Should Your Newest Adopted AI Tool Learn at the Expense of Your Company's Reputation?
I have heard a lot of people use the phrase “speed beats perfect every time” when it comes to AI. This is a dangerous phrase, and one that exposes an even deeper philosophy about AI tools that is misaligned with Operational Literacy with AI.
To be fair, I am willing to say that some of the individuals using this argument are probably doing so because they understand that the tool needs to “learn” and requires information from successful trial-and-error situations.
While I do agree with this on basic principle, adopting this mantra as a philosophy for operating AI tools in general is contrary to how the successful operation of such tools takes place. Potential customers and clients should not have to participate in the experimentation of our AI tools “learning.”
Like many organizations, we use AI tools at Structured Thinking to find potential new clients and, in most cases, initiate the first cold outreach. The tool we use, like most of the more powerful ones available, has an AI capability that allows it to crawl our website, “discover” our business and message, and draft emails and responses accordingly.
The tool then uses what it learns from its outreach and continually refines and “learns” more about what we offer and the types of clients who would best benefit from our services. It definitely sounds like the perfect tool.
The problem is that, for all of its sophistication, it is still a tool. It can access information about our company without necessarily understanding the relationships between that information.
This is where information and environment become two very different things.
A Structured Environment doesn't simply give an AI system more information. It establishes the context, standards, priorities, relationships, boundaries, and expectations the system needs to use that information appropriately.
Our website contains a great deal of information about our company. But information alone doesn't tell an AI system which offerings are central to our business and which ones are supporting products. It doesn't necessarily explain why we developed those offerings, which audiences we are trying to reach, what differentiates us from competitors, or which opportunities we intentionally choose not to pursue.
Those distinctions matter because an AI system can make perfectly logical decisions based on accurate information and still arrive at the wrong understanding of the organization.
In our particular situation, we offer several leadership development books that cover everything from new manager development to team building. Because these books and their offers are much easier and more straightforward to understand, the tool naturally tries to surface these products ahead of what our company primarily sells. So, while these books help to fill out our broader, more holistic approach to training, they are not the main attraction.
The issue with all of this is not simply that the tool can make a mistake about who we are, what we do, or what our goals as a company are. Humans can make those same mistakes. The issue is really two-fold.
First, the tool has the power to reach farther and faster than any of our human team members. An incorrect message, assumption, or outreach strategy can therefore reach potential customers at a scale that would be difficult for a human counterpart to replicate. By the time we recognize the mistake, hundreds or even thousands of people may have already experienced an inaccurate representation of our organization. Speed has not helped us. It has simply amplified the mistake.
The second issue is potentially even more consequential. The results of that outreach can become part of the information the system uses to determine what works.
That means the impact doesn't necessarily stop with the people who received the incorrect message. The system may begin learning from the results produced by its own incorrect assumptions.
Imagine the tool gradually shifting its outreach toward individuals looking for resources on building highly successful teams, while entirely missing the primary market for Operational Literacy with AI.
There are some who would argue that the tool is simply responding to market demand. But is it? I would argue the opposite.
Is the tool discovering genuine demand, or is it responding to signals created by its own incorrect assumption of our primary market? If the tool starts with an incorrect or incomplete understanding of our business, targets the wrong audience, and then uses the results of that outreach to determine what works and what does not, we have created a much larger problem.
At that point, the tool isn’t simply making an incorrect assumption. It may start generating the very data that appears to validate the incorrect assumption. Over time, the system could continue shifting toward a market we never intended to pursue while overlooking the very real market for our primary offering.
What looks like the tool learning from the market may actually be the tool learning from an environment it helped to create. This type of shift might very well be possible with human counterparts doing the same thing; however, the exponential speed at which AI can deliver can cause organizations to find themselves in an unexplained situation almost overnight.
The real question organizations should be asking themselves when using such tools isn’t “can the tool learn our organization?” but “can we build the environment and learning the tool needs to understand who we are, what we offer, and who our true clients really are?” This is where the problem stops being about what the tool is capable of doing and becomes a question of whether the organization is capable of governing what the tool does.
Operational Literacy with AI is the ability to intentionally direct, evaluate, oversee, and responsibly integrate AI systems while maintaining human judgment. That last part is extremely important.
Responsibly integrating AI systems doesn’t mean leaving tools to learn “on their own” and at the expense of potential clients. It means understanding and identifying where the decision layers exist and how to intentionally direct, evaluate, and oversee them. The goal isn't to create a system that can never drift. The goal is to understand where drift can occur, establish appropriate guardrails, and maintain enough human oversight to recognize and correct it when it does.
In our situation, after we identified the tool's decision layers, we realized that we could not exercise as much control over the environment as we needed to maintain the integrity of our message. This did not mean the tool became useless. Governance doesn’t necessarily mean using AI less; rather, good governance can allow you to use AI more confidently.
Once we understood what we were working with, we stopped expecting the tool to independently determine what mattered most about our business. We moved those decisions back into the environment we could intentionally control and allowed the tool to operate within those boundaries.
By doing so, we are much more successful at minimizing drift for our company while keeping the tool functioning at its peak performance for our situation. In this way, the tool has now become more important because we can more heavily rely on it for the task it is good at performing.
This may not be the same conclusion every other organization using the same tool would come to, but that is not the point.
The point is that lots of companies and organizations do not know what Structured Environments are or how to apply human judgment to them.
Organizations can respond to this lack of understanding in two very different ways. Some become so uncomfortable with what they cannot see that they underuse powerful tools. Others move in the opposite direction, trusting those same hidden layers far more than they should.
The two responses look completely different, but they originate from the same problem: neither organization understands the environment well enough to intentionally govern it.
This is why the concept of Structured Environments—and the ability to intentionally direct, evaluate, oversee, and maintain human judgment over them—is so important. Tools with greater sophistication often have less visible decision-making for the people using them. Greater capability, therefore, doesn’t eliminate the need for human judgment; rather, it necessitates the importance of knowing where it belongs.
The greater the sophistication of the tool, the more care and intentionality must be taken when using it.
Speed asks the question, ‘How quickly can we do this?’ Capability asks, ‘Can the tool do this?’ Operational Literacy with AI asks, ‘Should it, under what conditions, and who remains responsible for the output?’ Only the last question operates in the sphere of organizational stewardship.
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