The tools are deployed. The licenses are paid. And if you’re a senior SEO or GEO practitioner right now, you’re probably using AI every day - for drafts, for summaries, for first passes at content that used to take twice as long. That’s real productivity, and it’s not nothing.
It’s also not the return the investment is capable of producing. And the gap between what you’re getting and what’s available isn’t a tool problem. It’s a mode problem.
A peer-reviewed study published at the 2025 ASIS&T Annual Meeting by Tim Gorichanaz at Drexel University gives that problem a name (h/t to Shari Thurow for pointing me at this paper!). Analyzing 205 real-world ChatGPT use cases, Gorichanaz identified six distinct modes in which people actually use AI: Writing, Deciding, Identifying, Ideating, Talking, and Critiquing. The data came from Reddit and skews Anglophone, which limits its generalizability, but the taxonomy it produced maps uncomfortably well onto how most practitioners are actually working. Two modes dominate. Four are being left on the table. The four being left are the ones that determine whether AI makes you more strategically valuable or just faster at execution-layer work.
That distinction matters more right now than it has at any prior point in this industry’s history.
The Two Modes Everyone Defaults To
Writing was the largest category in Gorichanaz’s data at 47% of observed use cases - drafting, editing, summarizing, translating, generating. McKinsey’s 2025 State of AI survey confirms this at the enterprise level: the most commonly reported AI use cases are content drafting and information capture, and 63% of organizations using generative AI apply it primarily to create text.
Identifying - explaining something, answering a factual question, summarizing a document - was another 10% of the study’s data, and represents the other pillar most practitioners have built their AI workflow around. Research a topic, get a synthesis, move to the next task.
Together these two modes account for the overwhelming majority of how AI is being used, by practitioners and enterprises alike. Both have real value, yet neither is where the leverage is. And if your AI practice begins and ends there, you’re using an increasingly sophisticated tool to do work that was already being automated - just faster and at higher volume.
The other four modes (Deciding at 21% of Gorichanaz’s sample, Ideating at 9%, Talking at 8%, and Critiquing at 6%) are where the work becomes irreplaceable. They’re also where almost no practitioner has built a deliberate workflow, because nobody handed them one, and the pressure to show immediate output has consistently crowded out the space to develop one.
The Decisions You’re Still Making Alone
In the practitioner’s week, Deciding-mode questions are everywhere: which queries actually have AI visibility exposure worth prioritizing right now, whether a brand’s retrieval problem is a content architecture problem or a sourcing and signal problem, how to allocate effort across a portfolio when both SEO and GEO need attention and the budget doesn’t stretch to cover both fully, when to escalate a visibility concern to leadership versus when to fix it in the work before anyone asks.
Most senior practitioners are currently solving these questions with experience and intuition. That’s not a failure as experience and intuition are genuinely valuable, and no AI replaces them. But AI used deliberately in Deciding mode adds something experience can’t provide on its own: a structured pressure-test of the assumptions underneath the decision, applied before the decision hardens.
That requires more than a good question. Deciding mode requires giving the AI the relevant context (competitive landscape, current visibility posture, historical performance, strategic constraints) and then treating what comes back as a genuine input to the decision rather than a draft to be skimmed and set aside. It requires a workflow that doesn’t yet exist in most practitioners’ practice, not because anyone blocked it, but because no one built the time or structure for it either.
The same McKinsey data makes clear what that gap costs at scale: 88% of organizations use AI, but only 6% qualify as high performers generating meaningful enterprise-wide impact, and high performers are 3.6 times more likely to have fundamentally reworked their workflows rather than simply deployed tools into existing ones. The pattern holds at the practitioner level. Faster output from an unreconstructed workflow is not the same thing as better decisions from a restructured one.
The Gaps Nobody Briefed
For SEO and GEO practitioners, Ideating mode has a specific application that most are not using and most should be: mapping the entity and authority gaps the brand hasn’t recognized yet.
What angles of topical authority has the brand failed to establish that AI retrieval systems are currently filling from other sources? What community signals (forum discussions, aggregated reviews, third-party commentary) are shaping how LLMs represent the brand in response to category queries, and what would it take to shift them? What framings of the brand exist in model training data that the brand’s own content has never addressed or countered?
These are genuinely Ideating-mode questions. They’re also questions most practitioners have some version of in the back of their mind without a structured method for surfacing the answers. AI used in Ideating mode, not “give me five content ideas” but a genuine iterative exploration with deliberate constraints and real willingness to follow the output somewhere the team hasn’t already been, is one of the most direct methods available for finding those gaps before a competitor or a client audit finds them first.
The barrier isn’t capability. It’s the difference between a Writing prompt with a list output and an actual Ideating session. The first takes two minutes. The second takes twenty, requires a different posture toward the tool, and produces something that can’t be replicated by anyone who didn’t do it. That asymmetry is where practitioner value gets built in the current environment, and most practitioners are not claiming it.
The Honest Read Your Team Won’t Give You
This is the mode with the most direct application to daily practice and the most organizational resistance, because it requires using AI to find problems in work the practitioner or their team has already invested in.
Used properly, Critiquing is how a senior practitioner catches what internal review missed. The weak entity claim in a content strategy that sounds authoritative but isn’t backed by the sourcing AI retrieval systems actually trust. The gap between what the brand says about itself across owned properties and what a well-prompted LLM surfaces when asked a category question the brand should own. The assumed premise in a GEO recommendation that made sense six months ago and is now contradicted by how retrieval patterns have shifted.
That last application is not abstract. Running your own brand (or a client’s brand) through a structured AI Critiquing session before the next strategy cycle is exactly the kind of proactive work that separates practitioners operating at the judgment layer from practitioners operating at the production layer. It’s also the kind of work that changes the conversation with a client or a leadership team, because you’re surfacing problems before they become visible in the data rather than explaining them after the fact.
The reason Critiquing is underused isn’t a governance problem. It’s a disposition problem. Organizations and practitioners have broadly trained themselves to use AI to produce output, not to interrogate it. Reversing that habit is a choice, and it’s one of the more consequential choices available to a senior practitioner right now.
Rehearsal
The Talking mode in Gorichanaz’s taxonomy covers AI as a conversation partner, and for practitioners, the most valuable version of that is rehearsal for the internal and client conversations where the stakes are real.
The client call where you have to explain why organic traffic is down 30% while AI search visibility is also poor, and you need to hold two separate causal explanations simultaneously without letting them collapse into a single narrative that oversimplifies both. The internal briefing where you have to make the case for GEO investment alongside existing SEO budget to a leadership team that still conflates the two disciplines and wants a single number that explains the ROI of both. The agency or vendor review where you need to push back on a recommended approach without losing the relationship.
These conversations are recurring and high-stakes, and most practitioners walk into them with only their own mental rehearsal as preparation. Talking mode (role-playing the pushback, asking the AI to argue the other side, running through the version of the conversation that goes wrong) is not a replacement for experience. It is a preparation method that costs twenty minutes and materially changes the quality of the practitioner who walks into the room.
It doesn’t produce an artifact. It doesn’t show up in a utilization report. EY’s 2025 Work Reimagined Survey, which covered 15,000 employees and 1,500 employers across 29 countries, found that 88% of employees use AI at work, but only 5% use it in ways that fundamentally transform what they produce. The reason that gap is so wide is almost certainly that the advanced modes - Critiquing, Deciding, Talking - don’t produce something measurable in the moment. They produce a better practitioner over time, which is a return that compounds and doesn’t appear in a dashboard.
What Mode You’re In Is What Layer You’re On
The six-mode taxonomy maps almost exactly onto the split between execution-layer work and judgment-layer work. Writing and Identifying are execution-layer modes. They’re valuable, they’re visible, and they’re increasingly the modes that AI handles with less and less human involvement. Deciding, Ideating, Critiquing, and Talking are judgment-layer modes. They’re where the practitioner’s irreplaceability lives.
A senior SEO or GEO practitioner who uses AI only in Writing and Identifying mode is, functionally, positioning themselves as an execution-layer worker at exactly the moment when AI is most aggressively compressing that layer. That’s not a prediction about job displacement. It’s an observation about professional differentiation. The practitioners building durable value in this environment are the ones using AI to make their judgment better, not just their output faster.
Gorichanaz’s study reframes what information need actually means in the AI era, not just question-answering or uncertainty reduction, but what the authors call skillfully coping in the world, meaning the ongoing application of practical intelligence to situations requiring both understanding and action. For a senior practitioner, that framing is a useful diagnostic. The question isn’t what AI can do. It’s which parts of your work require the kind of practical intelligence that compounds with experience, and whether your current AI practice is making that intelligence sharper or just making everything around it move faster.
McKinsey’s workplace research finds that only 1% of leaders call their companies mature on AI deployment, meaning AI is fully integrated into workflows and driving substantial business outcomes. The practitioner-level version of that gap is just as wide, and just as fixable.
If you mapped your actual AI usage against the six modes this week (not what you intend to do, what you actually did) how would the distribution look? How much was Writing and Identifying? How much was Deciding, Ideating, Critiquing, Talking?
The practitioners who close that gap deliberately, who build even a minimal workflow around the judgment-layer modes, are not doing something exotic. They’re doing something most of their peers are not. In a discipline where the execution layer is getting compressed by the same tools everyone has access to, that gap is the one worth closing first.
If this framing connects to work you’re navigating, I’d like to hear about what you’re seeing. And if you want to go deeper on the structural layer beneath all of this, The Machine Layer is where that conversation continues.
And before you go, just a heads up that I have a special announcement coming on Tuesday this week, just 2 days from now! You’ll get an extra email and podcast this week. Thanks for your time, everyone, and I’ll be back soon.
