How to identify where AI creates value in companies

AI in Companies: How to Identify Where It Creates Real Value

Artificial Intelligence (AI) is becoming increasingly present in companies. It appears in meetings, software tools, proposals, and promises of efficiency gains. Many organizations have already experimented with AI in some form, yet a recurring question remains: where does AI actually create real business value?

This is a valid concern. As AI adoption accelerates, separating meaningful application from hype becomes harder. As with earlier stages of digital transformation, value does not come from simply “using AI,” but from applying it with context, structure, and clear objectives. This is where digital maturity makes the difference.

What “real value” from AI actually means

Creating real value with AI does not mean adopting advanced technology for its own sake. It means improving concrete business outcomes, such as operational efficiency, decision quality, response time, or the ability to scale without increasing costs at the same pace.

In practice, AI generates value when it:

• reduces time spent on analysis or interpretation of information,
• improves consistency and quality of decisions or responses,
• allows teams to handle higher volumes of work without overload,
• supports decisions based on real,
• historical business data.

On the other hand, AI delivers limited impact when it is used only as a standalone tool, disconnected from processes, data, and day-to-day operations.

AI is not “having your own ChatGPT”

One of the most common misconceptions about AI adoption is the idea that value comes from building an internal chatbot or “having a ChatGPT trained on company data.” While this can be a valid use case, it is not a strategy on its own.

AI should not be confused with the interface through which it is accessed. The value is not in the chat format or the tool itself, but in the function AI performs. AI can analyze data, identify patterns, summarize information, classify content, support decisions, and enhance existing processes.

When companies start with the technology instead of the problem, they often end up with solutions that are interesting but underused, difficult to measure, and poorly aligned with business needs. A more effective question is not “Which AI tool should we use?” but “Where do we spend excessive human effort analyzing information or working with underutilized data?”

How to identify real AI opportunities

Identifying where AI can create value does not require a large or complex initiative. It often starts by observing how work is currently done. Clear opportunities tend to appear where:

• tasks are repetitive and involve manual analysis of large amounts of information,
• decisions rely on historical data or recurring patterns,
• employees spend time classifying,
• summarizing, or validating information,
• data already exists in digital form but is not fully leveraged.

AI works best when the problem is clearly defined and there is enough data to support analysis. In many cases, the highest returns come from relatively simple, high-frequency activities rather than ambitious, organization-wide projects.

Where AI typically creates value across the company

In sales and marketing, AI can support lead analysis, contact segmentation, interpretation of campaign results, and content preparation. By working with historical and behavioral data, AI helps teams focus on areas with higher likelihood of return, without replacing strategic decision-making.

In customer service and support, value often comes from better information handling. AI can summarize long interaction histories, suggest responses based on similar cases, identify recurring issues, and support request prioritization. This leads to faster and more consistent responses while keeping humans in control of customer relationships.

In operations and back-office functions, AI reduces the effort required to handle documents, reports, and technical information. Common use cases include classification, extraction of relevant data, summarization of long documents, and detection of inconsistencies. These applications directly improve efficiency and reduce rework.

At the management level, AI supports decision-making by consolidating information from multiple systems, highlighting trends, flagging deviations, and preparing clearer reports. AI does not replace judgment, but it significantly reduces the time required to reach well-informed decisions.

Different levels of AI investment

Not all companies need the same type of AI solution, nor the same level of investment. A realistic approach recognizes different levels of adoption.

Many organizations start with off-the-shelf tools that already include AI, embedded in CRM, support, marketing, or analytics software. These solutions provide quick wins with limited initial effort, though customization is constrained.

More advanced use cases involve AI models adapted to the business, configured or trained using company-specific data. These deliver higher relevance and alignment with internal reality but require structured data and ongoing oversight.

At the most mature level, AI is embedded within automated workflows, acting as part of the process rather than a standalone feature. In this scenario, AI supports classification, analysis, or decision points inside automated flows. This approach delivers the greatest structural impact but also requires strong foundations in data quality, process clarity, and system integration.

There is no universal “right” approach. The level of investment should match both the problem being solved and the organization’s digital maturity.

Start small and scale with evidence

A key principle in successful AI adoption is starting small. Rather than deploying AI across the entire organization, it is more effective to focus on one specific process or challenge and test a targeted solution.

Defining simple metrics—such as time saved, quality improvement, or increased capacity—makes it possible to evaluate real impact. When results are visible and understood by teams, confidence grows and scaling becomes easier.

This incremental approach reduces risk, avoids frustration, and ensures AI evolves in line with actual business needs rather than abstract expectations.

AI as a continuation of digital maturity

AI does not replace digitalization, data organization, or process automation. Instead, it amplifies the value of these foundations. Without structured data, clear processes, and integrated systems, AI remains limited to isolated experiments with little long-term impact.

Identifying where AI creates real value is primarily a strategic exercise, not a technological one. It requires understanding the business, observing daily operations, and applying technology where it solves concrete problems. When used this way, AI moves beyond hype and becomes a practical tool for efficiency, better decisions, and sustainable growth.