You’re Using AI at the Execution Layer. the Value Is in the Judgment Layer

Most of us have already checked the boxes. The software licenses are paid, the tools are integrated into the workflow, and the daily habit is formed. If you are a senior SEO or GEO practitioner, you likely use AI every single day. You use it to clear the hurdle of the first draft, to condense long documents, or to handle the initial pass of content that used to take twice as long. This is a genuine win for productivity, but it is not the full return on the investment.

There is a gap between the efficiency we are currently seeing and the strategic value these tools can actually provide. The problem is not the software, nor is it a lack of prompts. It is a mode problem. We are using the tools to do the work faster, but we are not using them to think better.

A peer reviewed study from Drexel University, conducted by Tim Gorichanaz and presented at the 2025 ASIS&T Annual Meeting, provides a framework for this. By analyzing 205 real world use cases of ChatGPT, Gorichanaz identified six distinct modes of AI interaction: Writing, Deciding, Identifying, Ideating, Talking, and Critiquing. While the data comes from Reddit and has some geographic limitations, the taxonomy reveals a stark reality for practitioners. Most of us are stuck in two modes, while the four that actually drive strategic value are being ignored.

The Default Modes of Execution

According to the data, Writing is the most dominant mode, accounting for 47 percent of use cases. This includes the standard tasks we all know, such as drafting, editing, summarizing, and translating. This isn't just a trend in small groups, either. McKinsey's 2025 State of AI survey shows that 63 percent of organizations using generative AI apply it primarily to create text. It is the path of least resistance.

The second pillar is Identifying, which made up 10 percent of the study's data. This is the mode of factual retrieval, explaining a concept, or synthesizing a document. The workflow is simple: research a topic, get a summary, and move to the next task. Together, Writing and Identifying represent the vast majority of how AI is deployed in the enterprise.

The danger here is that while these modes are useful, they belong to the execution layer. If your AI practice begins and ends with these two, you are simply using a sophisticated tool to automate work that was already being automated, just at a higher volume. You are becoming faster at the very tasks that are most susceptible to total automation.

Expert Interpretation: This matters because speed is a commodity. When everyone can produce a draft in seconds, the value of the draft drops to near zero. The tradeoff is between immediate output and long term strategic positioning. You should inspect your calendar and ask if your AI usage is primarily reducing the time it takes to produce a deliverable, or if it is improving the quality of the strategy behind that deliverable.

Moving Into the Deciding Mode

In a typical week, a senior practitioner faces a constant stream of high stakes questions. Which queries have enough AI visibility exposure to justify the effort? Is a brand's lack of visibility a problem with content architecture, or is it a signal and sourcing issue? How do you split a limited budget between SEO and GEO when both are critical? When do you fix a visibility drop quietly, and when do you escalate it to leadership?

Currently, most of us solve these problems using intuition and years of experience. That is not a mistake, as intuition is an incredible asset. However, using AI in Deciding mode allows you to pressure test the assumptions that underpin those intuitions before a decision is finalized.

This requires a shift in how you prompt. You cannot simply ask for an answer. You must provide the AI with the full context, including the competitive landscape, current visibility posture, and strategic constraints. The goal is not to let the AI make the decision, but to use it as a structured sounding board to find the blind spots in your own logic.

Expert Interpretation: The value here is the reduction of cognitive bias. We often lean on what worked three years ago, but AI can help us simulate alternative scenarios based on current data. The tradeoff is time, as this requires a much more deliberate and slow prompting process than simply asking for a summary. You should inspect your last three major strategic pivots and ask if you had a mechanism to challenge your assumptions or if you relied solely on gut feeling.

Identifying Gaps Through Ideation

For those in the SEO and GEO space, Ideating mode is perhaps the most underutilized tool in the kit. Most people use AI to brainstorm headlines, but the real value is in mapping entity and authority gaps that the brand has not yet recognized.

There are specific questions that only this mode can answer. For example, what angles of topical authority is the brand missing that AI retrieval systems are currently filling with third party sources? Which community signals, such as forum discussions or aggregated reviews, are shaping how LLMs represent the brand? What narratives exist in the model training data that the brand's own content has never addressed?

By using AI to ideate on these gaps, you move from reacting to visibility drops to proactively shaping the information environment that the AI relies on. You stop guessing what content to create and start identifying the specific authority voids that need to be filled to influence the retrieval process.

Expert Interpretation: This is the difference between content creation and entity management. The tradeoff is between the ease of producing "helpful content" and the difficulty of shifting a model's perception of a brand. You should inspect your current content roadmap and determine if it is based on keyword volume or if it is designed to close specific authority gaps identified by the AI.

The Value of the Honest Critique

Critiquing is the mode that offers the most immediate value but faces the most resistance. This is because it requires using AI to find flaws in work that you or your team have already spent hours producing. It is an exercise in intellectual humility.

When used correctly, Critiquing acts as a final safety net. It can catch the weak entity claims in a strategy that sounds authoritative but lacks the sourcing that retrieval systems actually trust. It can highlight the gap between how a brand describes itself on its own website and how a well prompted LLM describes that brand when asked a category question. It exposes the assumed premises in a GEO strategy that might be based on outdated logic.

Using AI as a critic allows a practitioner to see their work through the lens of the machine before the machine presents that work to the end user.

Expert Interpretation: This matters because internal reviews are often plagued by politeness or shared blind spots. The tradeoff is the temporary discomfort of seeing your work dismantled. You should inspect your review process and ask if you have a "red team" phase where the work is intentionally challenged by an AI agent tasked with finding every possible weakness.

Rehearsing High Stakes Conversations

The Talking mode in the taxonomy treats AI as a conversation partner. For a senior practitioner, the most practical application is rehearsal. There are conversations in this industry where the stakes are incredibly high and the narratives are complex.

Consider the client call where you must explain why organic traffic has dropped by 30 percent while AI search visibility is also poor. You have to hold two different causal explanations in your head simultaneously without letting them collapse into a single, oversimplified narrative. Or consider the internal pitch for a GEO budget to a leadership team that does not understand the difference between GEO and traditional SEO.

Using AI to simulate these stakeholders allows you to refine your language, anticipate objections, and ensure your logic is airtight before you enter the room.

Expert Interpretation: Technical expertise is useless if it cannot be communicated to a non technical decision maker. The tradeoff is the time spent in "simulated" conversations versus the risk of a failed real world meeting. You should inspect your upcoming high stakes meetings and decide if you are walking in with a tested narrative or just a set of slides.

The Divide Between Execution and Judgment

When you look at these six modes, a clear divide emerges. Writing and Identifying are execution layer modes. They are visible, they provide immediate gratification, and they are the areas where AI is rapidly reducing the need for human intervention. If you spend your time here, you are competing with the tool.

Deciding, Ideating, Critiquing, and Talking are judgment layer modes. This is where the practitioner's irreplaceability lives. These modes do not replace the human, but they augment the human's ability to exercise superior judgment.

A senior practitioner who only uses AI for the execution layer is effectively positioning themselves as a commodity at the exact moment the market for commodities is crashing. The goal is to shift the weight of your AI usage toward the judgment layer. That is where the strategic value is, and that is where the long term career security resides.

Practical next steps

The useful part is not only the idea itself, but the operating habit behind it. Use it as a checklist for decisions: what deserves attention now, what should be monitored, what needs a stronger evidence base, and what can wait until the system has more scale.