Code of Leadership S2E17: How Can a Mid-Sized Business Use AI When Doing It ‘By the Book’ Is Too Expensive? (#AI)

Mid-sized businesses face an uncomfortable fork in the road with AI. A lab, a platform, and a team of scarce specialists make for an expensive entry ticket. But personal subscriptions, disconnected demos, and one “AI wizard” working in the evenings do not yet add up to an operating practice.

Today at 17:00, we will host a livestream and discuss this with Alexander Vorontsov, a partner at @revelio_tech and author of the AI Subjects channel (@aisubjects, which studies cognitive biases in working with AI). In our previous conversation, we reached an important point: the external expert will leave, and someone inside the company must remain who understands the problem, verifies the result, and carries the change forward. This time we will examine how to acquire and retain that capability when the strongest people are busy with their main jobs and it is too early to hire an AI department.

We will discuss: — how to distinguish an AI task from a broken process, poor data, and management debt; — how to choose the first use case, record a baseline metric, and define acceptance and stop criteria in advance; — whom to develop internally, whom to hire or borrow from the market temporarily, and what to buy off the shelf; — how to free up a domain expert's time without turning AI into their second shift; — what is needed beyond accounts: access, test examples, an error log, manual approval, and rollback; — how to calculate the entry ticket: licenses, integrations, internal time, quality control, support, and the cost of errors; — when a pilot that employees like should still be shut down.

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