What Google’s New AI Guide Actually Debunks. And What It Doesn’t via @sejournal, @slobodanmanic

Anyone selling you llms.txt, content chunking, or AI-specific schema as the path to AI Overview citations has been wrong for 18 months. Google said so.

But there is a wrinkle worth pulling out. “Wrong for Google Search” is not the same as “wrong for AI agents.”

In the section answering whether SEO is still relevant for generative AI search, Google’s new optimization guide addresses AEO and GEO by name: “From Google Search’s perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO.” Five tactics get named in the Mythbusting section as things you can ignore: machine-readable files for AI like llms.txt, content chunking, AI-specific content rewriting, inauthentic mentions, and structured-data obsession. That is the debunking, in Google’s own words.

Read those five again, once for Google Search, and once for everywhere else.

The Scope Google Covered, And The Scope It Did Not

Google’s guide, and the entire AEO and GEO playbook, is about one thing: getting your content cited inside an AI-generated answer. AI Overviews, AI Mode, ChatGPT, and Perplexity all have the same shape. The genuinely different scope is what happens when an autonomous agent does not cite your website but acts on it.

The guide does briefly mention this. Under an “Agentic Experiences” section, Google acknowledges that “AI agents are autonomous systems that can perform tasks on behalf of people, such as booking a reservation or comparing product specifications,” and that “browser agents may access your website to gather the data they need to complete these tasks, such as analyzing visual renderings (like screenshots), inspecting the DOM structure, and interpreting the accessibility tree.” Google points to a separate document at web.dev for agent-friendly UX patterns.

What the guide does not address is whether the five tactics it debunked for the citation scope might still have utility for the agent-acting-on-website scope. That is the unfinished question. Read each of those five tactics twice: once for the citation scope where Google’s debunking is correct, and once for the action scope where the answer differs by tactic and use case.

LLMs.txt And Machine-Readable Files For AI

For citation in Google Search, Googlebot reads your HTML and ignores llms.txt entirely. An llms.txt file does not change what gets cited in AI Overviews or AI Mode, and no consultant should be charging you for one as a citation tactic.

For the action scope, the concept of a “website manual for AI agents” is reasonable. An autonomous agent navigating your website to complete a task on a user’s behalf could plausibly benefit from a curated index of which content covers which capabilities, which API endpoints exist, which workflows are documented where. The principle of having a machine-readable map for agents that need to act, not just retrieve, holds up.

But llms.txt itself is not yet the widely-adopted standard for that. None of the major platforms whose agents would consume it has committed to reading it as a discovery mechanism. The concept may turn out to be useful. The specific file format might end up being the standard, or another format might emerge, or the question might resolve in some other way entirely.

What is clear: Do not bolt an llms.txt onto your website because someone told you it would help your AI Overview citations. An llms.txt file will not move your AI Overview citation count. If you have a separate reason to publish a machine-readable manual for autonomous agents reading your documentation, that is a different decision, and the deployment data does not exist yet to make it confidently.

AI-Specific Content Rewriting Is A Tell

For citation in Google Search, rewriting content specifically for AI Overviews is treated by Google’s quality systems as low-effort content. Rewriting for AI is a tell, not a tactic.

For the action scope, the framing is wrong from the start. Writing specifically for AI is the wrong frame. The right frame is writing clearly for any reader, human or machine. Content that is structured for extraction (answer-first, citable specificity, modular blocks) helps every reader, including the autonomous-agent reader. That is the Machine-First Architecture position, and it is content discipline that survives both scopes.

The same logic carries into the next three tactics on Google’s list.

Content Chunking, Inauthentic Mentions, And Structured Data Obsession

Content chunking for AI follows the AI-specific rewriting logic. Breaking your content into tiny pieces specifically for AI is the wrong move, and building modular content blocks for retrieval-friendly extraction is content discipline that helps any reader. Google’s systems handle multi-topic pages natively.

Inauthentic mentions apply regardless of scope. Fake brand mentions, link-buying, and manipulated citations are wrong for any reader-or-agent retrieval system. Google’s debunking here is closer to an ethics statement than to a scope question. Manipulating retrieval through fake signals was a guideline violation two decades before someone coined GEO to try to disrupt the SEO tooling scene.

Structured-data obsession is the most easily misread of the five. Google did not say to stop using schema. The guide said there is no special AI schema, and that overfocusing on schema as the citation lever is wrong. Standard schema.org markup still has utility for entity recognition, knowledge-graph identity, agent-readable product data for agent-as-buyer flows, and the foundation of machine-readable identity in general. The Ahrefs study published on May 11, 2026 (1,885 pages adding schema, no meaningful citation lift on Google AI Overviews, AI Mode, or ChatGPT) measured a narrower question than the headline suggests. Schema is now table-stakes identity infrastructure. What does not work is bolting it on in month six and expecting a citation lift.

What To Do About Google’s AI Optimization Guide

Ask yourself two questions after reading Google’s new guide.

Are you paying anyone for tactics on Google’s debunked list? Stop.

Do you have any visibility into how autonomous agents read your website outside Google Search? You probably do not, and neither does anyone else right now.

Read Google’s guide as authoritative for what it covers, and keep reading the rest of the web for what it does not.

More Resources:

This post was originally published on No Hacks.

Featured Image: Roman Samborskyi/Shutterstock

Amazon Vs. Perplexity: The CFAA Case That Decides Whether AI Agents Can Visit Your Website via @sejournal, @slobodanmanic

Amazon sued Perplexity over its Comet browser shopping on Amazon under user authorization. On March 10, 2026, a federal judge in the Northern District of California issued a preliminary injunction blocking Comet from accessing Amazon’s logged-in pages. Roughly a week later, the Ninth Circuit Court of Appeals paused the injunction pending Perplexity’s appeal. On May 8, 2026, Perplexity filed its appellate brief, calling Amazon’s Computer Fraud and Abuse Act theory “a fundamental misfit” for an AI agent that visits under explicit user authorization. Oral arguments are scheduled for June 11, 2026, in Seattle.

The case is the first major legal test of agent-as-visitor rights in the United States. The question at the center of it is who counts as an authorized visitor when a human delegates the visit to an AI agent. The answer at the Ninth Circuit will set the precedent for every retailer, marketplace, booking platform, and SaaS website facing the same question, and most of them will be facing it within the next 12 months.

What Happened, March Through May

The case moved through three distinct phases in eight weeks.

In early 2026, Amazon filed suit against Perplexity in the Northern District of California. Comet, Perplexity’s AI-powered browser, can log into a user’s Amazon account using the user’s stored credentials, browse products on the user’s behalf, and complete purchases through Amazon’s checkout flow. Amazon’s complaint argued that this constitutes unauthorized access to Amazon’s computer systems under the CFAA, regardless of whether the user authorized the agent. Amazon also raised trademark and unfair-competition claims tied to Comet, rendering Amazon’s pages inside Perplexity’s interface.

On March 10, U.S. District Judge Maxine Chesney granted Amazon a preliminary injunction. The order blocked Comet from accessing password-protected portions of Amazon.com, including account pages, order history, and checkout. The judge accepted Amazon’s CFAA theory at the preliminary-injunction stage, finding that Amazon’s terms of service govern who is authorized to access logged-in areas and that a user’s instruction to an agent does not extend that authorization to the agent itself. Public-facing Amazon pages remained accessible to Comet under the order.

Roughly a week after the District Court ruling, the Ninth Circuit Court of Appeals paused the injunction pending Perplexity’s appeal. The procedural effect: Comet could continue operating on Amazon’s logged-in pages while the appeal played out. The appellate pause was the first signal that the CFAA theory might not survive scrutiny at a higher court, because preliminary injunctions are routine, while appellate stays of preliminary injunctions are not.

On May 8, Perplexity filed its appellate brief. The brief argued that the District Court’s reading of the CFAA stretches the statute far beyond its 1986 anti-hacking origin, that the user is the authorized party at all times, that Comet acts under the user’s delegated authority, and that Amazon’s contractual terms cannot manufacture federal criminal-law violations out of an agent’s lawful access on the user’s behalf. Mozilla, the Electronic Frontier Foundation, and other digital-rights groups filed amicus briefs supporting Perplexity’s position. The Ninth Circuit set oral arguments for June 11 in Seattle.

Amazon’s CFAA Theory In Plain English

The CFAA was passed in 1986. Its original target was hacking-style intrusion, the kind of unauthorized access that sounded like crime in the era of WarGames. Over the past two decades, the statute has been stretched in civil litigation to cover scraping, automated access, account sharing, and other behavior that exists on a different spectrum from break-in hacking. The Supreme Court narrowed some of that stretch in Van Buren v. United States (2021), holding that a person with permission to access a system does not violate the CFAA by accessing it for the wrong reason. Whether that narrowing reaches agent-on-behalf-of-user access is the question Amazon v. Perplexity puts squarely on the table.

Amazon’s theory has three parts.

First, Amazon’s terms of service explicitly prohibit automated access. The terms reserve access to Amazon.com for natural-person browsing, not for software agents acting on behalf of users.

Second, when Comet logs into a user’s Amazon account, Comet itself is the entity making the request, and from Amazon’s perspective, the agent is now the visitor rather than the user. Amazon’s authorization runs to the user, not to a software agent the user has delegated to.

Third, because Amazon never authorized Comet, Comet’s access is “without authorization” under the CFAA. The user’s instruction to Comet is irrelevant to whether Amazon authorized Comet.

Perplexity’s counter-argument runs the other direction. The user is the principal. Comet is the user’s agent in the legal-mechanical sense. When the user instructs Comet to log into the user’s own account and complete a transaction the user is authorized to complete, Comet’s access is the user’s access, channeled through software. There is no unauthorized party in the transaction. The CFAA was not written for, and does not reach, software acting under explicit user delegation.

The trial-court ruling sided with Amazon’s reading. The Ninth Circuit’s pause is the signal that the appellate panel may not.

Why The Ninth Circuit Paused The Injunction On Appeal

Appellate stays of preliminary injunctions are uncommon enough to be a signal. The Ninth Circuit applies a four-factor test for staying an injunction pending appeal, and the first factor is likelihood of success on the merits. A panel granting a stay is, in effect, signaling that the moving party has a reasonable shot at winning the appeal.

The panel did not write an opinion explaining the stay. Appellate stays at this stage rarely come with reasoned opinions. The signal lives in the procedural fact of the stay itself.

The legal analyst’s reading of why the panel might be skeptical of the District Court’s CFAA theory comes down to two doctrinal pressures. The first is the Van Buren narrowing. Van Buren cut the CFAA back from a tool that could criminalize any computer use in violation of a terms-of-service clause to a tool that targets actual unauthorized access. Reading Amazon’s theory carefully, the District Court’s ruling expands the CFAA in ways that look more like the pre-Van Buren expansion than the post-Van Buren narrowing.

The second pressure is the legal-agency doctrine that has governed delegated transactions for centuries. When a person authorizes another party to act on their behalf, the agent’s acts are imputed to the principal. Software acting under explicit user instruction is the modern, automated extension of the same principle. Reading the CFAA to ignore that principle would create a federal criminal-law trap for any user who delegates online tasks to software, which is now most users.

Neither pressure guarantees the Ninth Circuit reverses, but together they explain why the panel paused.

Why This Decides More Than One Lawsuit

If the District Court’s CFAA theory survives appellate review, the doctrinal effect is straightforward. Every major website gets a legal weapon for blocking AI agents from logged-in user accounts, even on accounts the user fully owns. The blueprint Amazon used against Comet becomes the standard playbook for any platform that does not want its users using AI agents.

The downstream effects line up category by category. Retailers can block AI shopping agents from price-comparing on logged-in accounts. Booking websites can block AI travel agents from completing reservations on user accounts. Banks and brokerages can block AI financial-management agents from logged-in dashboards. Marketplaces can block agents from posting listings on user accounts. SaaS platforms can block agents from managing subscriptions or running workflows on user accounts. In every case, the website’s terms-of-service language becomes the controlling document, and the user’s explicit instruction to the agent becomes legally irrelevant.

If the Ninth Circuit reverses, the doctrinal effect is the opposite. The CFAA gets pushed back inside its narrower 1986 lane. Websites lose the federal criminal-law tool for blocking user-delegated agents, and the question of agent access shifts to the contract-and-technology layer, where it arguably belongs. Websites can still block agents through technical means, terms enforced by civil remedies short of CFAA claims, or partnership APIs. But they cannot reach for the federal criminal statute as the lever.

A middle-ground outcome is also possible. The Ninth Circuit could affirm the injunction on narrower grounds, distinguish between specific kinds of agent access, or remand for further factual development. Each of those outcomes leaves the larger question unresolved and pushes the legal test forward into other circuits and other cases.

Whichever way the panel rules, the case is now the load-bearing precedent for agent-as-visitor access rights in the United States. Every major retailer, marketplace, and booking website will write its agent-access posture against the standard the Ninth Circuit sets on June 11.

What To Watch For At Oral Arguments

Three signals at oral arguments are worth watching specifically.

The first is how the panel handles the agency-doctrine question. If the judges push Amazon’s counsel hard on why a user’s explicit instruction does not extend authorization to the user’s chosen agent, that is the soft tell that the panel is uncomfortable with the District Court’s reading. If the judges instead press Perplexity on why an automated agent should be treated identically to a human user, the panel may be open to the District Court’s framing.

The second is whether the judges distinguish between kinds of agent access. The case so far has treated “agent access” as one category. The panel might draw lines: agents that complete transactions versus agents that only retrieve data, agents that use stored credentials versus agents that ask the user to log in each time, agents identified by a verified protocol versus unidentified browser automation. A ruling that draws those lines would shape how websites can structure their access posture more than a blanket affirm-or-reverse.

The third is what the panel says about the future of the CFAA in the agentic era. The judges have an opportunity to write a doctrinal frame for how the statute applies to AI agents generally, and they may or may not take it. A narrow ruling on Amazon-and-Perplexity-specific facts leaves the larger question for another case, possibly in another circuit, possibly with different facts. A broader ruling sets the doctrinal frame for the entire category.

Oral arguments at the Ninth Circuit are public. The audio is typically posted within hours. The panel composition, when published, signals how the case will likely be heard. Tracking those three signals through argument day is the cheapest way for a website owner to read the direction of travel.

What To Do This Week

Three concrete moves for any website owner whose users might want to use AI agents on logged-in accounts.

Read your own terms of service for clauses about automated access. Most terms of service inherited their automated-access language from the pre-agent era, when “automated access” meant scraping bots and unauthorized scripts. Decide whether that language still says what you want it to say when the automated access is a user’s own AI agent acting under explicit user instruction. If your position is that you want to welcome user-delegated agents, your terms should say so. If your position is that you want to block them, your terms should say that too, and your robots.txt and access-control posture should match.

Audit your access-control posture against the AI agent user agents your users actually use. The current major ones include GPTBot, OAI-SearchBot, ChatGPT-User, PerplexityBot, ClaudeBot, and Google-Extended for the search-and-citation crawlers, plus Perplexity Comet, ChatGPT Atlas, and the various Gemini surfaces for the user-delegated browsers. If your robots.txt or web application firewall blocks any of these by default, your users may already be hitting the wall on their own accounts. That is your decision to make, but it should be a decision, not a default.

Decide your position on agent access before the Ninth Circuit decides it for you. Three postures are coherent. The first is welcome: You accept user-delegated agents on accounts, you may charge differently for agent-driven transactions, you may publish an agent-readable surface that makes the work easier for both sides. The second is block: You treat user-delegated agents as unauthorized access, you back that position with terms and technical controls, and you accept that some users will leave for websites with the welcome posture. The third is partner: You build an API or capability surface that user-delegated agents can use without scraping your logged-in pages, and you put the agents through that door rather than the front one.

The default posture most websites have today was written before agent-as-visitor was a real access class. Whatever the Ninth Circuit rules on June 11, the default is now the wrong posture for most websites. Choose deliberately.

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This post was originally published on No Hacks.

Featured Image: Billion Photos/Shutterstock

You’re Using AI At The Execution Layer. The Value Is In The Judgment Layer via @sejournal, @DuaneForrester

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.

To see what I just built after months of work, you can read more about data for decisions and evidence for your conversations.

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This post was originally published on Duane Forrester Decodes.

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