Introduction: why AI in operations management is no longer the future, but the present

Artificial intelligence (AI) has ceased to be an abstract technology from scientific laboratories. Today, it is becoming a central tool for companies seeking a competitive advantage through the optimization of operational processes. According to the McKinsey Global Institute, by 2025 AI technologies will already be generating more than $13 trillion in added value for the global economy, and a significant share of this effect falls precisely on operations management.

For Russian entrepreneurs, this trend is becoming especially relevant. Under sanctions pressure, import substitution, and the need to improve internal business efficiency, AI solutions in operations management open up opportunities to reduce costs by 15–40%, accelerate decision-making, and reach a new level of product and service quality.

“Operational excellence is not just about cutting costs. It is about creating a system that continuously learns and adapts.” — Satya Nadella, CEO of Microsoft

In this article, we will examine in detail what AI in operations management is, what specific tasks it solves, present international and Russian implementation cases, provide a step-by-step action plan, and answer the key questions entrepreneurs have.

What AI in operations management is: definitions and key concepts

Operations management covers all processes related to the design, management, and improvement of systems that create and deliver a company’s products or services. This includes supply chain management, production, logistics, quality control, equipment maintenance, and customer service.

Artificial intelligence in operations management is the application of machine learning, natural language processing, computer vision, and predictive analytics technologies to automate, optimize, and make operational processes more intelligent.

Key AI technologies in operations management

“Data is the new oil, but AI is the engine that turns data into action.” — German Gref, head of Sberbank

Key areas of AI application in operations management

1. Demand forecasting and inventory management

Accurate demand forecasting is one of the most complex and at the same time one of the most in-demand tasks in operations management. Forecasting errors lead to two problems: excess inventory (frozen capital) or product shortages (lost revenue).

Traditional forecasting methods, such as moving averages or exponential smoothing, take into account a limited set of factors. AI systems, by contrast, are capable of analyzing dozens and hundreds of variables simultaneously: seasonality, weather, holidays, competitors’ actions, macroeconomic indicators, social media trends, and the purchase history of each customer.

Example: Walmart (international case)

Walmart uses a machine learning platform to forecast demand in more than 11,000 stores. The system processes petabytes of transaction data and takes into account more than 100 external factors. As a result, the company reduced excess inventory by 10–15% and simultaneously cut product shortages by 30%.

Example: X5 Group (Russian case)

X5 Group, Russia’s largest grocery retailer (which operates the Pyaterochka, Perekrestok, and Chizhik chains), has implemented an AI system for demand forecasting and automatic order generation. According to pilot project results, the system reduced the write-off volume of perishable products by 20% while also improving product availability on shelves. For a retailer with revenue of more than 3 trillion rubles, this amounts to billions of rubles in annual savings.

2. Equipment predictive maintenance (Predictive Maintenance)

Unplanned equipment downtime is one of the most expensive types of losses in manufacturing. According to a Deloitte study, AI-based predictive maintenance can reduce unplanned downtime by 35–45%, cut maintenance costs by 10–25%, and extend equipment service life by 20–40%.

AI predictive maintenance systems collect data from equipment sensors (vibration, temperature, pressure, acoustics) and identify patterns that precede failures. This makes it possible to repair equipment before an incident occurs, rather than after.

Example: Siemens (international case)

Siemens deployed a predictive maintenance system for its gas turbines. The algorithms analyze more than 500 parameters of each turbine in real time and forecast maintenance needs with up to 95% accuracy. This enables the company to prevent failures costing millions of dollars and optimize maintenance schedules.

Example: Severstal (Russian case)

Severstal, one of Russia’s largest metallurgical holdings, is actively implementing AI solutions in production. The company uses machine learning models for the predictive maintenance of rolling mills and blast furnaces. According to the company, implementing the system made it possible to reduce the number of unplanned shutdowns by 20% and lower energy resource consumption. The company also applies AI to optimize metallurgical formulations, which improves product quality and reduces production costs.

3. Supply chain and logistics optimization

Supply chains are one of the most complex systems in business. They involve many participants, stages, geographic locations, and variables. AI is capable of handling this complexity and finding optimal solutions that are beyond human analysis.

Key AI tasks in supply chain management include: optimizing delivery routes taking into account traffic, weather, and fuel costs; automatically selecting suppliers based on multi-criteria analysis; inventory management across distributed warehouses; forecasting and managing the risks of supply disruptions.

Example: Amazon (international case)

Amazon is one of the world leaders in applying AI in logistics. The company uses AI to optimize the placement of goods in warehouses (anticipatory shipping — the product is sent toward the customer even before the order is placed), last-mile routing, and management of the robot fleet in fulfillment centers. More than 750,000 robots operate in Amazon warehouses, controlled by AI systems, which has cut order processing time in half.

Example: "Yandex.Routing" (Russian case)

Yandex offers Russian businesses the Yandex.Routing service, which uses AI algorithms to optimize delivery routes. The service takes into account traffic jams, delivery time constraints, vehicle capacity, and other parameters. Customers of the service report a 10–20% reduction in logistics costs and a 15–25% increase in the number of deliveries per vehicle. Users include restaurant chains, delivery services, and online stores.

4. Quality control using computer vision

Visual quality control in manufacturing is traditionally performed by people, which is associated with fatigue, subjectivity, and limited speed. AI-based computer vision systems are capable of detecting defects with accuracy above 99%, operating around the clock, and processing thousands of products per minute.

Example: BMW (international case)

At BMW plants, computer vision systems inspect the quality of body paint, the precision of component assembly, and the quality of weld seams. AI detects microscopic defects invisible to the human eye and reduces the number of defective parts that pass inspection by 40%.

Example: KAMAZ (Russian case)

KAMAZ is implementing computer vision elements on assembly lines to control the quality of large assemblies. The company is also actively developing a self-driving truck project, using computer vision technologies for navigation and safety. In addition, KAMAZ facilities use digital twins of production processes, which simulate and optimize conveyor operations with the help of AI.

5. Customer service and internal process automation

Generative AI and chatbots have radically changed the approach to customer service. Modern AI systems are capable not only of answering standard questions, but also of understanding context, working with documents, escalating complex cases, and learning from each interaction.

Inside companies, AI automates document processing, application approvals, HR process management, report generation, and many other routine tasks. According to McKinsey, up to 60–70% of work tasks in operational functions can be partially or fully automated with AI.

Example: Tinkoff (Russian case)

Tinkoff is one of the leaders in applying AI in Russian business. The company's chatbot handles more than 85% of customer inquiries without an operator. AI is also used for credit application scoring, automatic generation of personalized offers, and detection of fraudulent transactions. According to the company, automation has significantly reduced operating costs while the customer base has grown.

6. Workforce management and resource planning

AI is transforming human resource management in the context of operations management. Algorithms optimize work scheduling, predict employee turnover, automate recruiting, and analyze performance.

Example: Unilever (international case)

Unilever uses AI in recruitment: algorithms analyze video interviews, assess soft skills, and select candidates. The company stated that hiring time was reduced by 75%, and the diversity of hired employees increased by 16%.

Example: Magnit (Russian case)

The Magnit chain uses AI solutions to automatically create work schedules for store employees, taking into account predicted customer traffic, vacations, sick leave, and labor law requirements. This helps reduce staffing shortages during peak hours and cut payroll costs through more accurate planning.

Economic impact: figures and facts

Investments in AI for operations management pay off faster than in many other functional areas because the effect is measurable, repeatable, and scalable. Below are aggregated data from studies by leading consulting firms.

"Companies that implement AI in operational processes achieve a 3–5 percentage point increase in profitability in the first two years." — McKinsey report "The state of AI", 2024

For the Russian market, these figures are especially significant. In an environment where the margins of many businesses are under pressure, reducing operating costs by 10–20% can mean the difference between profitability and losses.

Russian specifics: opportunities and limitations

Government support and regulation

The Russian government actively supports AI development. Within the National Strategy for the Development of Artificial Intelligence until 2030 (approved by decree of the President of the Russian Federation), support measures for businesses are provided, including grants for the development and implementation of AI solutions, tax incentives for IT companies, the creation of regulatory sandboxes, and support for domestic platforms.

The Skolkovo Foundation, the Innovation Assistance Fund, FRP (Industrial Development Fund), and other development institutions offer grants and concessional loans for AI-related projects. Entrepreneurs can use these tools to reduce implementation costs.

Domestic AI platforms and solutions

After several Western vendors left the Russian market, domestic solutions received a powerful boost for development. Today, Russian companies offer competitive AI platforms for business.

Key barriers to implementation in Russia

• Shortage of qualified personnel. According to hh.ru, the number of vacancies in the AI and ML field in Russia has grown by 40% over the past two years, while the number of specialists is not keeping up with demand.
• Data quality. Many Russian companies do not systematically collect and store data. Without high-quality data, AI models cannot be trained or produce accurate forecasts.
• Resistance to change. Middle managers and front-line staff may perceive AI as a threat to their jobs, which creates organizational resistance.
• Budget constraints. Small and medium-sized businesses (SMEs) often do not have the budgets for large-scale AI projects. However, the development of cloud services and SaaS solutions lowers the entry barrier.
• Information security issues. Companies are concerned about transferring data to cloud services, especially in light of the requirements of Federal Law 152-FZ on personal data.

Where to start: a step-by-step plan for implementing AI in operations management

Implementing AI is not about buying a "magic box." It is a strategic project that requires a systematic approach. Below is a step-by-step plan adapted to Russian realities.

Step 1. Conduct an audit of operational processes

Before implementing AI, you need to clearly understand the current state of your operational processes. Map out the processes: where bottlenecks arise, which tasks take the most time, where errors occur most often, and what data you are already collecting.

Practical tool: perform Value Stream Mapping for 3–5 key processes. Identify the stages that could potentially be automated or optimized with AI.

"You cannot automate chaos. First put your processes in order, then introduce technology." — Taiichi Ohno, creator of the Toyota Production System

Step 2. Identify priority tasks (Quick Wins)

Do not try to implement AI across all processes at once. Start with tasks that meet the criteria for high AI potential.

Typical quick wins for Russian business: automating responses to common customer questions (chatbots); demand forecasting and automatic replenishment of goods; automatic processing and classification of documents (invoices, acts, requests); delivery route optimization; equipment monitoring using IoT sensors and ML models.

Step 3. Ensure data quality

Data is the fuel for AI. If your data is incomplete, inaccurate, or fragmented, no model will deliver good results. Key actions at this stage include: data centralization (combine data from different systems: ERP, CRM, WMS, MES into a single repository); data cleansing (eliminate duplicates, gaps, and errors); standardization of formats (common reference data, common units of measurement); organization of a continuous data collection process.

You do not need to build an expensive Data Lake right away. Start with simple tools: Google BigQuery, Yandex DataLens, ClickHouse (a Russian open-source DBMS created at Yandex) — all of them are suitable for the first steps.

Step 4. Build a team or find a partner

You have two main paths.

Path 1: Internal team. Hire or train a data scientist, an ML engineer, and a data analyst. This is more expensive at the start, but it gives you full control and the ability to iterate quickly. Suitable for companies with 200+ employees and an IT budget of 10 million rubles per year or more.

Path 2: External partner or cloud solutions. Use ready-made SaaS solutions or hire a specialized contractor. Suitable for SMEs. Among Russian integrators: "Digital Design", "Naumen", "DataFort", "Lanit", "CIAN".

Step 5. Launch a pilot project

A pilot is a limited-scale experiment that allows you to test a hypothesis with minimal risk. Recommendations for a successful pilot: choose one specific task and one KPI; limit the pilot to 2–3 months; secure management support; appoint a person responsible for the pilot (product owner); record the baseline (current metrics) before the pilot starts.

After the pilot, compare the "before" and "after" metrics. If the result is positive, scale it up. If not, analyze the reasons and adjust the approach.

Step 6. Scale and integrate

After a successful pilot, move on to scaling: roll out the solution to other processes, departments, or branches; integrate the AI system with your ERP, CRM, and WMS; set up model quality monitoring (models degrade over time and need retraining); create an AI center of excellence within the company.

Step 7. Train staff and manage change

Technology is useless if people do not know how or do not want to use it. Invest in training: show employees how AI helps them rather than replacing them; conduct a series of workshops and trainings; create internal AI ambassadors — enthusiasts who will promote the technology among their colleagues; include digital transformation goals in the KPIs of department managers.

"Culture eats strategy for breakfast." — Peter Drucker, founder of modern management

How to calculate the ROI of implementing AI in operations management

The issue of payback is critical for any entrepreneur. Let us look at the ROI calculation structure for AI projects in operations management.

ROI formula

ROI = (Benefit from implementation − Total cost of ownership) / Total cost of ownership × 100%

Benefit components

• Direct savings: reduction in operating costs, lower defect rates, reduced inventory, reduced equipment downtime.
• Revenue growth: higher sales due to better product availability, improved customer experience, and faster time to market.
• Strategic advantages: greater flexibility, faster decision-making, lower risks.

Cost components (TCO)

• Development or purchase of an AI solution (from 500 thousand to 50 million rubles, depending on scale).
• Infrastructure (cloud services or own servers): from 50 thousand to 2 million rubles per month.
• Data: collection, cleansing, labeling (often the most labor-intensive part).
• Staff: salaries of data scientists, ML engineers, analysts.
• Training and change management.
• Model support and updates.

Calculation example

Suppose a manufacturing company with a turnover of 2 billion rubles implements predictive maintenance. The cost of equipment downtime is 5 million rubles per day. The average number of unscheduled downtimes is 30 days per year. Calculation: the total cost of downtime is 150 million rubles per year; AI reduces downtime by 35%, saving 52.5 million rubles per year; the project TCO for the first year is 20 million rubles; the first-year ROI is (52.5 − 20) / 20 × 100% = 162.5%. The payback period is less than 5 months.

AI trends in operations management 2025–2026

1. Generative AI in operations

Large language models (LLM) are moving beyond chatbots and penetrating operational processes. They generate production plans, write technical regulations, analyze contracts, create code for industrial controllers, and compile reports based on data analysis results. In 2026, the mass adoption of AI agents is expected — autonomous systems capable of performing sequences of tasks without constant human supervision.

2. AI agents and autonomous systems

AI agents are programs that do not just respond to requests, but independently plan and execute actions to achieve a goal. In operations management, agents can automatically place orders with suppliers, reallocate resources between projects, respond to supply chain disruptions, and coordinate the work of multiple systems.

3. Edge AI — AI at the edge

An increasing number of AI models are being run directly on devices (sensors, cameras, industrial controllers) without connecting to the cloud. This reduces latency, improves data security, and ensures AI works even without an internet connection. For Russian enterprises, especially in remote regions, Edge AI opens up important opportunities.

4. Digital twins

A digital twin is a virtual copy of a physical object or process that is updated in real time based on sensor data. Combined with AI, digital twins make it possible to model and optimize production processes, test changes in a virtual environment before implementing them, and predict the behavior of complex systems. In Russia, the digital twin technology is being actively developed by Rosatom, Gazprom Neft, and Severstal.

5. Responsible AI (Responsible AI)

As the scale of AI adoption grows, the importance of ethical and legal aspects increases: transparency of algorithms, explainability of decisions, absence of discrimination, and data protection. Russian legislation in this area is evolving: in 2024, updated AI ethics guidelines were adopted, and requirements for the explainability of AI decisions are being discussed at the regulator level. Entrepreneurs implementing AI must take these requirements into account from the very beginning.

Checklist for business readiness to implement AI

Use this checklist to self-assess your company's readiness to implement AI in operations management.

Common mistakes when implementing AI and how to avoid them

Mistake 1: starting with technology rather than the business problem

Many companies buy an AI solution and then look for where to apply it. The right approach is to start with a business problem and understand whether AI can help solve it. Not every task requires AI: sometimes automation, process optimization, or simply getting the data in order is enough.

Mistake 2: underestimating the importance of data

According to experts, 70–80% of an AI project’s time is spent working with data: collection, cleaning, labeling, and preparation. If you are not ready to invest in data quality, the AI project is doomed.

Mistake 3: expecting immediate results

AI is not a silver bullet. The first results of a pilot may be modest. Models need training, calibration, and iteration. A realistic timeframe from project start to a tangible effect is 3–6 months for simple tasks and 6–12 months for complex ones.

Mistake 4: ignoring change management

A technology project without attention to people will fail. Invest in training, communication, and working through resistance. McKinsey research shows that 70% of digital transformation projects fail for organizational rather than technical reasons.

Mistake 5: lack of model monitoring

AI models degrade over time as data and conditions change (concept drift). It is necessary to establish a process for monitoring model quality and regularly retraining them. Without this, a model that produced excellent results a year ago may become useless today.

Expert opinions and authoritative sources

Leading global and Russian experts agree: AI in operations management is not a trendy buzzword, but a fundamental shift in how companies create and deliver value.

“AI will become as familiar a tool for a manager as a spreadsheet. Those who do not master it will be at a disadvantage.” — Andrew Ng, founder of DeepLearning.AI

“We are on the threshold of the fourth industrial revolution, where the physical and digital worlds merge into one. AI is a key technology of this transition.” — Klaus Schwab, founder of the World Economic Forum

“Artificial intelligence is a key tool for improving the efficiency of the Russian economy. We must not merely follow global trends, but create our own world-class solutions.” — Dmitry Chernyshenko, Deputy Prime Minister of the Russian Federation for Digital Economy

In addition to individual experts, pay attention to the following authoritative sources for an in-depth study of the topic: McKinsey “The state of AI” (annual report); Gartner Hype Cycle for Artificial Intelligence; Deloitte “AI in Manufacturing”; the National AI Development Strategy in Russia until 2030; reports by the Center for Strategic Research (CSR) on AI; materials from the AI Journey conference (held annually by Sber).

Conclusion: time to act

AI in operations management is not a question of “if,” but of “when.” Companies that start implementing AI today gain a competitive advantage that will only strengthen over time. Those who put it off risk finding themselves in the position of those chasing the leaders.

For Russian entrepreneurs, the window of opportunity is open right now. Government support, the development of domestic platforms, the availability of cloud services, and a growing pool of specialists are creating favorable conditions for getting started.

Remember: there is no need to implement AI for the sake of AI. Start with a business problem, ensure data quality, launch a pilot, measure the result — and scale what works. Step by step, project by project, you will build an organization that not only uses AI, but thinks and acts on the basis of data.

“The best time to plant a tree was 20 years ago. The second best time is now.” — Chinese proverb

Artificial intelligence in operations management is your tree. Plant it today.