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AI Adoption at Scale Requires More Than Technology

Updated: 4 days ago

How Leadership Engagement, Organizational Alignment & Change Management Shape Outcomes


Artificial intelligence (AI) adoption is accelerating across industries. Organizations are investing heavily in AI platforms, embedding generative tools into workflows, and expanding use cases across functions. Yet, outcomes vary widely.


According to McKinsey’s latest State of AI in 2025 research, 88% of respondents report regular AI use in at least one business function, but most organizations are still in experimentation or pilot phases. Only 39% report any enterprise-level EBIT (earnings before interest and taxes) impact from AI.


Also, their findings show that organizations with a return on investment in their use of AI have more leadership engagement throughout the process of driving AI adoption. 

McKinsey defines AI “high performers” as organizations that report EBIT impact of 5% or more attributable to AI and “significant” value from AI use, representing about 6% of their survey respondents.  

These high performers are three times more likely than their peers to strongly agree that senior leaders demonstrate ownership of and commitment to AI initiatives. They also are more likely to report that leaders are actively engaged in driving adoption, including role-modeling AI use.


These insights are important because scaling AI requires more than deploying technology. They suggest that leadership engagement differentiates AI adoption that scales from adoption that plateaus. 

Leadership Engagement and Change Management

Outcomes diverge when leadership ensures change management runs alongside tech rollouts and continues over time. This helps align expectations, behaviors, and workflows as AI becomes embedded in daily operations.


According to change management benchmarking research by Prosci, they found that 88% of projects using excellent change management practices met or exceeded objectives, compared with only 13% of projects with poor change management.  


This is not an argument that change management “trumps” implementation, but it does demonstrate that disciplined, well-led change practices improve the odds that complex initiatives, such as AI rollouts and adoption, achieve their intended outcomes.


AI Adoption Is a Leadership-Led Change

AI adoption is, at its core, a technology adoption process. It involves decisions about infrastructure, data readiness, security, integration, and governance. These decisions are inherently leadership decisions which are approved, funded, and prioritized at senior levels.


What distinguishes AI from many prior technology investments is how directly it influences human judgment, decision-making, and accountability. 


AI does not simply automate tasks; it informs how work is evaluated and how decisions are made. As a result, adoption depends not only on successful deployment, but on how leaders guide organizations through the behavioral and operational changes that follow.


McKinsey’s findings further suggest that organizations seeing stronger outcomes are more likely to combine technical deployment with organizational practices that support adoption and value capture. These practices include workflow redesign, leadership engagement, strategy, talent, operating model, technology, data, and adoption/scaling.


This is where organizational alignment becomes critical.


Where AI Adoption Often Slows After Deployment

In many organizations, AI adoption follows a familiar trajectory:


  • Leadership approves AI investments and priorities

  • Technology teams deploy platforms and tools

  • Initial training is delivered

  • Teams are expected to adapt quickly


Each phase involves leadership oversight. The challenge often emerges after AI moves from project status to everyday work.


At this stage, alignment work is often compressed. Expectations become unclear rather than clearly defined. Guidance varies by team or manager. Change management is treated as a short-term activity rather than an ongoing leadership responsibility.


This compression is rarely intentional. It reflects the pace of AI advancement and competing demands on leaders’ time. However, when organizational and team alignment do not keep pace with deployment, adoption becomes inconsistent—even if the technology is performing well.

Why Organizational Alignment Matters More as AI Scales

Unlike traditional enterprise systems designed to standardize processes, AI introduces variability. Employees must make ongoing judgments about how and when to use it.

Common questions include:


  • When are AI-generated outputs sufficient?

  • When should human judgment override AI recommendations?

  • How is AI-assisted work evaluated for quality and accountability?

These questions are not resolved through documentation alone. They are shaped by leadership direction, normalization, and reinforcement.

McKinsey’s research offers a concrete example of the kind of operational alignment that differentiates high performers. Compared with peers, AI high performers are more likely to report that their organizations have defined processes to determine how and when model outputs need human validation to ensure accuracy. Having governance protocols in place is one of the factors McKinsey identifies as distinguishing high performers.


Without alignment around expectations and decision standards, employees tend to either over-rely on AI to reduce perceived risk or underuse it to avoid scrutiny. Neither pattern supports sustained performance.


Leadership Engagement Beyond Approval

It would be inaccurate to suggest leadership is absent from AI adoption. Senior leaders are typically deeply involved in:


  • Strategic intent and investment decisions

  • Governance, risk, and compliance considerations

  • Performance metrics and outcomes


What differentiates higher-performing organizations is the continuity of leadership engagement after deployment.


Their involvement should reduce ambiguity about expectations and help translate deployment into consistent adoption across teams.


In contrast, when leadership engagement tapers after launch, teams are left to interpret how AI should be used. Over time, adoption fragments, not likely due to resistance, but due to misalignment.


The Organizational Cost of Misalignment

When organizational alignment does not keep pace with AI deployment, several patterns tend to emerge:


  • Wide variation in AI use across teams

  • Uncertainty about quality and accountability standards

  • Increased mental load and decision fatigue as employees self-navigate expectations

  • Difficulty measuring impact consistently

  • Gradual accumulation of operational and reputational risk

These outcomes are not failures of AI strategy. They reflect the reality that technology adoption alone does not ensure coordinated behavior at scale.


What Aligned AI Adoption Looks Like in Practice

Organizations that scale AI effectively treat adoption as an ongoing alignment and change management effort, not a phase to complete.


Common characteristics include:


Alignment Embedded in the Technology Roadmap

Organizational considerations—roles, decision rights, and workflows—are addressed alongside technical deployment.


Clear and Reinforced Expectations

Leaders clarify when AI should be used, how outputs are reviewed, and where accountability remains human.


Capability Building Focused on Judgment

Training emphasizes decision-making, critical thinking, and ethical application, not only tool proficiency.


Visible Leadership Reinforcement

Senior leaders continue to engage, model behavior, and recalibrate expectations as tools and use cases evolve.


This approach does not slow adoption. It makes adoption resilient.


Why This Matters Now

AI capabilities are advancing faster than organizational norms can adapt. Leaders are balancing speed, competitiveness, and workforce impact simultaneously.


As AI scales, misalignment becomes more costly. What works in small pilots or isolated teams often breaks down at enterprise level without sustained leadership engagement and alignment. Organizations that treat AI adoption as both a technology initiative and an organizational change effort are better positioned to translate investment into durable performance improvement.


Reframing AI Adoption at Scale

AI adoption at scale requires more than technology. It requires leadership engagement that extends beyond approval and organizational alignment and change that evolves as AI becomes embedded in daily work.


The organizations reporting meaningful financial impact from AI are not distinguished by tools alone. They are distinguished by how leaders stay engaged, reinforce expectations, and guide how AI is used in practice.


The relevant question for leaders is no longer “Have we deployed AI?” It is “Have we aligned our organization to use AI consistently and responsibly at scale?”


If you’re moving from experimenting with AI to scaling it, learn more about "Leading Teams Through AI Adoption: From Experimenting to Scale" a working session that helps leaders build a practical roadmap through the people and change phases of AI adoption.



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