I've been thinking about what makes collaboration valuable, particularly as artificial intelligence becomes increasingly integrated into how we work.
We often evaluate AI by what it produces. How quickly can it complete a task? How accurate is its response? How much time or effort can it save?
As AI capabilities evolve toward achieving outcomes with greater autonomy, these questions become even more important.
Yet measuring what AI produces tells us only part of the story. The interaction itself can create significant value.
What becomes possible when human experience, judgment, and artificial intelligence genuinely work together?
A recent article by Monideepa Tarafdar in UC Berkeley’s Greater Good Magazine, How Artificial Intelligence Can Help With Critical Thinking, explores the value of having AI challenge our thinking.
The research suggests that disagreement and alternative perspectives can encourage deeper critical thinking.
That finding resonates with me, but I believe there's another dimension worth exploring.
The challenge isn't necessarily where the greatest value occurs. It may be what the challenge causes us to articulate.
What Emerges Through the Exchange
Consider an experienced employee working through a complex organizational problem with AI.
The AI proposes an outcome that appears reasonable based on the information available. The employee recognizes a potential issue and explains why that outcome may not deliver the intended value.
Drawing on years of experience, the employee recalls an earlier initiative that encountered unexpected challenges, identifies an operational dependency missing from the analysis, or introduces a recent compliance requirement that changes what is achievable.
As the employee elaborates, knowledge developed through experience becomes part of the exchange.
The AI can incorporate that context, reconsider possibilities, and help refine the path toward a more meaningful outcome.
The employee, in turn, may begin to see connections or alternatives they hadn't previously considered.
Something important has happened.
Knowledge that previously existed primarily in someone's experience has become available for examination, connection, and potentially broader organizational use.
The value emerged through the interaction.
Trust Makes Deeper Collaboration Possible
There is a condition that deserves particular attention in this process: trust.
Experienced employees carry an extraordinary amount of organizational knowledge. Some of it is documented. Much of it lives in their understanding of how things actually work.
That knowledge includes decisions made years ago, relationships between systems and processes, lessons learned through difficult experiences, and judgments developed over time.
The willingness to share that knowledge depends, in part, on the environment in which the interaction occurs.
When people feel their expertise is respected, they may be more willing to explain their reasoning, acknowledge uncertainty, challenge recommendations, and explore unfamiliar possibilities.
Trust creates opportunities for deeper participation.
Trust in AI deserves careful definition.
Trust doesn't mean believing that AI is always correct or disclosing everything to it. It means understanding how the interaction works, how information will be used, who can access it, and having confidence that human expertise will be respected.
An environment that encourages thoughtful challenge, protects appropriate information, and preserves human judgment is more likely to support meaningful collaboration.
Trust may also influence something beyond what people are willing to share.
It may also influence how willing they are to reconsider what they believe they know.
That openness creates possibilities for discovery.
The Knowledge Organizations Already Possess
Think about what happens when an employee with 25 or 30 years of experience leaves an organization.
Suddenly, questions arise that nobody else can answer.
Why was this process designed this way? What happens when this exception occurs? Why does one system behave differently from another? What was learned during the last implementation?
The departure exposes a deeper issue.
The organization has spent years benefiting from knowledge it never developed effective mechanisms to draw out, connect, and preserve.
This is fundamentally an organizational design challenge, and it exists long before an experienced employee announces their retirement.
What interests me about human–AI collaboration is the possibility of addressing this challenge as part of everyday work.
Imagine interactions intentionally designed to ask meaningful questions, explore the reasoning behind decisions, recognize dependencies, and surface knowledge that might otherwise remain unexpressed.
The experienced employee benefits from additional perspectives and opportunities to examine familiar problems differently.
The AI gains richer context within the interaction, allowing it to develop more relevant responses.
The organization gains an opportunity to recognize valuable knowledge that might previously have remained invisible.
Knowledge capture is an outcome. Knowledge emergence is a capability.
The mechanisms that enable knowledge to emerge may be among the most valuable capabilities organizations can develop.
Designing for Collaborative Value
This brings us to a larger question about AI-enabled work.
Are we designing AI to achieve predefined outcomes, or designing human–AI collaboration that helps us discover, evaluate, and realize outcomes worth achieving?
Both have a place.
In some activities, efficiency and automation will be appropriate priorities. In others, the greatest opportunity may come from preserving space for human reasoning, experience, and judgment.
An organization might initially define success as reducing the time required to complete a process.
Through meaningful collaboration, it may discover that the greater opportunity lies in improving the quality of decisions, reducing downstream errors, or redesigning the process entirely.
The intended outcome provides direction. Collaboration can deepen our understanding of what achieving value actually requires.
That is why the design of human–AI interaction matters.
We should be examining where AI can help people articulate what they know, discover what they haven't considered, and connect their expertise with new information and perspectives.
The objective is to create the conditions for better understanding, better decisions, and better outcomes.
We also need to recognize that what emerges through collaboration may change what we thought we were trying to achieve.
Beyond the Individual Interaction
There is one more possibility worth considering.
What happens to the knowledge developed through these interactions?
An employee explains an important historical decision. A previously undocumented dependency becomes visible. A new compliance requirement changes how a process should operate.
The immediate problem may be resolved.
What happens to that understanding when the conversation ends?
With appropriate validation, governance, and mechanisms for preserving relevant knowledge, future interactions could begin with a richer understanding of the organization.
Over time, this creates the possibility of something more significant than individual productivity improvements.
An organization that becomes increasingly capable of learning through collaboration.
That leaves me with a question I believe deserves serious leadership attention:
How do we design systems that preserve the value created through human–AI collaboration, rather than allowing it to disappear when the conversation ends?
The opportunity may extend far beyond helping people work more efficiently.
It may change how organizations develop, share, and build upon what they collectively understand.
Lisa Insley
Founder | HR SOS