Are we asking people to embrace AI without acknowledging what they stand to lose?
As organizations embrace AI, what could people lose? Our community explores the future impact on human skills, trust and expertise.
AI promises greater productivity, faster decisions and new possibilities for organizations. But as businesses accelerate adoption, are we paying enough attention to what people might lose along the way? We asked members of the Agile Business Consortium community to share their perspectives.
The conversation around artificial intelligence is dominated by possibility. We hear about the hours it could save, the processes it could automate and the opportunities it could unlock. Organizations are encouraging employees to experiment, adopt new tools and rethink how they work.
But beneath the enthusiasm sits a more uncomfortable question: “What are we asking people to give up in return?”
For some, the concern is job security. For others, it is the gradual erosion of expertise, independence, human connection or the satisfaction that comes from doing meaningful work. And for organizations, there is a risk that pursuing efficiency today could undermine the very capabilities they will need tomorrow.
We invited members of our community to explore the question: “Are we asking people to embrace AI without acknowledging what they stand to lose?”
Their responses reveal something important. The challenge isn’t simply whether organizations should adopt AI. It’s whether they are asking the right questions about its purpose, its value and its impact on people.
Are we limiting AI’s potential by focusing on efficiency?
When organizations introduce AI, the conversation often begins with productivity. Which tasks can be automated? How much time could be saved? Where might costs be reduced?
But according to Jon Ward, this focus may be preventing organizations from recognizing AI’s greatest opportunities.
“The limit on how much AI can improve organizational performance is not the tool. It is our imagination.”
Jon Ward, Agile Business Consortium Ambassador
Jon argues that many organizations are simply using AI to automate existing activities rather than fundamentally rethinking what they could achieve.
He offers examples of previously impractical possibilities: a retail bank providing every customer with personalized attention, a leadership team reviewing its strategy monthly against fresh market information, or a service organization analyzing every customer complaint rather than relying on samples.
These aren’t necessarily new ambitions. They’re things organizations may have wanted to do for years but couldn’t justify because of the time, resources or expertise required.
AI changes that equation.
Instead of asking which tasks can be automated, Jon suggests organizations should be asking: “What could we now do that our competitors still think is impossible?”
It’s an important distinction. One approach focuses on doing the same things with fewer resources. The other focuses on creating entirely new value.
Crucially, Jon doesn’t suggest that people become irrelevant in this future. Quite the opposite. Realizing these opportunities requires people who understand customers, exercise judgment, experiment and translate technological capabilities into meaningful outcomes.
This is where enterprise agility becomes particularly important. Organizations capable of learning and adapting quickly will be better positioned to discover opportunities that more rigid organizations overlook.
But unlocking that potential requires more than access to technology. It requires a clear understanding of what organizations are trying to achieve.
Do organizations really understand what they stand to gain?
Philippe Guenet challenges the conversation from another direction.
“To be honest, I am not sure that the people asking are really clear what there is to gain either.”
Philippe Guenet, Agile Business Consortium Ambassador
His observation raises an interesting question. If organizations cannot clearly articulate the benefits of AI adoption, how can they expect employees to embrace the changes it brings?
Philippe distinguishes between specific, identifiable applications of AI and the broader expectation that everyone should start incorporating it into their work.
In areas such as software development, quality assurance or customer service automation, the opportunities can be relatively tangible. Organizations can introduce solutions incrementally, assess their effectiveness and support employees through the resulting changes.
The challenge becomes more complicated when AI adoption is encouraged without a clear purpose.
Philippe highlights the absurdity of using AI to elaborate an email, only for its recipient to use AI to summarize it.
Technology has been used twice, but has anything meaningful improved?
It’s a useful illustration of the difference between activity and value.
Organizations may be measuring how frequently employees use AI without understanding whether that usage improves customer outcomes, increases organizational capability or creates genuine business benefits.
Philippe also points to a structural challenge. Many organizations are designed around large planning and delivery cycles, while discovering valuable applications of AI requires experimentation, frequent feedback and continuous adaptation.
Rather than simply counting hours saved, he suggests examining how organizational capacity is distributed. Are people spending less time dealing with failures and routine operational work, and more time developing improvements, delivering valuable features and investing in future opportunities?
This shifts the conversation away from productivity as an end in itself.
After all, saving time only creates value if organizations make thoughtful decisions about what happens to that time.
What happens when efficiency comes at the expense of expertise?
While the potential for greater organizational performance is significant, Ilana Sprongl raises a concern that is less immediately visible.
What happens when AI begins performing the work through which people traditionally develop expertise?
“I am less concerned about particular tasks disappearing than I am about losing the experience through which people develop judgment, build expertise, and learn to take real ownership of their work.”
Illana Sprongl, Agile Business Consortium Ambassador
Expertise doesn’t emerge simply from having access to the right answers. It develops through experience: navigating uncertainty, making mistakes, solving difficult problems and learning to recognize when something isn’t quite right.
If AI increasingly handles these activities, organizations may achieve faster results while unintentionally weakening the capabilities that make those results trustworthy.
Ilana is particularly concerned about the next generation of professionals.
Experienced employees may already possess the knowledge needed to question an AI-generated recommendation. But what happens to those who never have the opportunity to develop that knowledge themselves?
Who will recognize when an apparently convincing answer is wrong? Who will challenge flawed assumptions or respond when an unfamiliar situation falls outside the patterns a system has learned?
These questions expose a potential tension between short-term productivity and long-term resilience.
Ilana also highlights the relationship between authority and accountability. AI may increasingly influence analysis, decisions and execution, but people and organizations remain responsible for the consequences.
If individuals are held accountable for outcomes that they have less ability to understand or shape, existing organizational tensions could become significantly worse.
For leaders, the implication is clear. AI adoption cannot be treated simply as a technology implementation.
It changes how work is performed, how people learn, where decisions are made and how responsibility is distributed.
Organizations therefore need to consider not only which activities AI can perform, but which activities people still need to practise to develop and maintain essential capabilities.
What about the things we cannot easily measure?
Not everything people stand to lose can be expressed in productivity metrics or organizational performance indicators.
Aga Gajownik brings a deeply human perspective to the discussion, questioning the less visible trade-offs involved in AI adoption.
“Sometimes it is simply efficiency at the expense of being present. Sometimes it is cognitive offloading, which may change how we process information and think.”
Aga Gajownik, Agile Business Consortium Ambassador
She shares an experience involving a client whose photograph was altered using AI by event organizers. The resulting image no longer resembled her, leaving her feeling violated and disrespected.
What made the situation particularly troubling was how casually the decision had been made.
The organizers had focused on solving a practical problem without considering whether the person concerned should have been consulted.
It’s a small example of a much bigger issue: the availability of a technological solution doesn’t automatically make its use appropriate.
Aga also questions the growing tendency to record and transcribe every meeting.
While AI-generated notes can be useful, she sometimes deliberately chooses not to use them. For her, being present, listening carefully and engaging with others are valuable parts of the conversation itself.
If we begin treating every interaction primarily as information to be captured and processed, what happens to the quality of our relationships?
Yet Aga’s perspective is far from anti-AI.
As someone who is neurodivergent, she recognizes its transformative potential in supporting executive function, reducing mental load and helping her explore ideas.
The distinction lies in how the technology is used.
“I am doing the thinking and the technology is helping me execute. I do not want to outsource the thinking itself.”
Aga Gajownik, Agile Business Consortium Ambassador
This captures an important principle. AI can enhance human capability without necessarily replacing human agency.
But that outcome requires conscious choices about where technology supports us and where it begins to diminish something we value.
The real challenge is designing change around people
Across these perspectives, a common theme emerges: AI adoption is not simply about introducing new tools. It is about changing the relationship between people, work and organizational value.
Jon encourages organizations to imagine possibilities beyond automation. Philippe challenges them to define and measure meaningful benefits. Ilana asks leaders to protect the development of expertise and judgment. Aga reminds us that efficiency, however attractive, should not automatically take precedence over autonomy, dignity and human connection.
These aren’t competing concerns. They are different dimensions of the same organizational challenge.
And they point towards an important role for business agility.
An agile organization should be able to experiment with emerging technologies, respond to feedback and adapt its approach as new information becomes available. But agility is not simply the ability to implement change quickly.
It also involves recognizing when change is creating unintended consequences and having the willingness to adjust course.
That means involving people in decisions about how AI changes their work, rather than presenting adoption as an inevitable requirement.
It means creating opportunities for employees to develop new skills while preserving the experiences through which essential judgment is formed.
It means evaluating success through customer value, organizational learning and human capability, rather than adoption statistics or cost reductions alone.
And it means being transparent about the difficult realities. Some roles will change. Some tasks will disappear. Some organizations will reduce their workforces. Pretending otherwise does little to build trust.
People deserve an honest conversation about both the opportunities and the potential losses.
Perhaps we need to ask a different question
The original question asked whether we are expecting people to embrace AI without acknowledging what they stand to lose.
Our community’s contributions suggest that, in many cases, we are.
But they also reveal a more fundamental problem: organizations may be moving too quickly towards adoption without adequately defining what progress should look like.
Aga offers a useful starting point:
“Why? What problem would it solve? And what might we lose by solving it this way?”
Aga Gajownik, Agile Business Consortium Ambassador
These are questions every organization should be prepared to answer.
Because the goal should never be AI adoption for its own sake. It should be creating better outcomes for customers, organizations and the people who make them work.
The organizations that thrive won’t necessarily be those that automate the most tasks or introduce AI at the greatest speed. They may be those that combine technological opportunity with human judgment, thoughtful leadership and the ability to learn continuously.
The real measure of progress isn’t simply what AI enables us to do. It’s whether we understand what we are gaining, what we risk losing, and what is worth protecting along the way.
Interested in exploring these thoughts further?
Take a look at our self-paced short course on AI in Agile Workplaces to build the foundational knowledge needed to make better decisions about how AI can be applied.
And of course, ultimately ask yourself are you asking people to embrace AI without acknowledging what they stand to lose?
Written By
Fallon Taylor
Knowledge & Content Lead, Agile Business Consortium
Fallon is the Knowledge Management and content production professional who is dedicated to the pursuit of knowledge as a gateway to personal and collective growth. She focuses on the creating of engaging content that educates, inspires and sparks curiosity. With demonstrated experience in large organizations, Fallon is a logical thinker who identifies creative solutions for the intended audience.
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