As AI investment soars, many organizations are hitting the same wall: Progress is slower than expected, and the results are uneven. Nearly two-thirds of organizations haven’t yet scaled AI beyond a few pilots, and no more than one in 10 report AI agent usage making it past the pilot stage within a specific business function.
What separates leaders from the rest isn’t just better data, models, or tools. It’s making a fundamental shift that helps people adopt, adapt, and create value with AI every day.
People and culture have always determined transformation success, but AI raises the stakes. Success now depends on moving from vision to action at unprecedented speed, redesigning work to capture AI’s full potential, and building teams that learn, adapt, and innovate faster than ever before.
Here are five questions leaders should ask to understand whether their organization is ready to adopt, scale, and capture value from AI. This perspective is grounded in over 20 years of research and experience with leveraging organizational health and the core building blocks of change, known as the influence model. At its core, scaling AI is a people and culture change effort, and transformations are nearly eight times more likely to succeed when activating all elements of the influence model.
Can employees see how AI advances strategy?
Leaders and employees alike should understand how AI advances the organization’s broader strategy. That shared understanding determines where to focus, how to invest, and what value AI can unlock.
Organizations with a clear, shared vision—where strategy is translated into specific goals, priorities, and milestones—build stronger, healthier cultures and outperform peers. When leaders define a vision for how AI will create value, people rally around it. They know where AI fits, what success looks like, and how their work contributes.
This requires translating vision into practical, measurable objectives: making explicit choices about where AI creates value, setting goals with clear metrics (e.g., growth, cost, margin, customer impact), and cascading clarity from strategy to execution.
For instance, one pharmaceutical company anchored its digital transformation around cutting a year from its drug development processes. By transforming digital from a set of tools to a core enterprise strategy, it reduced launch timelines to three months (from 12-18) and identified $100 million in annual run-rate impact within three years.
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Are leaders truly leading and championing AI?
Organizations capturing real value from AI are three times more likely to have senior leaders who own and champion their AI agenda. In these companies, leaders don’t just communicate a vision; they model it. They use AI, experiment with it, and make it part of how they lead.
Transformation success becomes more than five times more likely when leaders consistently model new behaviors. Employees follow suit when they see leaders and peers using AI to make better decisions, automate work, or spark innovation.
In organizations that get this right, leaders use AI to improve results, share wins openly, demonstrate curiosity and adaptability, and celebrate employee-driven experiments—making learning with AI part of everyday work.
At one bank, senior leaders became AI’s most visible users, integrating dashboards and tools into daily decision making. This signaled AI fluency as an essential behavior to be evaluated against and fueled rapid adoption, helping generate $150 million in incremental revenue.
Do employees know how AI reshapes their roles?
As AI takes on more analytical and routine work, shifts in roles, skills, and operating models can create confusion and anxiety. Recent McKinsey Global Institute research suggests that some roles will evolve dramatically; others will disappear altogether, and entirely new roles are emerging almost overnight.
Role clarity—understanding what’s expected, how success is measured, and where value is added—is one of the strongest predictors of a healthy, high-performing culture. In an AI transformation, leaders should deliberately design work, help employees understand how their responsibilities are changing, and reimagine how humans and AI work together.
Organizations that get this right show how AI augments human strengths—creativity, empathy, problem solving—and invest in tailored learning. The upside is significant: By 2030, about $2.9 trillion in economic value could be unlocked in the U.S. if organizations prepare their workforces and redesign workflows around people, agents, and robots working together.
One telecom player deployed an AI-powered coaching and learning system that provides real-time guidance to frontline employees, making explicit how their roles are evolving and how AI supports their work. The shift improved customer likelihood-to-recommend scores by 14 points while raising employee satisfaction.
Does the organization have the right people to lead in the AI era?
Thriving will depend on building an AI-fluent workforce; every employee will need to learn how to work with AI, not around it.
Technical expertise is essential, but the best AI organizations double down on skills that make people indispensable—critical thinking, creativity, collaboration, emotional intelligence. The goal is to close today’s skill gaps while creating a culture of continuous learning, where employees grow with technology rather than fall behind it.
Leaders should ask whether employees have the skills to use AI effectively and, if not, whether the organization has training, coaching, and learning communities to close gaps quickly.
One retailer uses an AI bot to manage nearly half of customer requests. Instead of displacing its 8,500 call center employees, the organization retrained them, increasing job satisfaction while achieving $1.4 billion in revenue gains.
Are employees actively experimenting with AI to reinvent work?
AI is rewriting the playbook faster than most organizations can turn the page, and many employees are struggling to keep up. Thriving requires a culture of experimentation that values curiosity, iteration, and agility over perfection.
This means creating safe spaces to test, learn, and iterate with AI. It means embedding learning loops into everyday work so that experimenting, reflecting, and improving become part of how teams operate.
In these environments, employees help shape how AI is used. Leaders set the tone by rewarding thoughtful risk taking and shifting the mindset from “prove it” to “try it.” That’s what turns uncertainty into momentum.
But speed alone isn’t the answer. One of the most common failure modes is diffuse experimentation—many teams testing many tools with little coordination or return. Winning organizations encourage experimentation within clear, strategic boundaries tied to value.
One insurer reinvented its claims operation by continuously testing, refining, and expanding AI models across the end-to-end journey. This experimentation-driven culture improved Net Promoter Score, cut customer complaints by 65 percent, and drove record-high employee engagement.
AI will amplify what’s already true about an organization. If culture is healthy, AI can accelerate progress. If not, it can accelerate dysfunction. Organizational health is the foundation for everything that follows—and activating all elements of change together is what separates AI ambition from enterprise value capture.
Organizations breaking through with AI know it’s not just about technology. They lead with purpose and clarity, invest in people, and create room for teams to test bold ideas and turn them into breakthroughs.
That’s how companies escape “pilot purgatory”—by turning AI into the engine of how they work, compete, and grow.
The authors wish to thank Sandra Durth, Julie Goran, Thibaut Larrat, Rebecca Pool, and Joy Zhou for their contributions to this blog post.
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This blog post is part of a People and Organization Blog series that explores how organizations will be transformed by agentic AI. Follow us on LinkedIn and keep an eye on the blog for our latest insights and how these technologies will shape organizations today and tomorrow.