The post has been translated automatically. Original language: Russian
Companies have been investing in digitalization for years, implementing CRM, ERP, BI platforms and monitoring systems. It seemed that the more data a business collected, the easier it was for it to make decisions. But today it is becoming obvious that the availability of data does not guarantee effective management.
Almost every modern company knows how to build reports. The manager can find out the sales volume, average receipt, number of orders, conversion rate or financial indicators for any period in a few seconds.
But a legitimate question arises:
If data is available to everyone, why are some companies growing faster than others?
The answer is that most organizations have learned how to collect information, but have not yet learned how to turn it into management decisions.
When BI stops helping
Imagine a typical executive morning.
There are dozens of graphs on the screen.:
- sales by branches;
- number of new clients;
- average check;
- remnants of goods;
- employee effectiveness;
- loading the contact center;
- execution of plans.
Everything looks beautiful.
But the main question arises:
What should I do with this information?
Let's say sales decreased by 12%.
Is this a problem?
Maybe.
But BI does not explain:
- why there was a decrease;
- what process caused the problem?;
- who caused it;
- how will this affect profits in a week;
- what actions need to be performed today.
It turns out to be a paradoxical situation: there is more and more data, but there is no less uncertainty.
From reports to recommendations
There has been a major shift in recent years.
If earlier analytics answered the question:
"What happened?"
Today, the business expects answers to completely different questions.:
- Why did this happen?
- What will happen next?
- What are the risks?
- What needs to be done right now?
That is why analytical systems are gradually becoming intelligent assistants to the head.
Artificial intelligence is able not only to process millions of records, but also to find patterns that are difficult for a person to notice on their own.
Let's imagine a regular contact center
The classic BI system will show:
- number of calls;
- average talk time;
- the speed of the operators' response;
- the number of missed requests;
- employee rating.
Is it useful?
Definitely.
But AI analytics goes much further.
It automatically analyzes the content of conversations and is able to identify:
- what questions do clients ask most often?;
- why do customers refuse to buy;
- does the operator follow the communication script;
- how emotional is the conversation;
- what promises were made to the client;
- has the employee completed the required actions;
- which topics are starting to repeat more often than usual.
In fact, the manager no longer needs to listen to thousands of recordings — the system itself identifies problem areas and shows them in an understandable way.
A similar situation occurs in production
The usual report shows:
- how many products are manufactured;
- how many orders have been completed;
- how many employees worked in the shift.
AI analysis is able to see much more.
For example:
- one production site regularly becomes a bottleneck;
- semi-finished products run out earlier than estimated time;
- a certain shift is slower than the others;
- some of the equipment starts to work with deviations;
- delays in orders appear long before deadlines are violated.
Instead of reacting to a problem, the business gets the opportunity to prevent it in advance.
AI begins to understand not only the numbers, but also the meaning.
This is one of the main differences between modern technologies.
Previously, analytical systems worked exclusively with structured data:
- tables;
- sales;
- leftovers;
- financial indicators.
Today, artificial intelligence is able to analyze almost any information.:
- phone conversations;
- correspondence between employees;
- customer requests;
- reviews;
- documents;
- email address;
- internal regulations.
That is, not only the numbers are analyzed, but also the context.
That is why completely new opportunities are emerging.
For example:
- automatically identify the causes of negative reviews;
- identify violations of internal processes;
- find recurring customer issues;
- detect a decrease in the quality of service even before complaints appear.
The biggest mistake companies make
Many people continue to believe that the digital transformation ends after the introduction of the next information system.
In practice, this is just the beginning.
Without proper analytics, even the most modern CRM or ERP is gradually turning into a huge repository of information that almost no one uses.
The amount of data is growing every year.
The number of decisions taken is not.
That is why today the main competitive advantage is no longer technology per se, but the company's ability to quickly extract meaning from information.
What will be the next stage of analytics development?
In the coming years, the role of analytical systems will change even more.
The supervisor will stop opening dozens of reports.
Instead, the system will generate daily recommendations by itself.
For example:
Today, it is worth paying attention to branch No. 3 — the waiting time for customers has increased by 27%.
The number of refusals after consultation increased by 18%. The main reason is the lack of the desired product.
Operators have become less likely to offer related services, which has already affected the average receipt.
The AI found repeated complaints about the speed of delivery in one region.
Such conclusions allow us to move from analyzing the past to managing the future.
Instead of a conclusion
We are entering a stage where the winners are not those companies that collect more data, but those that turn information into actions faster.
BI will remain an important tool. But its role is gradually changing: from visualization of indicators to intellectual decision—making support.
It is in this direction that global analytics is developing today. Artificial intelligence does not replace the head and does not make decisions for him. It takes over the routine analysis of large amounts of data, helps to see the relationships and suggests options for action.
In a world where there is more and more information, it is no longer the data itself that creates value, but the ability to understand its meaning and make timely decisions.
Когда несколько лет назад начали говорить о Shadow IT, большинство компаний решало одну задачу — как взять под контроль несанкционированные сервисы и приложения, которые сотрудники использовали без согласования с IT-отделом.
Сегодня ситуация повторяется, только в новом масштабе.
Разница лишь в том, что вместо облачных хранилищ или мессенджеров сотрудники используют искусственный интеллект.
И часто об этом никто не знает.
Почти невозможно определить момент, когда AI становится частью рабочего процесса. Это происходит незаметно. Разработчик просит модель объяснить ошибку в коде. Аналитик загружает требования, чтобы быстрее подготовить документацию. Маркетолог просит помочь с коммерческим предложением. HR редактирует описание вакансии.
Каждый отдельный случай кажется безобидным.
Но если посмотреть шире, возникает закономерный вопрос: какие данные уже покинули пределы корпоративной инфраструктуры?
Парадокс заключается в том, что большинство компаний обсуждает внедрение AI на уровне стратегии, тогда как сотрудники давно используют его в ежедневной работе. Просто делают это самостоятельно.
Запретить такие инструменты практически невозможно. Даже если доступ к одному сервису ограничен, через неделю появляется другой. Или сотрудник открывает его с личного устройства.
Поэтому проблема уже не в самом искусственном интеллекте.
Проблема в отсутствии понятных правил его использования.
На мой взгляд, ближайшие несколько лет станут периодом, когда компаниям придется создавать не только AI-стратегию, но и AI-культуру.
Она начинается с простых вопросов.
Какие данные можно передавать внешним сервисам?
Какую информацию категорически нельзя использовать в публичных AI-моделях?
Кто отвечает за проверку результатов, полученных с помощью искусственного интеллекта?
И где проходит граница между эффективностью и рисками?
В какой-то момент многие организации поймут, что вопрос уже не в том, использовать AI или нет.
Этот выбор сотрудники сделали самостоятельно.
Настоящий вызов — научиться использовать такие инструменты безопасно, осознанно и с пониманием их ограничений.
Возможно, именно поэтому через несколько лет термин Shadow AI станет таким же привычным, каким сегодня является Shadow IT. И чем раньше компании начнут обсуждать эту тему открыто, тем меньше проблем им придется решать в будущем.
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