By Vladimir Akimov*
More and more often, offices resemble not glass towers, but living organisms. Each department has its own “brain,” every project — its rhythm and pulse. But who manages these rhythms today? It increasingly seems that artificial intelligence is becoming not just a tool, but a kind of “nervous system” of the holding. It connects such, at times chaotic, branches as finance, marketing, logistics, HR, and production into a single stream of data, decisions, and actions.
Artificial intelligence efficiency is no longer just an abstract idea from laboratories. AI in business is the main engine of productivity that allows holdings to be faster, more precise, and smarter than competitors. But there is an important nuance — for AI to truly improve life, its use must be approached with great responsibility.
As a manager or investor, you have probably noticed: the larger the company, the more complicated the movement of information between departments. AI eliminates this barrier — it turns the holding into a synchronised mechanism where every “gear” knows what to do, without unnecessary meetings, Excel sheets, or delays. Let’s explore which processes in a holding AI can not only optimise but also elevate to a new qualitative level.
Data management and decision-making
Let’s start with the heart of any structure — data. Holdings operate thousands of reports, forecasts, and KPIs. Here AI in business operations acts as an analyst who never sleeps. It unites scattered databases, cleans information, identifies patterns, and suggests development scenarios.
For example, a machine learning system can predict financial risks for subsidiaries long before they appear in accounting reports. Or propose reallocating resources based not on intuition or managerial gut feeling, but on overall market behaviour patterns.
Instead of classic “reactive” management, the holding receives a preventive model of decision-making—and this radically changes efficiency and performance. Such digital transformation for holdings creates the foundation for more precise and faster management in real time.
Finance and audit: AI as a pill for chaos
Financial flows in holdings form an extremely complex network. AI automation in enterprises simplifies it into understandable schemes. Automated systems recognise transactions, detect anomalies, and analyse whether expenses comply with company policy.
With neural networks, one can view cash flow in real time, model budgets, and manage liquidity.
AI can also track micro-signals of potential financial abuses, helping compliance departments act proactively rather than reactively. In other words, artificial intelligence efficiency turns finances into a transparent ecosystem — everything under control, without loss of speed or flexibility.
Operational processes: From routine to automation
Operational efficiency is one of the most “painful” points for large holdings. How AI transforms business is especially visible here: it can completely rebuild production chains — from material procurement to logistics and servicing.
Imagine a system based on IoT sensor data predicting equipment failure before a painful downtime occurs. This is not about replacing humans, but about coordination on a scale where human error is eliminated and decision-making speed increases manyfold.
Human resource management: HR speaks the language of data
In large structures, the human factor is decisive. Here AI in business operations is not a joke but a real example: AI helps make this factor manageable. Modern HR platforms with AI analyse resumes, assess soft skills, and predict employee success based on behavioral models.
They can propose the ideal team for a project or identify employees approaching burnout. AI also transforms corporate learning — selecting courses and materials individually, turning training into a personalised development process.
Client and partner interactions
For holdings with multiple business directions, AI becomes a bridge between them and the ever-changing external environment. It analyses feedback, automates communications, and builds predictive models of client and partner behaviour.
Based on analysis of thousands of contracts, AI can suggest optimal terms for each new deal. For example, AI in business operations already demonstrates how algorithms can not only analyse user experience but also propose loyalty and retention scenarios.
In marketing, AI builds a unified, coordinated communication strategy for all brands of the holding, synchronising the company’s voice. Naturally, these are only suggestions — a human must refine them, taking into account emotional factors and applying the proposals of high-performing AI to specific contexts.
Corporate governance and strategic planning
AI becomes the support system for strategists. It allows simulation of dozens of development scenarios for a group of companies, assessment of regulatory impact, or even forecasting of merger outcomes before documents are signed.
When every number and document is processed instantly, the board of directors receives not just analytics, but a strategic map of the future. It’s much easier, more pleasant, and more comfortable to navigate by such a map in a complex corporate structure than to move blindly.
Artificial intelligence efficiency helps holdings not chase the fast-moving market but set its rules.
How to implement AI in a holding’s processes wisely: Steps toward transformation
Artificial intelligence can radically change a holding’s work — but only if its implementation is a well-thought-out strategic process, not a chaotic experiment.
Those who believe that “adding AI” automatically makes a company efficient are mistaken. In practice, uncontrolled use of technology without a clear data architecture, ethical framework, and human oversight leads not to efficiency but to digital collapse — distorted analytics, conflicting algorithmic decisions, and loss of manageability and trust.
AI is not a replacement for management but its amplifier. Therefore, implementation must proceed top-down, step by step, building a sustainable ecosystem — the foundation of digital transformation for holdings.
- Strategy Begins Not with Technology, but with Purpose
The first rule of responsible implementation is to answer the question “why”. If a holding wants to “follow a trend”, the result will be the opposite. AI must solve a specific problem — optimise costs, shorten decision-making time, or improve demand forecasting. Without a strategic goal, any machine learning system becomes an expensive toy with unpredictable outcomes.
- Data Architecture as the Foundation
AI feeds on data. If data is inaccurate, incomplete, or unsynchronised across departments, algorithms will generate false insights. In holdings, this is critical: subsidiaries often operate on incompatible platforms. That’s why it’s essential first to build a unified digital space — where all data is transparent, validated, and regularly updated. Without this, AI will make decisions “in the fog”.
- Human Oversight and Algorithm Ethics
In the AI era, humans remain the final filter of common sense. Algorithmic decisions must be explainable and verifiable, especially in finance, HR, or resource distribution. If a holding fully delegates control to machines, it risks facing situations where the system acts formally correctly but substantively disastrously. Ethical control and periodic auditing of machine learning models are key components of mature digital culture. AI must be an assistant, not a soulless arbiter.
- Training and Team Acceptance
Even the best algorithm brings no benefit if employees fear or misunderstand it. Therefore, AI implementation must be accompanied by training and communication. It’s important to explain that technology does not take jobs away—it makes them more effective. Managers must know how to read reports, interpret models, and use them for decision-making. Holdings that invest in digital literacy gain not just an innovative tool but a new management culture where humans and algorithms work in tandem.
- Constant Audit, Feedback, and Adaptation
AI learns, evolves, and can make mistakes. Therefore, wise implementation requires continuous monitoring — regular model review, data updates, and efficiency analysis. The holding must have an internal “digital audit” mechanism to detect and fix issues before they become crises. Moreover, one must not fear disabling or reworking a model if it becomes irrelevant. Digital resilience means not blind faith in technology, but the ability to recalibrate it in time.
In conclusion — AI as corporate capital
AI is not just innovation but a new mindset within the holding. It unites disparate links into a single ecosystem where data becomes fuel and reaction speed — a competitive advantage. Today, a holding that integrates AI into its DNA will tomorrow become the one dictating the market’s tempo. And the question is no longer when to start, but how quickly are you ready to change?
*Chief Product Officer / CPO / Product Director in iGaming.
