2026 EXECUTIVE RESEARCH REPORT
Employees Adopted AI.
Organizations Haven’t Adapted.
85%
of employees already use AI.
Employees have already integrated AI into the way they work.
Organizations are still adapting the way work is designed, governed, measured, and shared.
Based on three years of research, this report reveals why the AI Adoption Gap has shifted from employee adoption to organizational adaptation, and what leaders can do next.
A Note From the CEO
Three years ago, we set out to answer what seemed like a straightforward question: How are professionals adopting artificial intelligence? At the time, organizations were just beginning to experiment with generative AI. Employees were curious. Leaders were cautious. Nearly every conversation centered on one challenge: How do we get people to adopt AI?
We assumed this report would document that journey. In many ways, it does. Over the past three years, Human Driven AI has surveyed thousands of professionals, conducted hundreds of executive interviews, facilitated focus groups, and worked alongside teams inside enterprise organizations integrating AI into their daily work. This report captures that evolution. You’ll see how professionals moved from experimentation to routine use. You’ll hear the questions they asked, the concerns they voiced, the workflows they developed, and the ways their expectations changed as AI became part of everyday work.
But something unexpected happened along the way.
As we documented how employees were adapting to AI, another pattern began to emerge. The organizations themselves weren’t adapting at the same pace. What began as a study of AI adoption became something much more important: a study of organizational transformation. That realization changed the direction of our research, and ultimately became the central finding of this report.
Today, the challenge facing most organizations is no longer convincing employees to use AI. It is helping the organization learn from, scale, and govern the new ways of working employees have already created. It is helping the organization learn from, scale, and govern the new ways of working employees have already created. The companies succeeding are redefining roles, not eliminating people.
My hope is that this report serves two purposes. First, as a record of one of the fastest workplace transformations we’ve ever experienced, captured through the voices of professionals and leaders living it every day. Second, as a guide for leaders preparing for what comes next. The first chapter of enterprise AI was about adoption. The next chapter is about organizational adaptation. Thank you for joining us on that journey.
Jennifer Jones-Mitchell, Founder & CEO, Human Driven AI
What This Report Is
This isn’t a snapshot survey.
It isn’t a technology report.
It isn’t another collection of AI predictions.
It is a three-year record of how enterprise AI evolved inside real organizations.
Through surveys of 2,724 professionals, 594 executive interviews, focus groups, hundreds of workflow designs, organizational assessments, and hands-on implementation across 22 organizations, we documented not only what people thought about AI, but how those thoughts, behaviors, questions, and ways of working changed over time.
Those changes led us to one conclusion:
The next challenge isn’t AI adoption. It’s organizational adaptation.
RESEARCH FINDING #1
THE AI ADOPTION GAP ISN’T WHAT WE THOUGHT.
We expected to find an employee adoption problem.
Instead, we found an organizational adaptation problem.
Employees quietly created new ways of working.
Organizations haven’t yet redesigned how the work is managed.
“I stopped asking whether AI could do the work. I started asking which parts I should still do myself.”
— Communications Professional, Enterprise Retailer, 2026
By 2026, AI adoption is no longer the primary challenge. Organizational guidance has become the greater need.
85% are already using AI.
88% are asking leadership what comes next.
“Employees didn’t wait for permission to become more productive. They have already integrated it into their work. Organizations are now trying to catch up. Leaders need to change how they think about work itself.”
— Jennifer Jones-Mitchell, Founder and CEO, Human Driven AI
Employees Moved First. Organizations Are Still Catching Up.
The data suggests organizations aren’t struggling to deploy AI; they’re struggling to redesign work around it.
Organizations do not have an employee adoption problem. Employees have already integrated AI into the way they work. The challenge is that most organizations have not yet redesigned how work is governed, shared, measured, and improved.
For years, organizations focused on selecting AI tools. Meanwhile, employees quietly redesigned the way work gets done. Thousands of individual decisions reshaped research, writing, planning, analysis, and collaboration, often without becoming part of a shared organizational operating model.
The technology isn’t the bottleneck anymore. Organizational change is.
Employees didn’t wait for permission to become more productive. The result wasn’t one coordinated transformation. It was thousands of individual innovations that remained trapped within teams and individuals instead of becoming organizational capability.
Organizations that close this gap won’t simply become more efficient. They’ll be the ones that capture individual innovation and turn it into shared capability and measurable ROI.
RESEARCH FINDING #2
Why This Matters
The AI Adoption / Adaption Gap isn’t an employee problem. It’s a knowledge problem.
Every employee experimenting with AI is discovering faster, better, or different ways to work. Meanwhile, most organizations have no systematic way to capture, evaluate, share, or scale what those employees are learning.
Individual learning isn’t becoming organizational capability.
01
Reinventing Work
Employees spend time rebuilding prompts and workflows instead of building on proved processes.
02
Inconsistency
Without guidance, similar work produces different outputs, quality standards, and decisions across teams.
03
Untapped Value
Organizations haven’t yet captured + scaled what employees are learning.
04
Limited Visibility
Leaders can’t see which AI best practices should become standard.
Organizations don’t lose AI value because employees aren’t innovating.
They lose it because innovation never becomes organizational capability.
“It feels like every team is kind of figuring it out on their own. Then you’d hear somebody else had already solved the same problem six months ago, just in a different way.”
— Senior Director, Communications, Enterprise Technology Company, 2025
“Technology rarely creates organizational chaos. Generative AI exposes the cracks that unclear processes and insufficient support have already created and makes them impossible to ignore.”
— Katherine Morales, APR, VP, Operations & Governance
Companies bought the AI before building the operating model.
The real investment isn’t AI. It’s redesigning how work happens around AI.
The winners won’t be the companies with the newest AI. They’ll be the ones that redesign work the fastest. Until that happens, much of AI’s value remains trapped in individual employees rather than becoming organizational capability.
The cost appears as duplicated effort, inconsistent quality, repeated prompt creation, compliance uncertainty, fragmented knowledge, and thousands of small inefficiencies that accumulate across the enterprise.
Technology is easy to buy. Changing how people work is harder.
Leaders often ask, “how much will this save.” The better question is: “how much organizational knowledge are we failing to capture every day?”
RESEARCH FINDING #3
AI’s First Workflows Became Everyday Work
Beginning in 2023, employees first applied AI to a handful of knowledge-work tasks, primarily content creation, research, presentations, and data analysis.
Over the next three years, those early experiments became everyday practice.
By 2026, these weren’t emerging use cases anymore. They now represent some of the most common ways professionals integrated AI into their daily work. This experimentation quickly evolved into routine knowledge work.
Employees aren’t experimenting anymore.
They’re building new ways of working faster than companies can capture them.
“AI has become as routine as email or a Teams meeting.”
— HR Executive, Financial Services Company, 2024
RESEARCH FINDING #4
The Evolution of Enterprise AI
Over three years, we watched the same pattern emerge across organizations: AI adoption consistently outpaced the systems, workflows, and governance designed to support it.
The survey data revealed what was happening. The executive interviews, focus groups, workflow design sessions, governance engagements, and live training revealed why it was happening.
2023
Individual experimentation.
Employees discover and test practical uses for AI.
2024
Organizational commitment.
Companies invested in platforms, policies, and training.
2025
Workflow Integration.
AI became part of everyday work.
2026
Operational transformation.
The challenge shifted from adoption to designing how humans and AI work together.
Today
The technology evolved. Employees evolved. The leadership challenge evolved.
RESEARCH FINDING #5
Employees Built Hundreds of AI Operating Models.
It wasn’t one enterprise transformation. It was thousands of employees independently redesigning the same work in different ways.
Different prompts. Different workflows. Different decision making. Same business problems.
01
Experimentation Accelerated
Employees quickly discovered practical ways to use AI in their daily work, often creating new approaches faster than organizations could document them.
02
Teams Evolved Independently
Different departments developed their own prompts, workflows, and best practices based on their immediate needs.
03
Results Become Inconsistent
Without shared guidance, similar work was completed in different ways, producing varying levels of quality and efficiency.
04
Scaling Became Difficult
Organizations now face a new challenge, not AI adoption, but bringing successful practices together into one repeatable operating model.
“Every team thought they were solving a unique problem. Most were solving the same problems differently.”
– Executive, Communications Agency, 2023
RESEARCH FINDING #6
The Questions Changed as AI Changed Work
What we heard inside organizations from 2023 to 2026.
Most research captures what people believe at one moment in time.
Because this study unfolded over three years, and because we were working directly with organizations as they adopted AI, we were able to observe something different: the questions changed.
These were not hypothetical questions. They emerged through employee surveys, executive interviews, focus groups, readiness assessments, hands-on training, workflow development, and implementation alongside the teams doing the work. Together, they tell the story of how AI moved from curiosity to daily use, and how the challenge shifted from adopting technology to redesigning the organization.
2023
Awareness
What people were asking
“What is generative AI?” “Can we trust it?” “Am I allowed to use it?” “Does it save us time?” “Will it replace my job?”
What we observed
Employees were testing tools, often cautiously and individually. Leaders were trying to understand the tech, assess risk, and decide whether AI belonged in the workplace at all.
2024
Experimentation and Adoption
What people were asking
“Which AI tools should we use?” “How do we write better prompts?” What can I safely give it?” How do I get a better answer?
What we observed
Use expanded quickly. Professionals began applying AI to writing, research, ideation, analysis, presentations, and routine knowledge work; most were developing their own methods.
2025
Integration and Governance
What people were asking
“How do we integrate it into our processes?” “How do we govern it?” How do we know the output is accurate?” Which workflows should we standardize?” “How do we measure ROI?
What we observed
Organizations began to realize access to tools was not enough. Individual use was growing, but quality, governance, knowledge sharing, and consistency varied widely across teams.
2026
Organizational Adoption
What people were asking
“How do people and AI work together?” “How do we scale AI?” “How do we customize AI to fit our organization?”
What we observed
The question is no longer whether employees would adopt AI. They had. The challenge shifted to redesigning workflows, operating models, governance, roles, and organizational systems around a workforce already using it.
“The future of AI won’t be decided by who adopts the newest tech first. It will be decided by who intentionally designs the best way for humans and AI to work together.”
— Jennifer Jones-Mitchell, Founder & CEO, Human Driven AI
The Biggest Shift Wasn’t AI. It Was How Work Changed.
Watching conversation evolve for three years, the questions changed.
Every year, organizations asked different questions. At first, they wanted permission to experiment. Then they wanted better tools. Then better prompts. Today they’re asking something much more important: How should work happen in an AI-enabled organization?
That’s the question that defines the next phase of AI transformation. Organizations no longer need another AI platform.
They need a shared operating model that turns individual innovation into organizational capability.
RESEARCH FINDING #7
By 2026, Employees Weren’t Asking for More AI. They Were Asking for Direction.
By 2026, the conversation had shifted. Employees were no longer asking for more AI tools; they were asking how to use them consistently, responsibly, and effectively.
88%
Governance & Guidance
Employees want clearer rules for when and how AI should be used.
76%
Hands-On Training
Employees want hands-on guidance they can apply to real work.
80%
Prompt Engineering Support
Employees want help writing better prompts and improving AI outputs.
74%
Role-Specific Examples
Employees want use cases tied to their actual responsibilities.
We heard this years before it became the dominant theme.
“AI isn’t the problem. Untrained AI use is the problem.”
— Faculty Member, Higher Education, 2024
Years later, that prediction has become reality.
The demand is not for more tools.
It is for direction and shared ways of working.
“Adults don’t learn AI by mastering features. They learn it by solving the work they need to do tomorrow morning.”
— Dr. David Mitchell, Chief Education Officer, Human Driven AI
AI education is no longer a training initiative. It’s operational infrastructure.
Employees aren’t asking for another AI platform. They’re asking for the confidence, guidance, and shared practices to use the platforms they already have.
Across surveys, executive interviews, workflow design sessions, and live trainings, one pattern emerged consistently: employees weren’t asking for more AI. They were asking for clearer direction on how to use it responsibly, consistently, and effectively inside their organizations. They wanted governance. Practical examples. Role-specific guidance. Hands-on experience.
That changes the role of training.
Training is no longer a one-time introduction to a new technology. It’s how organizations establish shared ways of working, improve quality, accelerate learning, and scale what employees are already discovering.
Training no longer builds individual AI skills. It builds organizational AI capability.
Organizations don’t build AI capability by deploying software. They build it by helping people develop shared ways of working, and turning individual knowledge into organizational capability.
RESEARCH FINDING #8
By 2026, Most Employees Were Still Early in Their AI Journey
AI adoption accelerated faster than AI expertise.
By 2026, AI adoption had become commonplace. Expertise had not.
Organizations shouldn’t mistake widespread use for widespread mastery.
“People are either terrified of it, or convinced AI will solve everything. Neither group really understands it.
— HR Leader, Communications Agency, 2023
“I thought I somewhat understood AI, until you started talking about workflows.”
— Fundraising Professional, National Nonprofit, 2026
Adoption and confidence are not the same thing.
Technology can be deployed overnight. Confidence is built over time.
RESEARCH FINDING #9
Organizations Didn’t Build Capability. Employees Did.
As AI use became routine by 2026, employees increasingly relied on self-teaching rather than structured organizational development.
81%
Believe they know how to write effective prompts
72%
Have never received formal prompt training
76%
Rarely evaluate or improve their prompts
Employees are figuring it out on their own. Organizations aren’t building structured, uniform capability.
“Individual confidence accelerates AI adoption. Organizational capability determines whether it scales.”
— Jennifer Jones-Mitchell, Founder and CEO, Human Driven AI
Confidence Isn’t Capability. And Curiosity Isn’t Readiness.
The next competitive advantage isn’t AI adoption. It’s organizational capability.
Organizations have made remarkable progress in a short period of time. Employees learned to use AI faster than most leaders expected. But confidence alone doesn’t create organizational capability. Without shared ways of working, AI becomes an individual productivity tool instead of an organizational advantage.
Our research suggests the next phase of AI adoption isn’t about learning another tool. It’s about turning thousands of individual AI decisions into repeatable organizational practices.
The next phase of AI transformation will not be defined by curiosity or confidence. It will be defined by capability.
When confidence outpaces capability, organizations mistake AI activity for AI maturity.
Organizations that intentionally build workforce capability, operational governance, and shared Human + AI workflows won’t simply adopt AI faster. They’ll create a competitive advantage that compounds over time.
Confidence belongs to individuals. Capability belongs to organizations.
RESEARCH FINDING #10
AI Moved Beyond Tasks. It Began Changing How Organizations Think.
The first wave of enterprise AI focused on tasks.
Employees used AI to write content, summarize research, build presentations, and analyze information faster.
By 2026, employees reported using AI to improve how they think and work.
AI was no longer being used simply to complete work. It was increasingly shaping how employees generated ideas, made decisions, solved problems, and approached knowledge work.
AI no longer supports only what employees do. It’s increasingly influencing how they think, decide, and create.
The first wave of enterprise AI helped employees complete individual tasks. By 2026, AI had become a thinking partner—helping professionals generate ideas, improve judgment, make decisions, and reshape knowledge work itself.
RESEARCH FINDING #11
By 2026, the conversation had shifted again.
Scaling AI Isn’t a Technology Problem. It’s a Governance Problem.
The first three years of enterprise AI focused on adoption.
The next phase is about scale.
Organizations and employees across our research consistently identify governance, not technology, as the capability that would determine whether AI remains a collection of individual successes or becomes an enterprise advantage.
88%
of organizations identified Governance as their next strategic priority
Regardless of industry, organizations consistently identified governance, policies, and responsible AI practices as essential for scaling AI adoption successfully.
76%
want Privacy & Security Governance
Employees want guidance on what information can safely be entered into AI systems, which tools are approved, and how sensitive company data should be protected. Without those guardrails, employees make individual decisions about risk, often with inconsistent results.
65%
want policies addressing Accuracy and AI Hallucinations
Organizations are increasingly concerned about AI-generated inaccuracies, fabricated information, and inconsistent outputs. Employees need practical review processes, verification standards, and human oversight, not simply a reminder to “be careful.”
Governance isn’t simply about reducing risk. It’s about making successful AI repeatable across an organization.
RESEARCH FINDING #12
The Final Shift: From AI Adoption to AI Accountability
The first three years of enterprise AI transformed the conversation.
Organizations moved from asking: Should we use AI? to How do we use AI? to How do we govern AI?
By 2026, another question emerged:
How do we remain accountable for decisions made with AI?
Because once AI influences content, decisions, creativity, research, and daily workflows, accountability becomes a leadership responsibility, not an employee responsibility.
“The organizations moving fastest aren’t the ones with the fewest guardrails. They’re the ones with the clearest ones.”
— Katherine Morales, APR , VP, Operations and Governance
Governance Is How Leaders Turn Individual AI Use Into Organizational Capability.
It’s about creating the guardrails and to help your teams make better decisions.
For years, governance was viewed as something that restricted innovation.
Our research suggests the opposite.
The organizations moving fastest with AI aren’t succeeding because they’ve eliminated guardrails. They’re succeeding because they’ve established shared expectations that allow employees to innovate confidently, consistently, and responsibly.
Governance isn’t about controlling AI. It’s about creating a shared way of working. The purpose of governance isn’t to limit AI. It’s to reduce organizational friction.
When expectations are clear, knowledge spreads faster, quality becomes more consistent, and successful AI practices scale beyond individual teams.
That’s how organizations move from AI adoption to organizational capability.
RESEARCH CONCLUSION:
The Next Competitive Advantage Isn’t Better AI.
It’s Better Organizational Design.
By 2026, access to AI is no longer the differentiator.
The organizations that outperform won’t necessarily have the smartest models. They’ll be the ones that redesign work, governance, learning, and leadership so humans and AI improve together.
84%
of employees want AI embedded into everyday work.
By 2026, employees want AI integrated systematically into how their work gets done individually and collaboratively.
77%
of leaders want prompt libraries and collaborative playbooks.
By 2026, organizations want shared workflows that can be repeated, improved and scaled across teams.
The first chapter of enterprise AI was about access.
The second was about adoption.
The next chapter belongs to organizations that intentionally design how humans and AI work together.
“The next generation of AI won’t be measured by smarter models. It’ll be measured by smarter organizations.”
— Dr. David Mitchell, Chief Education Officer
HDAI PERSPECTIVE
The future won’t belong to the organizations with the most AI.
It will belong to the organizations that redesign themselves around it.
For the past three years, organizations focused on choosing AI tools. Today they’re asking a different question: How should work change? That’s the question this research set out to answer.
Our findings suggest the organizations creating the greatest value aren’t simply deploying AI faster. They’re intentionally redesigning how people, workflows, governance, leadership, and technology work together.v
Human + AI By Design™ is our answer, built from what three years of research and client work told us organizations actually need. It closes the distance between adoption and readiness across four connected parts:
Foundation: Build the operating system for AI adoption. Governance, policies, tech recommendations, and operational standards that make AI safe, scalable, and sustainable.
Integration: Redesign how people and AI work together. Map existing workflows, identify where AI creates real value, and build Human + AI Workflow Blueprints™ teams can follow.
Education: Turn AI into everyday behavior. Customized workshops, executive education, and department-specific training designed around how your organization actually works.
Automation: Turn your best work into intelligent AI systems. Custom GPTs, AI agents, knowledge assistants, and workflow automation that make expertise repeatable.
Together, they turn fragmented, individual AI use into a coordinated operating model, addressing the needs this research surfaced, from governance and consistency to workforce skill and scalable workflows.
Technology will continue to evolve. Organizational design will determine who wins.
Closing the AI Adoption Gap
The AI Adoption Gap wasn’t created because employees resisted AI. It emerged because employees transformed work faster than organizations transformed themselves.
Technology alone won’t close that gap.
Only intentional organizational design will.
The organizations that win won’t have access to different AI.
They’ll build different organizations.
What Leaders Should Do Next
✓ Assess organizational AI readiness before scaling adoption.
✓ Establish governance, policies, and responsible-use expectations.
✓ Create shared Human + AI standards across teams.
✓ Redesign workflows to intentionally define human and AI responsibilities.
✓ Build workforce confidence alongside technical capability.
✓ Measure adoption by business outcomes, not tool usage.
One Final Thought
The Future of AI Won’t Be Built by Technology Alone.
It will be defined by how intentionally organizations redesign work, capture knowledge, and create systems where humans and AI improve together.
The AI race isn’t a race to adopt better tech. It’s a race to build better organizations.
Technology will continue to evolve.
Competitive advantage won’t come from access to better AI. It will come from building organizations that learn faster, adapt together, and intentionally redesign how people and AI create value.
That’s the challenge ahead.
And it’s the opportunity.
“Technology may be artificial. Competitive advantage never is. It comes from intentionally designing how people and AI create value together.”
— Jennifer Jones-Mitchell, Founder & CEO, Human Driven AI
Schedule a free discovery call with Human Driven AI
ABOUT HUMAN DRIVEN AI
Human Driven AI (HDAI) helps organizations redesign how work gets done in the age of artificial intelligence. From custom AI trainings and tech stack selection to governance and AI automation, we help your people adopt AI and your organizational structures adapt to it.
Over the past three years, we have studied AI adoption across thousands of professionals and hundreds of executive leaders while partnering with organizations across enterprise technology, retail, healthcare, financial services, higher education, nonprofit, and communications.
Our work extends beyond AI training. We help organizations design Human + AI operating models that combine governance, workflow integration, workforce enablement, and AI visibility into one intentional system.
Our approach is built on a simple belief: Competitive advantage won’t come from having better AI tools. It will come from designing better ways for people and AI to work together.
This report is one part of our ongoing research into how AI is reshaping organizations, from internal workflows and leadership to AI visibility, customer discovery, and the future of work.
Learn more about our research, AI programs, and Human + AI By Design™
THE WHAT
About the Study
The Human Driven AI Executive Research Study analyzed data collected between 2023 and 2026 from 2,724 survey respondents and 594 executive and practitioner interviews conducted across 22 organizations. Respondents represented multiple organizational levels and functional disciplines, including executive leadership, marketing and communications, human resources, information technology, operations, legal, learning and development, and other business functions. While the organizations differed in size, industry, and AI maturity, every participant was actively evaluating, implementing, or governing AI within their organization.
The research combined quantitative surveys, executive interviews, organizational assessments, and consulting engagements to identify emerging patterns across industries. Rather than measuring technology adoption alone, the study examined organizational readiness, governance, workforce confidence, workflow integration, and operational maturity.
THE WHO
Participants
The Human Driven AI Executive Research Study analyzed data collected between 2023 and 2026 from 2,724 survey respondents and 594 executive and practitioner interviews conducted across 22 organizations. Respondents represented multiple organizational levels and functional disciplines, including executive leadership, marketing and communications, human resources, information technology, operations, legal, learning and development, and other business functions. While the organizations differed in size, industry, and AI maturity, every participant was actively evaluating, implementing, or governing AI within their organization.
Participating organizations represented a broad cross-section of industries, including:
– Enterprise Technology
– Enterprise Retail
– Financial Services
– Healthcare
– Higher Education
– Nonprofit Organizations
– Professional Associations
– Marketing, Communications & Public Relations Agencies
THE HOW
Methods
The study combines multiple research methodologies, including:
– Quantitative surveys
– Executive interviews
– AI readiness assessments
– Workflow observations
– Organizational consulting engagements
– AI training and enablement programs
– Longitudinal trend analysis across three years
This mixed-method approach allowed findings to be validated across multiple data sources rather than relying on survey responses alone.
THE INTERPRETATION
About the Findings
Percentages throughout this report represent survey responses collected across participating organizations unless otherwise noted. Quotes have been anonymized to protect confidentiality while preserving the context and meaning of participant responses. Because the research spans multiple years, individual findings reflect the evolution of organizational AI adoption rather than a single point in time.
AI Platform Use
- ChatGPT Usage: 81% | Base: 1,701 asked (62% of total sample) | ~1,378 respondents | 5 organizations | Enterprise Retail, Financial Services, Healthcare, Nonprofit
- Copilot Usage: 64% | Base: 1,666 asked (61% of total sample) | ~1,066 respondents | 4 organizations | Enterprise Retail, Financial Services, Nonprofit
- Gemini Usage: 19% | Base: 1,419 asked (52% of total sample) | ~270 respondents | 4 organizations | Enterprise Retail, Financial Services, Healthcare, Nonprofit
- Claude Usage: 12% | Base: 1,419 asked (52% of total sample) | ~170 respondents | 4organizations | Enterprise Retail, Financial Services, Healthcare, Nonprofit
Current AI Use
- Content Creation: 85% | Base: 1,969 asked (72% of total sample) | ~1,674 respondents | 6 organizations | Enterprise Retail, Enterprise Technology, Financial Services, Nonprofit
- Research & Analysis: 75% | Base: 1,687 asked (62% of total sample) | ~1,265 respondents | 5 organizations | Enterprise Retail, Enterprise Technology, Financial Services, Nonprofit
- Presentation Development: 69% | Base: 1,659 asked (61% of total sample) | ~1,145 respondents | 3 organizations | Enterprise Technology, Financial Services
- Analytics & Reporting: 59% | Base: 1,969 asked (72% of total sample) | ~1,162 respondents | 6 organizations | Enterprise Retail, Enterprise Technology, Financial Services, Nonprofit
Training & Requested Support
- Prompt Engineering Support: 80% | Base: 1,659 asked (61% of total sample) | ~1,327 respondents | 3 organizations | Enterprise Technology, Financial Services
- Governance Guidance: 85% | Base: 1,779 asked (65% of total sample) | ~1,512 respondents | 4 organizations | Financial Services, Nonprofit
- Role-Specific Examples: 74% | Base: 1,479 asked (54% of total sample) | ~1,094 respondents | 2 organizations | Financial Services
- Prompt Libraries: 77% | Base: 1,479 asked (54% of total sample) | ~1,139 respondents | 2 organizations | Financial Services
- Practical AI Training: 76% | Base: 1,656 asked (61% of total sample) | ~1,259 respondents | 3 organizations | Financial Services, Nonprofit
- Demand for AI Embedded in Work Apps: 84% | Base: 1,479 asked (54% of total sample) | ~1,242 respondents | 2 organizations | Financial Services
Governance & Risk
- Organizations requesting governance: 19 of 22 organizations (or 86%) | all 8 sectors
- Privacy: 76% | Base: 1,941 asked (71% of total sample) | ~1,475 respondents | 4 organizations | Enterprise Technology, Financial Services
- Accuracy: 65% | Base: 1,761 asked (65% of total sample) | ~1,145 respondents | 3 organizations | Financial Services
- Brand Reputation: 52% | Base: 1,941 asked (71% of total sample) | ~1,009 respondents | 4 organizations | Enterprise Technology, Financial Services
- Copyright/IP: 51% | Base: 1,659 asked (61% of total sample) | ~846 respondents | 3 organizations | Enterprise Technology, Financial Services
- Regulatory: 56% | Base: 1,761 asked (65% of total sample) | ~986 respondents | 3 organizations | Financial Services
Workforce Readiness: Self-assessed AI proficiency distribution
- Beginner — 47%
- Intermediate — 38%
- Advanced — 14%
- Base: 1,888 assessed (69% of total sample) | 7 organizations | Enterprise Retail, Enterprise Technology, Financial Services, Nonprofit
Research Evolution: Because this research was conducted over three years, new questions were added as AI adoption evolved. Early surveys focused on awareness and experimentation. Later surveys examined governance, operating models, workforce capability, responsible AI, and organizational design. As a result, individual findings are based on the respondents who were asked each question rather than the total sample population.
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