There’s a shift happening inside organizations that most leaders haven’t fully named yet.
It’s not just that AI is being adopted. It’s that the definition of “good work” is changing in real time. What used to signal quality, effort, and expertise is being compressed, reshaped, and in some cases replaced by something faster and less visible. And most teams haven’t stopped to define what that means.
The Standard Is Moving (Whether You Set It or Not)
When AI enters a workflow, expectations shift immediately.
Tasks that used to take hours are now expected in minutes, and that changes how work is perceived. First drafts are no longer the result of thinking through a problem; they’re generated instantly and often accepted as a starting point without much challenge. The visible effort behind the work starts to disappear, and with it, the cues people used to rely on to evaluate quality. Over time, speed becomes the signal people trust, even when it shouldn’t be.
This is where “good enough” starts to take over.
And “good enough” moves fast.
Why This Becomes an HR Issue Quickly
This is not just a productivity shift.
It’s a shift in perception that affects how people are evaluated and how they evaluate themselves. HR feels this first because performance, feedback, and expectations all rely on a shared understanding of what “good” looks like. When that definition changes without explicit discussion, confusion arises quickly and spreads across teams. That confusion doesn’t stay contained to one function; it shows up everywhere.
As Catie Maillard, Global VP of People, pointed out: “We don’t even know what skill set we need… we’re figuring out what actually matters in real time.”
What Teams Are Doing Without Realizing It
In the absence of clear direction, teams start creating their own standards. Some over-rely on AI and assume speed equals effectiveness, while others pull back and try to prove value through effort instead of outcomes. Most land somewhere in between, adjusting based on what feels safe or expected in the moment. None of that is aligned, and over time, that misalignment creates friction that’s hard to pinpoint.
It also starts to take a toll on people. When standards are unclear, employees can feel like nothing they do is quite right. They are putting in effort, but if that effort is not aligned with what leaders actually expect, it does not land. That disconnect creates a quiet kind of burnout. Not from lack of effort, but from lack of clarity.
And misalignment at that level doesn’t show up immediately in metrics. It shows up in inconsistency, frustration, and a slow drift in how work gets evaluated.
This dynamic is reflected in a recent Harvard Business Review article, “Leaders Feel Their Agency Eroding—and They’re Starting to Withdraw,” which explores how unclear expectations and rising complexity are reshaping leadership behavior. The piece argues that when leaders can no longer clearly define what good looks like, especially in environments influenced by AI, their sense of agency begins to erode. Instead of driving direction, they start to hesitate, delay decisions, and withdraw from active leadership. That withdrawal creates a feedback loop. The less leaders intervene, the more misalignment spreads, making it even harder to step back in and reestablish clarity. What shows up as inconsistency across teams is often a signal of something deeper happening at the leadership level.
What HR Leaders Should Pay Attention To
This isn’t something you solve with a policy; this is something you solve with culture.
It shows up in everyday behavior that often goes unnoticed at first. How managers give feedback, what gets praised in team meetings, how we talk about failed projects, and how work is evaluated when AI is involved all start to shift. Those signals become the new standard, whether intentional or not, and employees quickly adjust to match them.
As Catie shared: “We’re not even sure what technical skills we will need in 6 months, but we know the ability to think critically and prioritize is rising to the top”.
A Better Way to Approach This
Instead of trying to control AI usage, focus on redefining roles & outcomes.
Ask what strong work actually looks like now and where human judgment matters most. Clarify what AI should accelerate and where it should not be used, especially in decisions that require nuance. These conversations don’t need to be perfect, and often, as the tools themselves update and roles may shift.
This is not about slowing teams down.
It’s about making sure they’re moving in the right direction.
Why This Matters More Than It Seems
If you don’t reset expectations, the shift doesn’t happen all at once. It happens gradually with errors and confusion, and then all at once.
High performers start to question whether their work is still differentiated, and that uncertainty begins to impact motivation over time. At the same time, lower-quality work starts passing more easily because it meets a surface-level standard, even if it lacks depth. That combination creates a quiet erosion of quality that’s difficult to spot until it’s already embedded in how work gets done.
As Catie Maillard, Global VP of People, put it: “AI makes us feel confident because we have an answer, but we don’t always understand what that answer is.”
This is one of the earliest indicators that AI is changing your workforce, not just your workflows.
Where This Conversation Needs to Go Next
Most organizations are reacting to AI.
Very few are defining what it actually means for their people and how work should evolve as a result. That gap creates risk, but it also presents an opportunity for HR to lead in ways that shape long-term outcomes. The organizations that step into this early will have a clear advantage over those that wait.
If you’re trying to figure out how to reset expectations and align your teams, this is exactly where strategic guidance matters.
This is the work Donna Medeiros leads every day. She brings over 30 years of experience advising enterprise and public-sector leaders on moving from experimentation to execution, including time in CDAO leadership roles at Gartner. Her approach is practical and grounded in how organizations actually operate. She does not start with tools. She starts with the decisions your teams are already trying to make.
What makes this conversation valuable is that it is not theoretical. It is focused on your current state, your constraints, and what is realistically achievable within your organization. For many HR leaders, this is the first time they have a space to talk through how AI is changing performance, expectations, and workforce dynamics in ways that are specific to them.
If you’re seeing these shifts but don’t yet have a clear way to respond, it is worth having the conversation.
Connect with Donna Medeiros, VP of AI and Data Advisory, to talk through how this is showing up in your organization and what to do next.
Frequently Asked Questions
AI is raising expectations around speed while lowering visibility into effort. This creates a shift in which faster output is often perceived as better, even when depth and critical thinking may be lacking.
AI is also making it easier to make mistakes. From presenting confidently incorrect answers from a chatbot to missing the visual notification that a call is being recorded, the likelihood of a mistake when using AI tools is high. You can’t innovate without a few failures, though most companies haven’t yet revisited their failure tolerance levels.
