Александр Данильянц – THE HUMAN FACTOR IN AN ALGORITHMIC WORLD (страница 2)

In the 2020s, many retailers who fully entrusted inventory management to AI faced collapse. Algorithms could not predict changes in consumer behavior caused by global stress. They ordered goods that were needed "yesterday" and ignored what became needed "today." Humans saved the situation. Managers who turned off automatic orders and called suppliers, relying on intuition and news rather than reports.

Context vs. Content

The algorithm sees content. The human sees context.

In marketing, which I engage in through the "Digital Action" agency and the RankBoost project, this distinction is critical. A neural network can write perfect text from an SEO perspective. It will insert keywords, follow structure, maintain tone. But it doesn't know that yesterday your competitor had a scandal, and now any mention of that topic triggers aggression in the audience. It doesn't know that your client is currently going through a crisis and doesn't need "aggressive selling" but a sense of security.

I recall a case from my Luxoft practice where I managed procurement. We were selecting a system for automatically distributing developers' tasks. The vendor's algorithm was flawless: it assessed task complexity, employee qualification, their workload, and deadlines. It distributed tasks to maximize speed.

The result? Productivity dropped. People burned out.

Why? Because the algorithm didn't account for the human factor. It didn't know that developer Ivanov was going through a difficult divorce and shouldn't be given stressful tasks, even though his skills were perfect. It didn't know that Petrov and Sidorov were in a quarrel, and putting them on the same team guaranteed conflict.

When we included a requirement for a "human filter" from team leads in the contract, efficiency returned. The machine provided the optimal scheme, the human adjusted it for reality.

Boundaries of Applicability

It's important to understand: I am not calling for abandoning data. I am calling for ceasing to deify it. Data is a tool, like a hammer. You can build a house with a hammer, or you can smash your finger. The problem is not the hammer, but who wields it.

In business, there are zones where data reigns:

Logistics and Supply Chains. Variability is low, physics is predictable.

Financial Reporting. Numbers don't lie if they aren't intentionally distorted.

Mass Personalization. Recommendation systems ("you might also like") work perfectly because the cost of error is low.

And there are zones where data is powerless without a human:

Strategic Vision. Data cannot invent a new market. It can only optimize an existing one.

High-Level Negotiations. Arguments don't decide the outcome here; chemistry between personalities does.

Crisis Management. When there are no rules, improvisation is needed.

The "Average Temperature" Error

Statistics knows the concept of "average hospital temperature." If one patient has a fever of 104°F and another 97°F, on average they have a normal temperature. Algorithms often operate with average values. They optimize processes for the "average user," "average employee," "average customer."

But in business, money is made on deviations. Your most loyal customers are a deviation. Your most talented employees are a deviation. Your riskiest and most profitable deals are a deviation.

If you entrust business management to an algorithm, it will start "trimming" deviations, bringing everything to a gray middle. This is the path to stagnation.

The human factor lies in the ability to see value in the unique, the non-standard, in what breaks the pattern. Innovation always looks like an anomaly in the data until it becomes mainstream.

Case Study: When the Instruction Kills

In the heavy industry where I began as a specialist, safety is a religion. There are thousands of pages of regulations. Every step is prescribed. This is necessary to avoid injuries. But I have seen the other side of the coin.

An employee sees a potential problem not described in the regulations. But he is afraid to deviate from the instructions. "I'm not paid for initiative, I'm paid for compliance," he thinks. As a result, a minor malfunction escalates into an accident.

Algorithmic personnel management works the same way. KPIs, metrics, deadlines. The employee becomes a function. He stops thinking about the company's welfare; he starts thinking about how to "game" the system to meet the metric.

I've seen sales managers who, to fulfill their call quota (tracked by CRM), called clients with questions that annoyed rather than helped. Metric met. Client lost.

Implementing AI in personnel management without considering psychology leads to rebellion or quiet sabotage.

What to Do?

The "Human-in-the-Loop" Principle. Never give the final decision on critically important matters to the machine. AI should propose options, the human chooses.

Data Audit for Bias. Understand what your model was trained on. If you hire people through AI, check if it discriminates against certain groups based on historical hiring data.

Encourage Deviations. Create a culture where an employee has the right to say "the algorithm is wrong" and is not punished for it but thanked if they turn out to be right.

Develop Critical Thinking. In an era where answers can be obtained in seconds, the question becomes more important than the answer. Learn to ask "Why?" and "What if?".

Data gives us a map. But only a human can decide where we want to go. The map won't show the beauty of the landscape waiting for us along the way. The map won't tell us if the game is worth the candle. That decision requires a soul.

Chapter 2. THE DEATH OF ROUTINE INTELLIGENCE

We are used to being proud of our intelligence. The ability to memorize facts, calculate quickly, know foreign words, operate with formulas – all this was considered a sign of intelligence. In school and university, we were taught to be living hard drives.

But let's be honest: in storing and processing information, humans lose to a 90s calculator, let alone modern cloud storage.

What we called "intelligence" in the 20th century is becoming a "routine operation" in the 21st. If your job can be described by the algorithm "If A, then B," it will be automated. This is not a question of the future, but of the coming years.

The Expert's Comfort Zone

The biggest danger for a modern specialist is becoming an expert in something easily copied.

I have seen lawyers who spent years drafting standard contracts. They prided themselves on their speed and knowledge of nuances. Today, a neural network does it in 30 seconds, finding precedents worldwide.

I have seen analysts who built pivot tables in Excel. Today, an AI assistant does it by voice command.

Who are they now? If they haven't changed, they have become neural network operators. And an operator's salary is lower than an expert's.

But there is good news. The death of routine intelligence gives birth to creative and strategic intelligence.

Three Pillars of Human Indispensability

What remains for us? Three spheres where algorithms (at least in the foreseeable future) cannot surpass us.

Generation of Meaning (Why?)

AI can answer the question "How?". How to increase sales? How to optimize code? How to reduce costs?

But AI cannot answer the question "Why?". Why do we need this business? What pain of the world are we healing? What is our mission?

Meaning is human territory. People don't buy drills; they buy holes in the wall. But even deeper: they buy the feeling of confidence that the shelf won't fall. AI can sell a drill based on specs. A human sells confidence through a story, through trust in the brand.

Synthesis of the Unconnected (Creativity)

AI works by combining what already exists. It doesn't create anything fundamentally new; it mixes patterns.

Humans are capable of insight. Of connecting things that logically shouldn't be connected.

Steve Jobs connected calligraphy and computers. No one asked him to do this from a data standpoint. It was an intuitive leap.

Responsibility (Who is to Blame?)

This is the most important point. An algorithm cannot bear responsibility. You can't put it in jail, you can't fire it in disgrace, it feels no shame.

In business and society, there must always be a person who says: "I decide. I am responsible."

The ability to take risks and bear responsibility for them is the highest form of human capital. The more complex the world, the more valuable are people ready to say "I'll take this on."

Transformation of Education

If routine intelligence is devalued, then the education system must change. Memorizing dates and formulas loses meaning. It's all on your smartphone.

What needs to be taught?

• Learning to learn. The skill of quickly adapting to new tools.

• Philosophy and Ethics. To understand the consequences of technology.

• Communication. The ability to negotiate, persuade, inspire.