AI is changing work. That is a given.
The much harder question for leaders is: What comes next?
There is no single answer. What comes next for a global financial institution may look very different from what comes next for a young technology company. It will vary based on your institution’s size, complexity, industry, and culture. And within the same organization, it will vary based on the readiness of your workforce and your leaders.
That nuance matters.
At Seramount, we’ve been looking closely at what separates organizations that are simply using AI from those beginning to get meaningful value from it. Our latest research brings together market insights, interviews with senior workplace leaders, and more than 10,000 data points from Seramount’s Employee Voice Sessions.
While not explicitly about AI, those 10,000-plus data points tell us something equally important: how employees experience their workplaces today—from leadership and workload to advancement, trust, and whether they feel safe speaking up. Together, they give us a window into whether organizations have the conditions employees and leaders need to navigate the kind of change AI is bringing.
And the broader research reveals a striking gap: 88 percent of organizations report regular AI use in at least one business function, but only 30 percent are redesigning key processes around it.
Using AI and transforming work are two very different things.
Start by knowing your organization
I often say that one of the most important skills any employee can develop is self-awareness. Organizations need that same self-awareness.
Call it institutional self-awareness.
Before deciding where AI should take you, you must understand where you are. How ready are your teams? Which leaders are already experimenting and moving forward? Where are people struggling? Where is trust strong? Where are the capability gaps? Which parts of the organization can move quickly, and which need more support?
Our own employee data reinforces the importance of starting there. Only 32 percent of employees say they understand what it takes to succeed in their organizations. Now consider what happens when AI begins changing the tasks people perform, the skills they need, how their work is evaluated, and where new opportunities emerge.
People need a map. And leaders need one too.
That doesn’t mean every function or team needs to move at exactly the same pace. One part of your organization may be ready to move faster while another is not. Leaders need to understand those differences and determine what each part of the organization needs to move forward.
Your plan must change as AI changes
There is another complication: Organizations move more slowly than technology does. You can create a thoughtful plan around today’s AI, spend six months preparing to implement it, and discover that the technology has already moved again.
That means some of the most important skills we need right now have very little to do with technology. We need the willingness to experiment, learn from what happens, and change course when something isn’t working. You need a Plan B. Maybe even a Plan C.
And leaders need to provide direction without pretending we have all the answers. We don’t. We can, however, be honest about what we know, where we still have questions, and what we are learning along the way.
Decide what AI is actually for
We spend a lot of time asking: How do we get more value from AI?
I would turn that question around: What do we want AI to make possible?
If AI allows someone to complete a task in two hours instead of six, what happens to those four hours? Do we simply give that person more work?
Or do we create more time with customers? More space for innovation? More opportunities to learn? More time to collaborate, solve a difficult problem, or make a better decision?
This isn’t an abstract question. According to our data, 34 percent of employees already cite excessive workload as a career difficulty. And broader research shows that even when AI saves employees significant amounts of time, organizations often give them little guidance about what they should do with that time.
Leaders need to decide what saved time is for before short-term demands consume it.
AI can save time. It cannot tell us what people should stop doing, where human judgment matters most, what good work should look like, or who should gain access to the new opportunities AI creates. Those are choices we make.
Don’t underestimate collaboration
There is another human capability I am watching very closely: collaboration.
In fact, as AI becomes more embedded in our work, I believe collaboration may become one of the most valuable skills we can develop.
I see collaboration in real time. When people with different responsibilities and perspectives come together around the same problem, they can reach ideas and solutions that none of them would have realized independently. When teams do not collaborate, we lose something.
Our research raises a similar concern: AI can make individuals more productive while potentially reducing some of the human interactions through which people learn, build trust, and grow.
The answer isn’t to resist the technology. It’s to make sure we preserve those interactions while using technology to make our collective work better.
The more capable AI becomes, the more important our ability to work well with one another may become.
Trust can’t wait
And none of this happens without trust.
Organizations that have already built strong trust with employees have an advantage. People are more willing to experiment, ask questions, acknowledge when something isn’t working, and believe leaders when they say, “We don’t know yet, but we’re going to figure this out.”
But if that trust isn’t there, you can’t stop and say, “We’ll build trust first and come back to AI later.” You have to do both at once.
Move the organization forward while building trust. Listen to employees, explain what is changing, invite questions, and make it safe for people to tell you when something doesn’t look right.
Today, only 41 percent of employees in our data say they feel comfortable taking risks. That should get our attention.
The employee closest to the work may be the first to notice that an AI-generated answer is wrong, that a new process is creating more work instead of less, or that an opportunity isn’t reaching everyone equally. We need those employees to speak up.
We still get to choose
Our research points to five workplace conditions that leaders need to get right: clarity about how work and opportunity are changing, thoughtful redesign of work, access to new opportunities, manager capability to translate change, and a culture where people can question and speak up.
Why do these matter? Because this is where an AI strategy becomes real.
Employees don’t experience transformation through a strategy document. They experience it through their workload, the expectations their manager sets, the opportunities they are or aren’t given, and whether they can say “This isn’t working” without fear.
Get those conditions right, and AI can create better work, stronger teams, and new opportunities for people. Get them wrong, and we risk using powerful new technology to make old workplace problems move faster.
That is why I believe this moment is about much more than AI. It’s about the choices we make as leaders. AI will continue to evolve faster than any of us can predict. That makes leadership more important, not less.
What do we want our people to have more time to do? Where should human judgment become more important, not less? Who will have access to the opportunities AI creates? What kind of culture will allow people to experiment, collaborate, and challenge something when it doesn’t look right?
And ultimately: What kind of organization do we want to become?
AI will keep changing work. We shouldn’t simply allow work to change around us.
We must decide what we want AI to make possible—and what comes next.
