Agency Without Understanding

The New Responsibility

AI gives organizations more choices.

That is one of its most important promises. A team can now explore a workflow that would once have required a large implementation effort. It can produce a working prototype, change the behavior, test an alternative, and keep moving. A local problem no longer has to wait behind the cost and risk of a major software project before it can be addressed.

But more choices do not automatically create better outcomes.

When software was expensive to build and change, many organizational decisions were made indirectly. A purchased system imposed a model. An implementation timeline forced prioritization. A technical team translated the gaps it could see into something workable. The constraints were sometimes frustrating, but they also reduced the number of decisions people had to make explicitly.

As software becomes more adaptable, those decisions return to the organization.

That is agency. It is also responsibility.

Choice needs judgment

Imagine a team that wants to improve how it handles customer exceptions.

With a conventional system, the path may be familiar. Find the available configuration. Decide whether the process can fit it. Make a request for a customization if it cannot. If the request is too expensive or disruptive, create a workaround and move on.

None of those options are ideal, but their limits are visible.

With AI-assisted system design, the team may be able to create an alternative almost immediately. It can describe a workflow, generate an interface, define an agent’s role, connect relevant data, or model a report—and watch a version of the idea come to life. The barrier to trying something has fallen dramatically.

Then the harder questions arrive.

What counts as an exception? Who decides how it is resolved? When should it be escalated? Is the team solving a customer-service problem, a policy problem, or a symptom of an upstream process that no one has examined? What happens when a similar case appears in another part of the organization?

The generated workflow, agent, automation, or report has to answer these questions somehow. If the people closest to the work have not answered them together, the tool will still produce an answer. It will choose fields, states, defaults, paths, thresholds, and definitions that seem plausible from the available prompt or examples.

That is not a failure of the tool. It is the nature of building a system. Every system makes decisions about what exists, what matters, and what can happen.

The difference is that AI can make those decisions operational before an organization has had time to recognize that they were decisions.

The danger is not speed

It is tempting to say that this means organizations should slow down. I do not think that is the lesson.

Speed is valuable because it makes learning possible. A working prototype can expose an assumption that would remain hidden in a meeting. A generated draft—whether it is an interface, an agent behavior, an automation, or a report—can give people something concrete to disagree with. A new workflow can reveal that two teams use the same word to mean different things.

The danger is not moving quickly. The danger is mistaking visible output for shared understanding.

A prototype can look persuasive while embedding a narrow interpretation of the work. A clean interface, a confident agent response, or a tidy dashboard can make a process feel resolved when the underlying responsibilities are still ambiguous. A generated set of requirements can sound complete even though the people who will rely on the system have different mental models of what it is for.

This is why a working system is not evidence that an organization has made a decision intentionally. It is evidence that a decision has been made somewhere.

The real question is whether the organization can see, test, and own that decision.

Understanding is the discipline that makes agency usable

Judgment is what turns choice into an intentional direction. Shared understanding is what makes that judgment possible across a group of people.

By shared understanding, I do not mean unanimous agreement or a perfect description of the business. Organizations are complex. People see different parts of the work. Healthy disagreement often reveals something important that has not yet been made visible.

I mean a sufficiently clear, shared view of the important things:

• The concepts the organization is using and what they mean.
• The relationships and handoffs that shape the work.
• The rules, constraints, and tradeoffs that guide decisions.
• The places where the team is uncertain or does not yet agree.

This is not administrative overhead before the real work begins. It is the work that lets an organization use its new freedom without simply encoding the loudest assumption or the most convenient default.

An organization with shared understanding can use AI to explore faster because it has a way to evaluate what comes back. It can ask whether a generated workflow, agent response, automation, or report reflects its operating model. It can tell the difference between a useful simplification and a dangerous omission. It can revise the system without losing sight of why a decision was made.

An organization without it may generate more systems, more quickly, while becoming less clear about what any of them are meant to accomplish.

Responsibility is collective

For a long time, organizations could treat system definition as something that happened mainly in a project. Business stakeholders supplied requirements. Product or IT teams translated them. Engineers built the result. The work was important, but it was often bounded by a delivery process.

That model becomes less adequate when the system can change continuously.

If a workflow can be reshaped in a day, an agent can be given a new instruction, or a report can redefine a key measure, the people who understand the work cannot hand off their judgment once and assume it has been preserved forever. They need ways to remain involved as the model evolves. They need to review what the system now says about the business, notice where it has drifted, and negotiate the next change together.

This does not mean every employee needs to become a software designer. It means the knowledge held across the organization needs a path into the systems and agents that increasingly define how work is done.

That is a collective responsibility. Leaders must make room for discovery rather than demanding certainty too early. Product and technology teams must make assumptions visible rather than hiding them inside implementation choices. People close to the work must have a way to contribute the distinctions that only they can see.

The organization is no longer just a customer of software. It is increasingly a participant in the ongoing formation of the systems that work, decide, and communicate on its behalf.

A better measure of readiness

Organizations often ask whether they are ready for AI in terms of data, tools, skills, security, and governance. Those are important questions.

There is another question underneath them: are we ready to make more of our own operating assumptions explicit?

The answer is not a readiness score. It is a practice.

Start with places where work is difficult to explain but easy to recognize. Look for recurring exceptions, handoffs that depend on personal relationships, and disagreements that appear whenever a team tries to automate a process, delegate work to an agent, or define a metric. Use prototypes and AI exploration to make those areas concrete. Then bring the people affected by the work into the conversation before the first plausible version hardens into the only version.

This is how agency becomes useful. Not by replacing human judgment with faster output, but by giving human judgment more opportunities to shape what is built.

The next question follows naturally: if capable organizations often struggle to state what they want, is that because they are failing at requirements—or because understanding itself has to emerge through the work?