption. Established a workflow-first adoption strategy focused on improving productivity, efficiency, and confidence rather than increasing tool usage. Positioned the initiative as a business transformation effort rather than a technology rollout.
Established the core design principles
Created the principles that guided every aspect of the program:
Focus on workflows, not tools
Measure comfort alongside usage
Prioritize proof over promotion
Keep human judgment at the center of decision-making
Build capability progressively rather than through one-time training
These principles became the foundation for program design, communications, governance, and measurement.
Designed the enablement framework
Developed a structured learning model that introduced AI through real business workflows rather than generic feature demonstrations.
Designed a sequenced curriculum that progressively moved employees from basic AI usage toward workflow redesign and higher-value work.
Created a common capability framework and shared language that could be used across multiple business units.
Built organizational adoption mechanisms
Recognized that adoption would spread socially rather than through formal training alone.
Designed multiple reinforcement channels to accelerate learning and reduce resistance:
Weekly office hours
Learning Sprint cohort
Peer-led sharing
Executive participation
Open discussion forums
These mechanisms were intended to create visible proof points and normalize experimentation across the organization.
Created executive feedback and steering processes
Implemented a leadership pulse process to monitor adoption trends, identify barriers, and adjust the program as conditions changed.
Used survey data and qualitative feedback to continuously refine content, messaging, and support mechanisms.
This shifted the initiative from a fixed training plan to an adaptive change-management process.
Established governance and risk controls
Designed the governance framework required to scale adoption responsibly.
This included:
AI usage guidance
Maturity framework
Connector governance
Human review expectations
Escalation paths for emerging risks
The objective was to increase organizational confidence while ensuring speed did not outpace judgment.
Built executive sponsorship and visibility
Worked with senior leadership to make AI adoption visible and acceptable across the organization.
Encouraged leadership participation in office hours and learning activities, helping reinforce that AI was a capability-building initiative rather than a workforce reduction initiative.
Results
Adoption: Frequent use rose in every division. In the largest, most reliable group it went from about a third of people to nearly half, with non-use near zero.
Comfort and confidence: Comfort rose from roughly 40% to 55% company-wide, and over 20 points in one business unit.
Impact: 94 to 100% of respondents, depending on the team, said AI was helping their work.
Organizational outcome: AI moved from isolated experimentation to a shared capability, with a common language and framework, and a repeatable model leadership can reuse for future workflow initiatives.
Key leadership insight
-The signal that mattered most was comfort, more than usage. Usage responds to a mandate. -Comfort reflects voluntary adoption, which is what holds once the pressure stops. The data showed people integrating AI into their work faster than they were building trust in it, which means long-term success depends as much on confidence and judgment as on tool access. --
Capabilities demonstrated:
-Enterprise change management
-AI adoption strategy
-Cross-functional program leadership
-Executive stakeholder alignment
-Workflow modernization
-Learning and enablement design
-Governance and risk management
-Adoption measurement and analytics
