{"id":5090,"date":"2026-08-09T18:36:24","date_gmt":"2026-08-09T15:36:24","guid":{"rendered":"https:\/\/ivitskiy.com\/blog\/london-business-club-2025\/"},"modified":"2026-08-09T18:36:24","modified_gmt":"2026-08-09T15:36:24","slug":"london-business-club-2025","status":"publish","type":"post","link":"https:\/\/ivitskiy.com\/blog\/en\/london-business-club-2025\/","title":{"rendered":"The AI Era in Marketing and Business: Igor Ivitskiy at London Business Club 2025"},"content":{"rendered":"\n
In short:<\/strong> At London Business Club, Igor Ivitskiy described how a year of experimentation changed his view of AI from a novelty into a practical layer for thinking, company knowledge, client research and marketing. His central conclusion was that useful adoption starts with business context and human judgement, not with a complicated stack of integrations. A model-independent set of company documents let him move between AI systems without losing accumulated knowledge. In marketing, the same approach led to regular inbound enquiries from people who said an AI assistant had recommended him.<\/div>\n\n\n\n

London Business Club took place in London on November 19, 2025, as an in-person business talk and discussion. The speaker was Igor Ivitskiy<\/a>, an entrepreneur and former researcher in mathematical modelling.<\/p>\n\n\n\n

The talk, The AI era in marketing and business<\/strong>, followed Igor\u2019s own year-long path: an unsuccessful attempt to automate too much, a simpler way to preserve company context, specialised AI advisers for different business roles, customer models for reviewing ideas, and a new view of search, advertising and brand visibility.<\/p>\n\n\n\n

\u22482 a week<\/div>
inbound enquiries from people who said an AI assistant had recommended Igor, according to the talk<\/div><\/div>\n
Source: London Business Club talk, Igor Ivitskiy, November 19, 2025.<\/div>\n\n\n\n

The useful layer is context, not automation theatre<\/h2>\n\n\n\n
\"Owner
Owner connected to ads, AI and growth blocks<\/figcaption><\/figure>\n\n\n\n

Igor\u2019s first attempt followed the familiar integration route: connect AI, customer systems and automation tools, then let data move through an elegant diagram. In practice, connections failed, maintenance invoices kept arriving and a client-facing bot invented promises. The experience changed the question. Instead of asking how much of the company could be automated, he began asking where AI could improve a decision while a person still owned the outcome.<\/p>\n\n\n\n

The answer was a deliberately lighter architecture. Company context, audience knowledge, communication principles and operating constraints live in documents controlled by the business. An AI system processes that context for a defined role, but it is not the only place where the knowledge exists. This matters in marketing because useful output depends on the same discipline as sound keyword research<\/a>: the quality of the evidence and the frame determines what can be learned from it.<\/p>\n\n\n\n

The same logic extends to advertising data. Google describes website visitors and app users as part of an advertiser\u2019s own data segments<\/a>. Igor\u2019s broader point was strategic: when platforms have less observable context and more automated decision-making, the business must become more deliberate about the information it owns and the outcomes it sends back.<\/p>\n\n\n\n

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1<\/span>Think<\/strong>
Role-specific advisers examine strategy, finance and marketing from different angles.<\/div>\n
2<\/span>Remember<\/strong>
Company-owned documents preserve context outside any single model or chat.<\/div>\n
3<\/span>Listen<\/strong>
Customer evidence is turned into models that challenge internal assumptions.<\/div>\n
4<\/span>Learn<\/strong>
Marketing results and failed tests become reusable organisational knowledge.<\/div>\n<\/div>\n
Source: framework presented at London Business Club by Igor Ivitskiy.<\/div>\n\n\n\n

My principle<\/strong><\/p>

I keep business context in documents that I control and use AI as a processor of that context. This lets me change models without rebuilding the company\u2019s memory and keeps a person responsible for every client-facing decision.<\/p><\/section>\n\n\n\n

This is also the approach we teach: understand the business problem, preserve the evidence and evaluate the output before expanding its role. The training goes deeper into the working method without treating AI as a substitute for judgement.<\/p>\n\n\n\n\n\n\n\n

Key insights<\/h2>\n\n\n\n