AIVO Journal

Neutral Framing Wins for People. Institutions Can Beat It.

Political AIRS Pilot #3, v0.1 — Senate Leadership Fund (R) and Senate Majority PAC (D), US market, July–August 2026 Corrected in light of Pilot #1 findings (JD Vance / Gavin Newsom) This is the third political AIRS pilot we have run. The first, on JD Vance and Gavin Newsom, established

Measuring the Wrong Era: Why the IAB's New Visibility Framework Can't See Past the Query

Summary On August 3, 2026, the Interactive Advertising Bureau published Measuring Visibility in the AI Era, the industry's first attempt at a shared vocabulary for AI visibility measurement. It is a genuinely useful document in the narrow sense: it gives brands, agencies, and publishers common terms — Mention Rate,

Which AI a Voter Uses Materially Changes What They Hear

Political AIRS Pilot, v0.1 - Kathy Hochul (D) and Ron DeSantis (R), US market, July 2026 Every conversation about AI and politics so far has been about visibility: does a candidate get mentioned, does a claim get cited, is a campaign's messaging showing up in AI answers. That

The Other AI Failure Rate: Why "It Worked" Isn't the Same as "It Sold"

A number everyone is quoting, and mostly misreading Over the past year, one statistic has become shorthand for enterprise AI skepticism: 95% of generative AI pilots fail to deliver measurable P&L impact. It comes from MIT's Project NANDA, in a study titled The GenAI Divide: State

Adrift

A magazine starts selling ads to robots. The harder problem is what happens after they read them. Advertising has always required an audience willing, however reluctantly, to pay attention. Time, a magazine venerable enough to have covered both world wars, has found a new one: machines. Since July it has

When AI Talks About Candidates, It Doesn't Talk About Them the Same Way Twice

A Republican and a Democrat were run through the same four-turn AI probe. The distortions weren't partisan. They were structural. The finding in one line We measured how AI models represent political figures across a full voter conversation, not a single query. The same failure patterns showed

Occupying the Clause Isn't the Finish Line

A post making the rounds this week put it well: in AI search, you don't occupy a results page, you occupy a clause. "For running shoes, most people recommend ___." That position isn't bought at auction. It accumulates upstream, in reviews, citations, and cultural presence

Being Cited Was Never Proof of Anything

A sanctioned Wagner front group just showed why "generative engine optimization" was always measuring the wrong thing. A new report from CASM, the Centre for the Analysis of Social Media at the UK think tank Demos, tested five leading AI chatbots against fifty propaganda claims sourced from a

The Agent Decides

Every finding we've published so far assumes a person is still there. Someone opens a chat, asks a few questions, reads a recommendation, and either acts on it or doesn't. Even when a brand gets displaced at the third or fourth turn of that conversation, a

When Agents Want Things

A summer thought experiment for executives who think they've already priced in AI Every framework in this Journal so far assumes something convenient. It assumes the agent doing the recommending has no interests of its own. It ranks. It weighs context. It survives or doesn't survive

Recalled Is Not Recommended

Most of what gets published about AI brand visibility is vendor content wearing a research costume. This month, Harvard Business Review ran something different: a real study, credentialed authors, a methodology that holds up. It deserves a real response, not a dismissal. The research. John Gale, Luca Cian, and Luc

The Assistant Can't Keep a Secret

Luxury has spent a century teaching customers to read between the lines. A model built to answer questions plainly may be structurally unable to Take away the label and show a product against a blank, spacious background, more room than the object needs, nothing crowding it. To a human, that

The Committee You Can't See

Forrester's 2026 State of Business Buying report puts a number on something most vendors already suspected but couldn't prove. The typical B2B buying decision now involves 22 people: 13 internal stakeholders, nine external influencers. When the purchase includes genAI features, the buying group roughly doubles compared

Four Questions Worth Asking Before You Trust an AI Visibility Score

A public fight between two well-funded measurement companies revealed the real problem. It isn't the number either of them is defending. The CEO of a $19 million company told a few thousand marketers that a $1 billion competitor had a data problem. Brian Stempeck, who runs Evertune,

The Feed Is Becoming the Decision

Gen Alpha skipped the search box entirely. What replaced it may be the same gatekeeper problem, wearing a different interface A fourth grader in Colorado stands outside a Sephora, addressing a TikTok audience of strangers who've been debating whether she should be there at all. She walks the

What GEO Can and Can't Prove

A new academic survey of 45 studies checked the industry's most-cited claim against the evidence. It didn't hold up. The number shows up everywhere in GEO (Generative Engine Optimizaton) sales material: content optimized for AI search sees up to a 40% lift in visibility. It

Trust Breaks in Two Places, and Neither Is Visible

Why a recommendation can be trustworthy, sincere, and still not lead to a sale A bank can be validated early in a conversation with an AI system, recognized, credible, genuinely a contender, and still be quietly eliminated by the time the system reaches its final answer. That's not

The Zero-Click Blind Spot

Why the biggest source of AI-influenced revenue doesn't show up in any analytics dashboard, and what we're proposing to do about it A buyer asks an AI assistant for a recommendation. The assistant reasons through the comparison, names a brand, and the buyer, a few

What People Say About AI Recommendations, and What They Do

Two recent studies point in opposite directions, and the gap between them is the interesting part Ask travelers directly, and they say they don't trust AI to recommend where they should stay. A study published this month in Tourism Management, built on interviews, focus groups, and validated survey

Media Transparency Couldn't Be Delegated. Neither Can This.

Why GEO and AEO are necessary but insufficient, and why the decision belongs to the CMO, not the practitioner who built the current dashboard A decade ago, marketing leaders faced a version of this problem already, and most of them got it wrong for years before a handful of them

Recommended Is Not Booked

A question we're beginning to ask, not yet an answer we have An AI system reasons through a multi-turn conversation about a family trip. Dates, budget, a preference for all-inclusive, a location in Turkey. It compares options, weighs trade-offs, and lands on a recommendation. A

The Price Is Not Right

Why correct prices are becoming invisible to the systems now shopping on your buyers' behalf Zendesk's pricing page loads fine in a browser. Every plan, every tier, every number, all there. But when an AI agent tried to read that same page to answer a buyer'

Agentic Brand Control 101: What It Is, Why It Exists, and How It's Measured

Agentic Brand Control℠ is a brand's ability to survive an AI system's multi-turn reasoning process and remain the recommended choice at the point of final decision, not merely to be mentioned somewhere along the way. Most brand-AI measurement today stops at citation or visibility:

Procurement teams are learning a lesson brand teams already know

A Forbes Technology Council piece this month made a sharp observation about AI in procurement: the technology doesn't repair broken operations, it reveals them. Deploy AI across fragmented supplier data, siloed ERP systems, and inconsistent workflows, and the cracks don't disappear. They surface faster. Replace "

Profound's Own Report Proves the Wrong Point

“Christmas in July: How Retailers Win the AI Shelf” is careful, well-sourced work. Read closely, its own numbers argue against its own headline Profound published a report this month, “Christmas in July: How Retailers Win the AI Shelf,” on how retailers win what it calls the AI shelf. It