By Jen Fisher and Paula Davis
Tension For too long, resilience has been framed as a problem to be solved by strategies of resilience or resilience – but these approaches often miss something important: people need hope. At one time artificial intelligence (AI) is reshaping the way we work, many conversations are about productivity earnings, job disruption and speed. Yet AI may offer another, less-discussed opportunity: the chance to reduce unnecessary friction, restore a sense of possibility, and help people become more capable and supportive of themselves in the face of growing demands. If the burn is a chronic cause stress and helplessness, then hope can be one of the most important antidotes.
The AI Paradox
While many workers who use AI report increased productivity, many also experience increased stress because free time is lost. 2025 year report According to research institute Upwork, the most productive AI users are more likely to report burnout and self-discharge — and are almost twice as likely to consider leaving. In the same study, 90 percent of workers described AI as a “colleague,” and more than half said they trust AI more than their teammates.
Eight months apart learning found something similar. When one tech company introduced AI tools (without requiring them to use them), employees worked faster, longer, and took on more responsibilities. What appears to be an increase in productivity has also created a shift in scope, a “slump of work,” which increases cognitive load, constant pressure to produce more, and reduced recovery time. Over time, leaders have struggled to distinguish true performance from unsustainable intensity, which can become a precursor to burnout.
A manager at a large consulting firm describes his experience this way: “AI is my fastest teammate and my worst boss.It can do anything I ask it to do. wisdom What’s worth asking about.” It needs to use AI tools with little guidance, clear expectations, and no organizational vision for what good AI-enabled work looks like. It’s this gap that comes from confusion at work: more product, less thinking about which outcome is important. And if no one stops to ask if the outcome is good — am I hopeless here? Or Am I approving what the machine has already decided?
For decades research identify that there are three aspects of fatigue: chronic fatigue, chronic cynicismand inefficiency – “Why bother?” the mentality that occurs when people can no longer see how their work makes a difference.
Most organizations focus on fatigue. But in the age of AI, inefficiency can be a more dangerous signal. Employees ask, “What value do I add if the system can do it faster than I can?” they are not just overflowing. They question their own importance. And when relevance is lost, agency follows.
Agency is the engine of hope.
What hope looks like now
Psychologist CR Snyder defined hope as more optimism. It relies on three components: meaningful goalsagency (the belief that you can act to achieve those goals) and pathways (the ability to see multiple routes forward). The problem is that hope is often confused with optimism or desire, both of which are passive states. When you hope, you both have high hopes for the future and a realistic view of the obstacles you must overcome to reach your goal. Phrases like “Don’t lose hope” or “Think positive” are often well-intentioned but really masquerade as hope and can undermine your efforts.
AI doesn’t eliminate the need for human agency—it changes what agency looks like. Shift “Can I do this task?” “Can I direct this technology to meaningful results?” In addition, ways of thinking are important. AI should be the only way to do great work among many.
When leaders send the message (directly or indirectly) “You’re being replaced” during AI change, it undermines agency by producing tools without employee input or measuring performance without measuring impact.
When leaders create AI, they create agency adoption When they invite teams to decide where AI can add value and where human judgment remains important, and when they publicly acknowledge their learning curve with new tools, not as a performance mandate, but as an ongoing experience.
Hope grows when people believe they still have influence within the system.
Trust factor
As organizations integrate AI, some leaders are finding that team dynamics can change not because people resist the technology, but because trust has changed.
When a human teammate makes a mistake, teams can ask questions, understand the context, and learn together. AI is not involved in this mutual learning process. It introduces what researchers call “confidence uncertainty.”
The danger is that people will stop asking their colleagues for help, not because they are not good, but because AI is faster and no one will feel stupid for asking. And suddenly, without anyone noticing, the team stopped talking.
Hope requires psychological safety. People need permission to say, “I don’t know how to use this yet,” without being afraid career consequences.
Harvard Business School professor Amy Edmondson—whose seminal work on psychological safety has shaped how organizations think about learning and risk—and co-author Jayshree Seth argue that AI integration should be seen as a learning problem rather than just learning. This means leaders can:
- Using Frame AI as an experience, not an expectation. Position placement as ongoing, hands-on learning rather than a performance mandate.
- Modeling error by sharing their AI errors. When leaders recognize their own learning curve, they normalize growth fear.
- Distinguish between smart failures and avoidable mistakes. Low-risk experiences that lead to insight should be celebrated, not punished.
- Create a space to discuss AI challenges – not just AI wins. Teams need a place to name what isn’t working.
Without psychological safety, agency remains silent.
Monday morning activities
Hope is not formed through speech. It is built through structure and good team practices.
There are three things you can do this week:
- Set clear AI norms. Determine when to use AI and when to use human judgment. Before moving on to automation, ask: “What are three more ways we can learn?”
- Change the one-on-one routine around the agency. Beyond productivity metrics: “What meaningful impact are you making now? Where should we have more control over how we use AI?” ask that.
- Share your training course. Describe how the AI didn’t work as expected and what you learned. When leaders model curiosity instead of certainty, they normalize growth instead of fear.
Hope is a real competitive advantage
Hope is not naive optimism about the potential of AI. It’s a disciplined practice of protecting the agency and maintaining the roads—even as technology advances. Organizations that thrive in the age of AI won’t be the ones that automate the fastest. They will be the ones who help people see how important they are within the system.
