The People Side of AI: Trust, Governance, and Getting Adoption Right

By Megan Valesano on May 6, 2026

Telecom field technician on tablet

The People Side of AI: Trust, Governance, and Getting Adoption Right
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Key takeaways

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    Utilization does not mean adoption - People can use a tool without unlocking its value
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    Trust is a business metric - Low trust drives attrition, disengagement, and failed initiatives
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    Frontline resistance is often rational - Concerns usually stem from misunderstanding or misaligned incentives
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    AI introduces new risks - Governance, privacy, and accountability must be built in from day one

When organizations roll out new technology, especially AI, the assumption is often simple: if people use it, the job is done. In reality, that's rarely true.

In a recent Frontline Innovators Podcast conversation, AI governance leader Nancy Green highlighted a critical gap many organizations overlook: the difference between using a tool and actually adopting it.

That gap is where most AI initiatives succeed or fail.

 

How to Know Your AI Adoption Isn't Working

When adoption falls short, the symptoms show up quickly, if you know where to look.

People Use the Tool, But Only at a Surface Level

Employees may log in, complete required steps, and technically "use" the system. But they aren't leveraging its full capabilities. They're doing the minimum needed to get through their day.

A simple analogy: giving someone a smartphone and watching them only use it to make calls. The tool is there, but the value isn't realized.

Trust Starts to Erode

Poorly managed change has a direct impact on trust. When employees feel blindsided, excluded, or unsupported, they disengage. Some leave. Others stay, but contribute less, speak up less, and stop investing energy in improvement.

That loss compounds over time. Each new initiative becomes harder to implement than the last.

Feedback Disappears

When trust is low, communication breaks down and what looks like "alignment" is often silence.

Employees stop sharing concerns, even when those concerns are valid. Leaders lose access to the very insights that could make the rollout successful.

 

Why Adoption Breaks Down

The failure isn't usually the technology. It's how the change is handled.

The Wrong People Aren't in the Room

Frontline leaders and workers often aren't involved early enough. That means critical realities get missed: timing conflicts, workflow constraints, union considerations, or operational nuances that only show up in the field. By the time those issues surface, trust has already taken a hit.

Communication Comes Too Late, or Feels Misleading

Surprises are one of the fastest ways to lose trust. If managers hear about changes at the same time as their teams, it damages credibility immediately.

Leaders may describe a change as an "efficiency improvement," while frontline workers experience it as slower or more complex. Even if the organization benefits overall, that disconnect feels like dishonesty.

Incentives Work Against the Change

One of the most overlooked issues is misaligned incentives. If employees are measured on speed, but a new process takes longer (even if it improves quality), they're being penalized for doing the right thing.

In those cases, adoption failure isn't resistance. It's rational behavior.

 

3 Ways to Drive Real Adoption

To move from surface-level usage to meaningful adoption, leaders need a more intentional approach.

1. Communicate Early, Even If You Don't Have All the Answers

Waiting for perfect clarity before communicating creates risk. Instead, share what you know early and create channels for feedback. Frontline leaders can flag constraints, timing issues, and operational realities that would otherwise be missed.

Early communication builds trust, and better plans.

2. Use Change Champions to Scale Trust

In large organizations, one-on-one conversations don't scale. That's where change champions come in. These are respected individuals within teams who can translate messaging, gather feedback, and influence adoption locally.

Importantly, they shouldn't all be enthusiasts. Some of the most valuable champions are skeptics: the ones asking hard questions and surfacing real concerns.

3. Shift from Training to Enablement

Traditional training often overloads people with information they won't retain. A better approach is "minimum viable proficiency": give employees just enough to get started, then provide real-time support as they encounter issues.

Short, hands-on learning paired with accessible help in the moment is far more effective than long, upfront training sessions.

 

What Makes AI Adoption Different

AI introduces a new layer of complexity that traditional technology rollouts didn't face.

Higher Perceived Risk

Employees aren't just learning a new tool. They're evaluating its impact on their job. Common concerns include job security, data privacy, and accuracy of outputs. These concerns need to be addressed directly and transparently.

Changing the Nature of Work

In some cases, AI automates the parts of a job people enjoy most. Even if roles aren't eliminated, they're reshaped. That shift can create subtle resistance that's easy to overlook.

 

The Role of Governance in AI Adoption

Unlike many past technologies, AI requires strong governance from the start. That means clearly defined roles and responsibilities, involvement from legal, risk, and compliance teams, and ongoing oversight rather than just project-based decisions.

Without this structure, adoption may happen, but it won't happen safely or sustainably.

 

FAQs from Nancy Green on AI Adoption

What's the difference between utilization and adoption?

Utilization is using a tool. Adoption is integrating it into workflows to create real value.

Why does trust matter in AI adoption?

Low trust leads to disengagement, poor feedback loops, and higher turnover, all of which undermine the success of any AI initiative.

How can large organizations personalize change at scale?

By using change champions who can adapt messaging and gather feedback within their specific teams, making the change feel relevant and credible at every level.

What makes AI adoption harder than other technologies?

Higher perceived risks around job security and data privacy, potential impact on job roles, and the need for strong governance from day one.

Who is responsible for decisions made with AI?

Humans. AI is a tool, but accountability remains with the user. Governance structures must make that accountability clear from the start.

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