The Accuracy Paradox: Why Precision is Not Enough for Success with GenAI

We are entering a new economic age, defined not by capital or labor, but by partnership with artificial intelligence. Our reliance on these powerful collaborators benefits from taking inventory of classic economic principles, particularly the division of labor, and fine-tuning our approach. If we confuse the machine's ability to execute consistently (Precision) with its capacity for goal setting (Accuracy), we set ourselves up for failure. Mastering this partnership requires acknowledging the GenAI is a powerful collaborator requiring thoughtful management, where the ultimate quality of the outcome—whether success or biased error—lies squarely in the hands of the human leader.

We start by discussing partnerships between people to establish a foundation for understanding how collaborations with AI might be similar or differ significantly.

We often define a successful partnership through the lens of complementarity. One partner possesses strengths compensating for the other partner’s weaknesses. This dynamic leads to what Adam Smith and many other economic thinkers described as an appropriate division of labor. This form of efficiency often leads to Pareto efficiency, a state where the partnership has reached its maximum collective efficiency, such that any adjustment would decrease overall productivity. Achieving greater efficiency via a division of labor relies on complementary specialization.

In the context of this collective efficiency, you perform tasks suited to your skills, while your partner performs tasks suited to theirs. Then, over time, all partners improve their productivity. This is known as Wright's Law or the learning curve. Even if one partner excelled in every area, they would quickly realize they lack the time to handle everything alone. Allowed to freely organize, members of a partnership naturally gravitate toward a division of labor focusing on tasks where they incur the lowest opportunity cost. This emergent specialization enhances productivity and represents the formal concept of comparative advantage, formalized by David Ricardo.

Together, the partners create superior efficiency. The sum of these parts creates a greater whole than if one individual attempted everything alone. This complementarity defines what makes a partnership effective. Yet another component exists regarding how partners work together. Humans experience bad days. Maybe a partner gets sick. Unforeseen circumstances arise, rendering a partner unable to perform as they usually would.

Excellent partners step into the breach. They look out for one another. They provide assistance when necessary. A superior partner is proactively helpful. They understand the goals and objectives in a way to help their partner when they cannot perform as desired. While we optimize based on our division of labor, humans still possess the ability to perform the partner’s job in a pinch. It may lack optimality, but it remains practical. Ultimately, people are doers and have anticipatory imagination. We can empathize and sympathize. We fill gaps.

Let us apply these two concepts—1) complementarity and 2) proactivity—to partnering with a Generative AI (GenAI).

1) Complementarity and The Division of Labor

Partnering with GenAI can lead to massive efficiency. However, this success requires the human partner to understand the optimal division of labor to realize the most value from the relationship.

To explain this, we will use two concepts generally associated with statistics: precision and accuracy. Many consider precision and accuracy synonyms. Perhaps at the highest level, one could consider this true. However, upon deeper inspection, they remain distinct. This nuance is critical.

Precision The GenAI focuses heavily on precision. Think of precision as repeatability. If you ask the AI to perform a task, it will execute the request almost exactly the same way repeatedly, yielding a consistent result. For example, if you request a summary, the tool will produce a summary based on your prompt and the narrative data provided. Across multiple narratives being summarized, the output will vary because the content of each narrative is different. However, how it is summarized will be incredibly consistent. This is the nature of precision. However, the output will not be perfect. Human partners must still provide quality control. Often, a lack of precision (an error called noise) manifests not in what the AI did, but in what it failed to do. (an error of omission) It may omit critical context. This tendency makes quality control difficult, as the human must be aware enough to understand what the summary excluded. In the appendix, we provide suggestions for enhancing prompt engineering to achieve more precise GenAI outcomes. Also, 100% perfect may not be the goal. In the context of the Pareto Principle or the "80/20 Rule," sometimes 80% is good enough given the cost/benefit tradeoff for achieving more precision.

Accuracy On the other hand, we humans own accuracy. View accuracy as goals, objectives, targets, and the ultimate decisions made with information. A GenAI is completely incapable of understanding accuracy. You should not view the tool as "causing" an accurate or inaccurate result. It lacks the capacity to function in this context. Being accurate in setting goals and objectives falls squarely on the human partner. The GenAI is unable to supply anything related to your strategic intent.

The standard of accuracy is as a comparison to those goals, objectives, and targets. The degree to which results vary from that goal measures the degree of (in)accuracy. Thus, this variance results from the underlying data available to inform the results.

Next is an example of AI misuse leading to imprecision and doubtful accuracy.

AI Misuse In The News

The news surrounding Deloitte Australia and the Australian government's Department of Employment and Workplace Relations (DEWR) in late 2025 provides a clear illustration of GenAI output appearing to be both imprecise and inaccurate.

"Deloitte misused AI and used it very inappropriately: misquoted a judge, used references that are non-existent. I mean, the kinds of things that a first-year university student would be in deep trouble for." — Senator Barbara Pocock, Australian Greens spokesperson on the public sector, on the Deloitte report to the Department of Employment and Workplace Relations (DEWR), 2025.

Certainly, the report appears to be riddled with factual errors, suggesting a lack of quality control to manage the expected precision. Certainly, the lack of precision gives the appearance that the report's recommendations lacked accuracy. The client should rightfully wonder,

"Well, if they cannot even quote the known law and judges correctly (imprecise), what other data sources did they leave out? Should I rely on their conclusion (inaccurate)?"

My past experience as a Big 4 Managing Director at a firm similar to Deloitte suggests this is a textbook example of a consulting firm's misplaced reliance on AI. I can only speculate that this client disaster resulted from the firm's attempt to drive down the costs of a low-margin government project. In the Big 4 firm context, the ultimate responsibility for quality control is the Managing Director or Partner leading the project. Clearly, their eye was not on the ball. Or perhaps their eye was clouded by misplaced confidence in AI.

The Bias Fallacy and The Three Nevers

Since accuracy is the primary job of the human partner, next, we will discuss how data is often central to the (in)accuracy achieved.

A lack of accuracy is also known as bias. Much literature discusses how GenAI can lead to biased results. A seminal article titled "On the Dangers of Stochastic Parrots" outlined many of these challenges, noting potential social implications. However, blaming a GenAI for bias or a lack of accuracy involves misplaced blame.

To appreciate the subtlety of potential bias, it helps to view accuracy through the lens of the "Three Nevers of Data." I explored these principles in our article titled "When Maps Melt: The Limits of Knowledge in Decision-Making."

The Three Nevers state:

1. Data is never complete. (Rumsfeld's Never)

2. Data is never static. (Goodhart's Never)

3. Data is never centralized. (Hayek's Never)

For the purposes of this article, the operative understanding is that we (neither humans NOR the AI) NEVER have all the data when making a decision.

GenAI excels at processing known data. However, the tool remains incapable of perceiving the Three Nevers. AI cannot know what it does not know. Thus, there is ALWAYS data the GenAI did NOT include in its processing. If the machine lacks the data, it does not stop functioning. Instead, it proceeds to generate content solely based on the patterns it knows. It functions, but it becomes precisely inaccurate (see the lower left dartboard in the next graphic). The AI fills the void with plausible-sounding outcomes. Most GenAI is programmed to be encouraging to the human user. Because data inevitably suffers from being incomplete, dynamic, and decentralized, information gaps will always exist. The GenAI will proceed using only the data it possesses, oblivious to what is missing. This limitation represents the ultimate expression of inaccuracy and bias.

Understanding the Three Nevers reveals the true source of bias. It is not the algorithm; it is the input data. The GenAI merely processes the incomplete, dynamic, and decentralized data available to it. This mechanical reality brings us to the nature of the tool itself.

How Bias is Built from Precision and the 3 Nevers of Data

Consider a hammer. A hammer is helpful when building a house. A hammer can also be used to bludgeon a neighbor. No one would blame the hammer for its misuse in such a horrible scenario. The hammer was the tool used; the intent to hurt came from the person wielding the hammer. Similarly, the AI should not bear blame for biased outcomes resulting from the Three Nevers. In most situations, the challenge is not an intention to do the wrong thing; it is not having the data to accurately guide the right thing.

Regardless of intention, the GenAI amplifies the quality of the data. If people ignore the Three Nevers and feed the machine raw, uncurated, incomplete information, we effectively choose to misuse the tool.

Garbage In -> Garbage Out.

-- and in the case of AI --

BIG Garbage In -> BIG Garbage Out.  

The bias is not a ghost in the machine; it is a reflection of the data we failed to curate. Therefore, the responsibility lies with the human partner and the degree to which they resolve potential bias.

We manage this by remaining explicitly clear about goals and objectives. For example, we regularly partner with AI for writing an article to help my Founders’ Co-pilot clients with a startup challenge. Achieving accuracy means we must be specific. We define who the clients are, what challenges they face, and the specific objectives of the piece we wish to write. Also, we relentlessly curate the data provided to the GenAI. Also, we consider decision-making through a Bayesian updating lens. That is, since we know the 3 Nevers exist, we seek to update our understanding and decisions as the 3 Nevers are inevitably revealed.

Over time, our ongoing startup article series has provided a known, curated set of entrepreneurial-focused data, both external to PFR and created by PFR practice with our clients. This is a source of accuracy. Being clear upfront, before engaging the GenAI, is essential for ensuring useful results.

Rules for an Accurate and Precise Partnership

While GenAI offers precision, human guidance increases this precision and enables accuracy. I adhere to a few rules of thumb to ensure this. Please see the appendix for more information.

1. Ring-Fence the Source Material We always provide the source material. We will not give a prompt asking the AI to scour the general internet for information. This practice is dangerous. The human partner should always source the core information the GenAI uses. For instance, upload PDFs or paste material directly into the chat. PFR regularly adds to its curated data sources to assist our Copilot clients. Put a "ring fence" around the source material. The human must curate this information. Using validated, high-quality sources ensures the AI works from a foundation of truth.

2. Define the Voice You must teach the AI exactly how the human partner wishes to write. Without this instruction, the output will resemble a generic social media post.

Be specific about citation formats. Be specific about grammatical rules. Be specific about the voice concerning your persona. For example, Jeff Hulett is the President and Founder of Personal Finance Reimagined. In this role, I utilize an executive voice. Also, Jeff Hulett is a professor and faculty member at James Madison University. In this role, I utilize a different, academic voice.

3. Prompt Engineering We have written extensively on prompt engineering elsewhere. Suffice it to say, a specific method exists for asking GenAI questions. Mastering this skill increases precision significantly.

By strictly adhering to this division of labor—humans providing accuracy, AI providing precision and processing—we create a partnership far greater than the sum of its parts.

2) The Proactivity Gap

Finally, the nature of the GenAI-Human partnership is addressed. We people are prone to what is called anthropomorphism. We are given to humanize our nonhuman relationships. Of course, the family pet is a classic example of humanizing nonhumans. (Wait a second, you mean Fluffy is NOT human?!) The same is true with GenAI, especially since it is programmed to act more human. But remember, GenAI is ultimately a highly efficient association machine. It correlates a new set of data, consistent with its neural network programming, seeking to render a precise output based on that association. Its magic is the immense processing power and the scope of the data it was programmed upon (the Large Language Model or LLM). Plus, its programming includes Natural Language Processing (NLP), which makes the output more pleasing to the human user.

The AI is unable to be proactively helpful. It lacks the capacity to step in if you experience a difficult day. As a partner, the GenAI performs its specific precision-focused functions well. However, it lacks the capacity to perform many of your unique functions. The AI does not have the capacity to perform your complementary accuracy roles the way you can perform the AI's precision role, even if not as well. This limitation is acceptable, provided you understand the constraint. You will get the most out of your AI relationship by understanding its strengths and weaknesses in the context of the human partner's role. It is best to resist anthropomorphizing GenAI, but it can be difficult to resist.

Your relationship with the AI should be strictly functional, much like a high-quality tool is to a carpenter. Is it possible an AI could identify when your productivity is slipping? Yes, if it has access to your productivity data. Could the AI even behave in an empathetic kind of way and encourage you? Quite possibly, as long as the associations found in the LLM's training predicted an empathetic response to an input.

But this is not the same as being proactive to perform a role it is not capable of performing. People tend to be gap fillers, adapting to the needs of a situation. People are able to do and to imagine beyond the data we have. AI tends to be a role filler, but only those association-based roles found in the neural network and the data it has.

Conclusion

The optimal Generative AI partnership depends on adherence to the division of labor. We should not mistake the machine’s consistent output for truth; confusing precision with accuracy invites organizational bias and strategic failure. Just ask Deloitte. True efficiency is only unlocked when the human leader diligently manages the "Three Nevers of Data" and provides the ethical goals the AI cannot supply. By accepting this high level of responsibility, we move beyond simply using a tool and achieve the unparalleled power of a truly productive collaborative partnership.

Appendix: The Essential Guide

For those wishing to go deeper, We have written a more extensive guide on this topic. The article provides a comprehensive framework for integrating GenAI into your professional workflow. It details specific strategies for achieving the delicate balance between accuracy and precision.

You can read the full article here: The Essential Guide to Partnering with GenAI

In this piece, I outline eight actionable suggestions for maximizing your partnership. Here is a brief summary of what you will find:

1. Define Clear Goals You must remain specific about your objectives. The AI performs best when given a well-defined task. This clarity allows the tool to align its precision with your intended outcome.

2. Provide Context The AI lacks inherent context. You must ensure your prompts contain detailed and relevant background information. This step helps generate responses tailored to your specific needs rather than generic outputs.

3. Review Outputs for Precision While the AI often sounds confident, it is not always correct. You must review the output to confirm it aligns with your original goals. I perform a heightened level of quality control, especially regarding citations.

4. Curate Credible Information Enhance the output by supplying curated information from reliable sources. Do not rely solely on the AI to source information. I often use my own articles from The Curiosity Vine or Personal Finance Reimagined to provide the AI with a foundation of truth.

5. Use Iterative Prompting Refine your prompts if the initial responses do not meet expectations. Breaking long prompts into a series of "bite-sized" requests will help steer the AI closer to what you need.

6. Leverage Consistency The AI excels at generating repeatable, structured responses. Use it for tasks benefiting from this precision, such as summarizing information or generating lists.

7. Specify Output Format Clearly indicate the format you want. Whether you require a summary, a bullet-point list, or a narrative, this guidance helps the AI structure its output to align with your preferences.

8. Provide Feedback After receiving an answer, offer feedback. Letting the AI know when an answer meets or falls short of your expectations allows it to refine its future responses.

The full article also includes a detailed example featuring "Bob," a young professional buying a car. This practical application demonstrates how to use these eight suggestions to make a complex financial decision. I highly recommend you read the full piece to see these rules in action.

Additionally, my book, Making Choices, Making Money, provides GenAI prompt engineering guidance and suggested prompts in the context of specific Personal Finance decisions. Also, GenAI is integrated into our personal finance choice architecture technology called Definitive Choice. The technology is available with the book.