Organizations are investing heavily in artificial intelligence, automation and analytics. Yet widespread adoption has not translated into widespread enterprise-level impact. Why?
McKinsey’s 2026 State of AI research found that nearly nine in ten respondents report regular AI use in at least one business function. Yet only 37% attribute any positive impact on earnings before interest and taxes to their organizations’ use of AI, and just 6% qualify as AI high performers.
The gap between AI adoption and organizational impact is not merely a technology or implementation problem. It is also a leadership influence problem.
In my consulting work, I repeatedly see leaders being held accountable for realizing the benefits of their organizations’ AI investments without directly controlling the technology, implementation timeline or decisions affecting their teams. They face an accountability-without-authority challenge: responsibility for results without control over many of the factors that determine success.
Consider a functional leader whose team must adopt an AI platform selected by enterprise technology leaders. That leader did not choose the tool, establish the timeline or determine how workflows would change. Yet the leader is accountable if employees resist adoption, customer service declines or anticipated productivity gains fail to materialize.
Closing the gap between AI adoption and organizational impact requires more than communicating decisions and managing implementation plans. It requires leaders who can influence both the people shaping the change and those being asked to adopt it.
The importance of this capability is reflected in The World Economic Forum’s Future of Jobs Report 2025, which identifies leadership and social influence among the core skills employers consider most essential.
Why AI-Driven Change Requires Influence at All Levels
Leadership influence is the ability to earn trust, understand what people and situations require, and move stakeholders from competing concerns toward shared action.
During AI-driven change, leaders must influence upward and across organizational boundaries to help deliver intended results. They must also influence their teams by creating trust, clarity and ownership that people need to adopt new ways of working.
To turn AI-driven change into organizational results, leaders must demonstrate three essential influencing capabilities:
- Build a foundation of trust that creates the opportunity to influence
- Adapt to what people and situations require
- Navigate high-stakes conversations without losing trust and momentum
1. Build a Foundation of Trust
Titles may provide access to a conversation, but actions determine whether others trust a leader’s judgment. Leaders earn that trust through three foundational disciplines.
Establish credibility. Technical knowledge and strong performance may open the door to influence, but leaders sustain credibility by delivering quality work, honoring commitments and exercising sound judgment. These leaders have the courage and humility to acknowledge what they do not know rather than pretending to have certainty they do not possess. The baseline for building trust is doing what you say you will do.
Find common ground and build shared goals. Influence begins with understanding what matters to others. Leaders ask questions such as, “What does success look like for you?” and listen until people feel heard and understood. They look for opportunities to connect competing priorities to a shared purpose, helping stakeholders move from defending individual positions toward pursuing outcomes that benefit the broader organization.
Build strong professional relationships. AI-driven change rarely occurs within a single team or function. Leaders must collaborate across technology, operations, human resources, legal, risk and other organizational boundaries. Strong professional relationships make it easier to exchange information, surface concerns, resolve disagreements and coordinate action when formal authority is limited.
Trust does not guarantee effective collaboration, but it does create the conditions in which meaningful influence becomes possible.
2. Adapt to What People and Situations Require
AI-driven change creates disruption, complexity and uncertainty. Yet many AI initiatives struggle not because leaders fail to act, but because they misread the situation or the people involved.
Resistance may reflect concerns about job security rather than reluctance to learn. Slow execution may result from competing priorities, unclear decision rights or poorly redesigned workflows. What appears to be an execution problem is often a diagnostic problem.
Effective leadership agility begins with Situational Awareness because the quality of a leader’s response depends on the quality of their understanding. When confronting complex technology or implementation challenges, leaders ask:
- What assumptions am I making?
- What else could be true?
- What matters most to the stakeholders involved?
- How might this situation make sense from their perspectives?
Situational Awareness helps leaders understand stakeholders’ interests, organizational constraints, competing priorities, risks and emotional dynamics. Behavioral Agility enables leaders to use that understanding to adapt their communication, decisions and engagement. For example, influencing a technology leader focused on implementation speed may require a different approach from engaging employees concerned about how AI will affect their roles.
Behavioral Agility without Situational Awareness creates action without wisdom.
Situational Awareness without Behavioral Agility creates insight without impact.
Together, these capabilities require leaders to answer two essential questions:
What does this situation require from me?
How should I adapt to support the best outcome for the team and organization?
Routine decisions and familiar situations rarely require extensive analysis. But as decisions become more important, complex, uncertain, emotional, stakeholder-dependent or difficult to reverse, leaders must deepen their assessment. This enables them to make better decisions, build stronger alignment and influence people more effectively through AI-driven change.
3. Navigate High-Stakes Conversations Without Losing Trust and Momentum
High-stakes conversations are interactions in which perspectives differ, emotions run high and the outcome matters. AI-driven change requires leaders to engage in effective high-stakes conversations across organizational boundaries and levels of authority. Stakeholders may agree that change is necessary while disagreeing about speed, risk, capacity, governance and accountability.
Teams need equally candid conversations to question assumptions, surface obstacles, redefine responsibilities and decide which practices should change. Leaders may be right about a risk or recommendation and still get the conversation wrong through poor timing, harsh delivery or insufficient curiosity.
Effective collaboration requires a fundamental shift from “me” to “we.”
- From advocating for my position to aligning on the real issue.
- From relying on my perspective to understanding the broader context.
- From providing answers to co-creating solutions.
- From assigning responsibility to establishing shared ownership.
I developed the ACES Model to provide a practical framework for making these shifts with organizational partners and teams. It moves leaders from competing perspectives toward shared understanding, workable solutions and collective ownership.
ACES Model for High-Stakes Conversations
Tony Gambill
Align on the issue: Get clear on the real opportunity, problem or decision before debating solutions.
Contextual awareness: Develop a shared understanding of stakeholders, constraints, risks and implications.
Explore solutions: Co-create possibilities that integrate technological capability with human and organizational needs.
Shared action: Establish clear ownership, decision rights, next steps and accountability.
Each step builds on the one before it. Leaders can use ACES across organizational boundaries to shape better decisions and within their teams to translate change into clear responsibilities and coordinated action. In both settings, it supports progress while preserving the relationships required for the next change.
Leadership Influence Turns AI Adoption into Results
AI-driven change increasingly crosses the organizational boundaries where formal authority ends. Turning technological capability into organizational results depends on leadership influence.
Trust creates the opportunity to influence. Situational Awareness and Behavioral Agility enable leaders to understand what the situation requires and adapt their approach. ACES turns that understanding into shared decisions, coordinated action and relationships strong enough to navigate the next change.
Leaders must influence in both directions: shaping better organizational decisions and helping their teams turn disruption into stronger performance. Technology may initiate the change, but leadership influence determines whether people turn it into progress.
