Here's a number that should make every executive stop and think:

70% of workers are open to offloading work to AI.

According to [Deloitte's 2025 Global Human Capital Trends research](https://www.deloitte.com/us/en/services/consulting/articles/human-capital-and-hr-trends-thought-leadership.html), the vast majority of your workforce *wants* AI to help them. They want to offload tedious tasks. They want more time for meaningful work. They want augmented capability.

Now here's the number that should keep you up at night:

Only 9% of organizations have achieved AI maturity.

Seventy percent of workers are ready. Nine percent of organizations can deliver. That's an organizational failure gap of 61 percentage points.

Your people are waiting. Your systems are failing them.

The Readiness Paradox

This paradox—worker openness combined with organizational stagnation—reveals something important: **The AI transformation problem isn't technology. It's everything surrounding technology.**

[PwC's 2025 Global Workforce Survey](https://www.pwc.com/gx/en/issues/workforce/hopes-and-fears.html) found that employees who trust their direct manager are **72% more motivated** than those who don't. AI adoption follows the same pattern.

Where human systems work—where there's trust, clarity, safety, and capability—AI adoption succeeds. Where human systems are broken—where managers are burned out, communication is poor, and fear goes unaddressed—AI adoption stalls regardless of the technology.

The 9% achieving AI maturity aren't using different tools. They're running different organizations.

Five Ways Organizations Block Their Own AI Success

At hmn, we've studied why AI adoption fails. The patterns are consistent:

1. The Mandate Problem

Executives announce AI transformation. They purchase tools. They set adoption targets. Then they declare victory.

But mandates don't produce transformation. "Use AI" becomes another checkbox on already-overwhelmed employees' lists. People comply minimally while continuing to work the old way.

Mandates produce compliance theater. They don't produce capability development or workflow transformation.

2. The Fear Problem

Despite 70% openness to AI in the abstract, concrete fear persists:

*"Will AI replace me?"* — The existential anxiety that no amount of corporate reassurance fully addresses
*"Will I look incompetent?"* — The fear of being slower to learn than colleagues
*"Is using AI cheating?"* — Genuine ethical uncertainty about AI assistance
*"What if I break something?"* — Fear of AI mistakes with professional consequences

Organizations that don't surface and address these fears don't get genuine adoption. They get performative usage and underground resistance.

3. The Skills Problem

Openness to AI doesn't equal capability with AI.

[According to SHRM](https://www.shrm.org/about/press-room/shrm-report-warns-of-widening-skills-gap-as-ai-adoption-reaches-), only 35% of employees received any AI training last year—and most of that was basic tool familiarization, not workflow integration or judgment development.

Workers want AI to help. They don't know how to make that happen. And their organizations aren't teaching them effectively.

4. The Manager Problem

Managers are the make-or-break layer for AI adoption:

When managers don't model AI use, employees receive a clear signal: *"This isn't actually important."*
When managers don't coach AI integration, employees struggle alone
When managers are too burned out to lead another change initiative, AI adoption dies in the middle
When managers can't answer AI questions, employees don't ask

With 75% of managers reporting burnout, most lack capacity to drive AI adoption even if they have the will.

5. The Integration Problem

Most organizations deploy AI tools without redesigning workflows around them. Employees have to:

Switch between multiple interfaces
Figure out integration themselves
Navigate unclear policies about AI use
Work around friction points nobody addressed

Every friction point reduces adoption. Most organizations create dozens of them.

What 70% Openness Actually Represents

The Deloitte finding is genuinely remarkable. 70% openness to organizational change is rare.

But it requires careful interpretation. Workers are saying:

**Yes to:** - Offloading tedious, repetitive tasks - Having more time for meaningful work - Augmented capability for complex analysis - Faster execution of routine processes

**Not yes to:** - Figuring it out alone without support - Looking incompetent during the learning curve - Losing what makes their work valuable - Training the AI that eventually replaces them

The 70% is conditional openness. Organizations that meet the conditions—support, safety, meaning preservation—capture it. Organizations that don't create cynicism and resistance.

What the 9% Do Differently

The organizations achieving AI maturity share common characteristics:

Leadership models first.

Executives and managers visibly use AI, share their learning curves, normalize experimentation, and demonstrate that AI competence matters. They don't just mandate from above—they demonstrate from within.

Fear gets named and addressed.

These organizations have honest conversations about AI's impact on roles. They make concrete commitments about workforce development. They create explicit safety for the vulnerable emotions AI evokes.

Pretending fear doesn't exist doesn't make it go away. It drives it underground.

Skills build continuously.

Not one-time training events, but ongoing development infrastructure. Learning is embedded in how work happens, not separated into occasional courses.

Capability develops through practice, not PowerPoint.

Managers are equipped and empowered.

Managers have capacity to lead AI adoption—which means they're not completely consumed by other demands. They have specific coaching skills for AI integration. They can answer questions and guide experimentation.

The manager layer is enabled, not bypassed.

Workflow transformation, not tool deployment.

AI gets embedded in how work actually happens. Workflows are redesigned around human-AI collaboration. Friction points are identified and removed.

The goal is transformation, not technology addition.

The hmn Approach: Closing the 61-Point Gap

We've built our entire approach around this insight: **AI transformation is human transformation.**

The technology is the easy part. Anyone can deploy AI tools. The hard part is building organizations where AI actually gets used effectively—where people feel safe enough to experiment, skilled enough to contribute, supported enough to persist.

We call this **adaptation capacity**—the organizational capability to continuously evolve without burning out.

Our work focuses on three interconnected systems:

Manager Activation

Since managers determine adoption success, we start with manager capability. We develop specific skills for leading AI transformation:

How to model AI use visibly and authentically
How to coach AI integration in day-to-day work
How to address fear and resistance directly
How to create safe experimentation spaces
How to evaluate AI use quality

Activated managers become adoption accelerators. Burned-out, untrained managers become adoption blockers.

Culture Architecture

Sustainable AI adoption requires cultural infrastructure—norms, expectations, and systems that support continuous learning and experimentation.

We help organizations build:

Explicit permission structures for AI experimentation
Recognition systems that reward capability development
Knowledge-sharing mechanisms that spread learning
Feedback loops that connect AI use to outcomes

Culture isn't what you say. It's what you reward and what you punish.

Skill Integration

Generic AI training doesn't produce capable workers. Role-specific, workflow-integrated skill development does.

We work with organizations to:

Identify highest-value AI use cases by function
Develop targeted capability-building for each
Create practice opportunities within actual work
Measure capability development, not training completion

The Urgency of the Moment

The 70% openness is a wasting asset.

Every month of failed AI initiatives, every clunky rollout, every unanswered question, every abandoned tool erodes trust and builds cynicism.

Workers who started open become skeptical. Skeptical workers become resistant. Resistant workers become the reason your next transformation fails.

The window for capturing worker enthusiasm is narrowing. Organizations that move now—that build the human systems to match their technology investments—will capture value competitors miss.

What You Can Do This Quarter

While comprehensive capability building requires systematic intervention, you can start closing the gap immediately:

1. Ask the fear questions.

Survey your organization: *"What concerns do you have about AI in your work?"* Listen without defensiveness. Address what you can. Build trust through honesty.

2. Identify your friction points.

Where are people struggling to integrate AI into workflows? What's creating friction? What policies are unclear? Map the specific obstacles and start removing them.

3. Activate your managers.

Start with your most capable managers. Build their AI coaching skills. Let them become adoption accelerators for their teams. Expand from there.

4. Pick three use cases.

Instead of broad AI mandates, pick three specific use cases with clear value. Build capability around those specific applications. Demonstrate success. Expand from there.

5. Measure adoption behavior, not tool deployment.

Stop counting how many AI licenses you've distributed. Start measuring how many people are actually using AI, how effectively, and what's blocking those who aren't.

The Bottom Line

70% of your workers want AI to work. Only 9% of organizations deliver on that promise.

The gap isn't technology. It's everything else—leadership, culture, manager capability, skills development, fear, and workflow integration.

Your people are ready. Your organization is blocking them.

The organizations that close this gap will capture unprecedented productivity, capability, and competitive advantage. The ones that don't will watch their best people disengage from failed transformation after failed transformation.

The technology exists. The openness exists. What's missing is the human system to connect them.

**Is your organization ready to close the gap?** [Start the Adaptation Assessment](/) and discover what's actually blocking your AI success.

Your People Want AI to Work. Your Organization Is Getting in the Way.