AI in Business: How Companies Use Artificial Intelligence Today
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By
VireonPress Editorial
- Technology
- 5 min read
-
By
VireonPress Editorial
- Technology
- 5 min read
Some solutions are ahead of their time. Others simply turn out to be hype. But what can be said about one of the most discussed technologies today?
For instance, just a few years ago, artificial intelligence was perceived as just another experiment whose long-term viability was uncertain. However, today, over 62% of companies worldwide are already regularly using it in at least one business function [1]. But where does this deliver the greatest benefit? Let’s find out.
Where AI Actually Delivers Value
Artificial intelligence in the business context is most often used for those tasks, where the data volume exceeds the capabilities of human analysis.
Operations
Here, it serves as a tool for eliminating inefficiencies in real time. It typically enables proactive maintenance and optimization of supply chains, as in the case of Siemens, which uses AI to monitor the condition of gas turbines. Their sensors record microvibrations, which the AI then analyzes to determine whether a breakdown will occur in the coming days. This saves millions of dollars on emergency repairs and prevents downtime [2]. In logistics, smart algorithms like those used by Amazon predict optimal routes, taking into account thousands of factors simultaneously, from driver shifts to holiday traffic jams.
Customer service
In this category, it’s worth mentioning Conversational AI, recognizing intonation and the stress level of the person it’s talking to. Klarna, a bank, was one of the first to adopt these technologies, implementing an AI assistant in its call center [3]. In its first month of operation, the bot performed the equivalent of 700 full-time agents, reducing the average time to resolve customer issues from 11 minutes to less than 2 minutes.
Forecasting
While conventional forecasting is based on last year’s data, an AI-based one takes into account thousands of external variables in real time. For example, Walmart uses it to forecast demand for over 500 million product SKUs, analyzing local events and automatically increasing procurements of specific products for specific points of sale [4]. This reduces the cost of storing unsold goods and leads to increased revenue due to the availability of the right products on store shelves.
Fraud detection
AI in this sector is the only way to detect anomalies in real time. JPMorgan Chase’s system, for example, analyzes every card transaction in real time [5]. When the algorithm detects that a customer who typically shops for groceries in New York suddenly attempts to buy jewelry in Dubai with behavioral irregularities, the transaction is blocked. This is how the bank prevents billions of dollars in theft.
Enterprise AI vs Isolated Tools
Today, we see a conflict between enterprise AI and isolated tools. Many companies, for example, may use a separate bot for messaging apps, a separate script for handling Excel data, and so on. However, the true value of this technology arises when it covers the entire infrastructure. Otherwise, decision support systems and intelligent automation tools will be difficult to scale and open up to new company departments.
The Implementation Gap: Why Adoption Is Slower Than Headlines
So far, the presence of technology in a given company hasn’t been a proven successful integration into business processes. This is due to the following reasons:
- Data silos. Since AI uses data for training, if the company stores it in different formats or if this data contains errors, the algorithm will be susceptible to hallucinations.
- Lack of qualified personnel. The international talent market still faces a shortage of specialists capable of combining a deep understanding of business with the necessary technical expertise.
Staff resistance. Employees often perceive artificial intelligence as a threat to their jobs, leading to the sabotage of the process efficiency.
Cost, Risk, and Governance Considerations
Companies cannot afford to use machine learning models without control; otherwise, it leads to the following categories of risks.
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Risk type
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The essence of the problem
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How to minimize the risk
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Financial
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Large costs for computing power and APIs
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Using open-source and cloud-based models
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Reputational
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Bias
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Human-in-the-loop and ethical audits
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Security
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Data leakage through public models
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Deployment of on-premise models
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Legal
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Copyright infringement
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Continuous monitoring of the legislation that regulates the use of AI for companies
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AI as Infrastructure, Not Feature
Today, the use of AI in business is no longer a unique selling proposition – it’s an infrastructure standard that simply allows businesses to stay afloat. It’s important to understand that when implementing AI for workflow optimization, companies must completely redesign their decision-making architecture. Instead of a hierarchical structure, where data flows from the bottom up for management, this technology should decentralize the process of making data-driven decisions, while preserving the role of ethical overseer for humans.
Conclusion: AI as a Structural Layer of Business
The main insight from the current phase of AI and business integration is that this technology exposes all the weaknesses of your current business strategy. Therefore, in the coming years, the companies that will win will be those that stop treating this technology as a separate IT project and start integrating it into their corporate culture.
Sources:
[1] – The state of AI in 2025: Agents, innovation, and transformation
[2] – Generative artificial intelligence takes Siemens’ predictive maintenance solution to the next level
[3] – Klarna AI assistant handles two-thirds of customer service chats in its first month
[4] – Walmart uses AI to predict demand
[5] – How AI will make payments more efficient and reduce fraud
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