{"id":5018,"date":"2026-08-09T02:27:32","date_gmt":"2026-08-08T23:27:32","guid":{"rendered":"https:\/\/ivitskiy.com\/blog\/strategic-profits-2025\/"},"modified":"2026-08-09T02:27:32","modified_gmt":"2026-08-08T23:27:32","slug":"strategic-profits-2025","status":"publish","type":"post","link":"https:\/\/ivitskiy.com\/blog\/en\/strategic-profits-2025\/","title":{"rendered":"How AI Search Will Make or Break Your Brand: Igor Ivitskiy at Strategic Profits 2025"},"content":{"rendered":"\n
In short:<\/strong> In a year-long experiment, Igor Ivitskiy changed how AI systems described and recommended his professional brand. The starting answer was two generic lines; a year later, the profile was detailed enough to generate recommendations and dozens of warm enquiries in a month. His central argument was that AI visibility is built around meaning, evidence and a coherent entity, not by copying an SEO keyword playbook. The result is a strategic case about earned recommendation, not a shortcut or a promise of instant reach.<\/div>\n\n\n\n

In 2025, Strategic Profits hosted this interview-style presentation in Florida, US. The speaker was Igor Ivitskiy<\/a>, who shared a personal experiment outside his usual Google Ads topic.<\/p>\n\n\n\n

The talk, \u201cHow AI Search Will Make or Break Your Brand\u201d<\/strong>, examined what changes when prospects ask an AI system whom to trust instead of opening a list of search results. Igor contrasted conventional keyword research for Google Ads<\/a> with the way an AI forms a connected picture of a person or company from public evidence.<\/p>\n\n\n\n

12 months<\/div>
from two generic lines about Igor to detailed AI answers, recommendations and dozens of warm enquiries in one month<\/div><\/div>\n
Source: Strategic Profits 2025 talk, Igor Ivitskiy.<\/div>\n\n\n\n

AI visibility is a coherence problem<\/h2>\n\n\n\n
\"AI
AI answer panel citing website sources<\/figcaption><\/figure>\n\n\n\n

Igor\u2019s main idea was that an AI answer does not simply promote one page. It tries to recognise an entity and assemble a useful account of that entity from multiple signals. A brand becomes easier to recommend when its public facts point to the same area of expertise, carry concrete evidence and make sense together.<\/p>\n\n\n\n

This is why replacing Google keywords with chatbot phrases misses the point. Search advertising still has its own controls: Google documents keyword matching options<\/a>, and broad match<\/a> governs how widely an ad can meet relevant searches. Those mechanisms manage campaign reach. AI recommendation depends on whether the system can understand what the brand is known for and why that claim is credible.<\/p>\n\n\n\n

The boundary also applies to exclusions. In advertising, negative keywords<\/a> and Google\u2019s negative-keyword guidance<\/a> help prevent ads from serving on unwanted searches. They cannot create a coherent professional identity in an AI system. That requires consistent facts and evidence strong enough to survive synthesis across sources.<\/p>\n\n\n\n

\n
1<\/span>Territory<\/strong>
A clear problem and area of expertise in which the brand should be understood.<\/div>\n
2<\/span>Evidence<\/strong>
Concrete public facts that are varied in expression but coherent in meaning.<\/div>\n
3<\/span>Recognition<\/strong>
An AI system connects the evidence to the entity and can use it in a recommendation.<\/div>\n<\/div>\n
Source: high-level model from the Strategic Profits 2025 talk.<\/div>\n\n\n\n

My principle for AI visibility<\/strong><\/p>

I do not treat it as a rewrite of SEO. I want public evidence to tell one coherent story about the work I do, while each fact adds something specific enough for an AI system to understand and reuse.<\/p><\/section>\n\n\n\n

This distinction belongs in training because it changes the question from \u201cWhich phrase should we repeat?\u201d to \u201cWhat should the market be able to verify about us?\u201d Our school examines that way of working as part of a broader evidence-led marketing practice.<\/p>\n\n\n\n\n\n\n\n

Key insights<\/h2>\n\n\n\n
    \n
  • A recommendation changes the starting level of trust.<\/strong> Prospects who arrived after an AI recommendation were already warm and had framed a relevant need.<\/li>\n
  • The unit of visibility is the entity, not a single page.<\/strong> The system needs a coherent picture of a person or brand before it can explain why that entity fits a request.<\/li>\n
  • Facts matter more than generic expertise claims.<\/strong> Numbers, outcomes and specific credentials give an AI system material it can distinguish from another broad guide.<\/li>\n
  • Consistency does not require identical wording.<\/strong> Public descriptions can vary while still reinforcing the same professional identity.<\/li>\n
  • Timing is controlled partly by model updates.<\/strong> Igor could not request immediate indexing and saw the visible change after a major update.<\/li>\n
  • The case is evidence, not universal proof.<\/strong> It is a one-person before-and-after experiment whose business effect should be checked through repeated answers and traceable enquiries.<\/li>\n<\/ul>\n\n\n\n

    Key moments from the talk<\/h2>\n\n\n\n

    The starting point: two generic lines<\/h3>\n\n\n\n

    Igor began with a simple test in March 2024: he asked ChatGPT who he was. The answer recognised a Ukrainian mathematician with some Google expertise, but it did not capture the professional position he wanted a potential client to see. His summary was blunt: \u201cZero authority, zero recognition.\u201d Instead of treating that answer as a reputation verdict, he treated it as a baseline. The business context made the gap consequential. His consultancy works with clients spending at least $1 million a year on advertising. They are experienced buyers who rarely respond to ordinary targeting, feeds or familiar sales claims. Years of pitches have trained them to question every assertion. If an AI system could not connect Igor\u2019s name to a precise problem, the brand was absent at the moment when one of those buyers asked for a recommendation. The experiment therefore focused on recognition inside a narrowly relevant context, not on producing a flattering general biography.<\/p>\n\n\n\n

    Why an AI recommendation changes the conversation<\/h3>\n\n\n\n

    Traditional advertising and AI recommendation created different openings in Igor\u2019s experience. An ad introduced an unfamiliar seller, so the prospect\u2019s first reaction was often scepticism. Credibility then had to be built from the beginning. A recommendation generated in response to the prospect\u2019s own question arrived with context already attached. The person had described a budget, a challenge or a preference, and the system had returned a name that appeared to fit. Igor called this an instant transfer of credibility from the AI system. It did not close the sale, but it changed the first conversation. The lead was warmer because the initial problem definition had happened before contact. During the month described in the talk, dozens of prospects approached him with a version of the same explanation: \u201cChatGPT recommended you.\u201d For a consultancy aimed at a small group of sophisticated buyers, that difference mattered more than raw exposure.<\/p>\n\n\n\n

    The strategic value is easy to misunderstand. The aim is not to make an AI say that a company is excellent. Generic praise carries little diagnostic value and can change from one answer to the next. The useful outcome is narrower: the right person asks about a real problem, and the system can connect that problem to a brand using specific, intelligible evidence. This also explains why Igor rejected courses that merely renamed SEO concepts. His instruction from the stage was \u201cForget about keywords.\u201d He was not declaring search advertising obsolete. He was separating two jobs. Keywords help organise demand and campaign reach in a search-ad system. AI visibility asks whether scattered public information resolves into a stable identity, a recognised field of work and defensible reasons for a recommendation. The difference is between matching a query to a page and synthesising a person or brand for a decision.<\/p>\n\n\n\n

    What changed after a year<\/h3>\n\n\n\n

    The experiment lasted about a year, and most of that period did not produce a dramatic visible change. Igor kept checking several AI systems and initially received versions of the same shallow answer. Unlike a page submitted through a search console, the material could not simply be pushed into an AI system on demand. The shift became visible after a major ChatGPT update: the answer expanded into a much richer professional account and began to support recommendations. That delay is part of the case, not an inconvenience to edit out. It means the work has an uncertain feedback cycle, and no one can responsibly promise a fixed time to recognition. It also makes monitoring essential. The meaningful checkpoints are not publication volume by itself, but whether neutral questions produce more accurate descriptions, whether the intended field of expertise is understood, and whether real prospects mention AI as the route by which they arrived.<\/p>\n\n\n\n

    The talk explained that Igor had built a five-part authority system, but the durable lesson sits above its mechanics. He first defined the knowledge territory in which he wanted to be recognised, then made the public evidence around his name deeper and more coherent. Facts across sources were meant to complement one another rather than turn every profile into the same slogan. Content was judged not only by whether a person might click it, but by whether it gave a model concrete material to connect with the entity. The result was the before-and-after demonstrated in the session: two generic lines at the start, a detailed account and relevant recommendations later, followed by dozens of warm enquiries in a month. This does not isolate the effect of every intervention from the effect of the model update. It does show a practical hypothesis worth testing: coherent, fact-rich evidence can improve how an AI system understands and recommends a specialised brand.<\/p>\n\n\n\n

    The full recording of this talk, including the five-part system, audit prompts and detailed audience discussion, is part of our bonus pack for students.<\/p>\n\n\n\n