AI is one of the loudest topics in business today, but there is a very big difference between fashion and real value. On the one hand, companies are investing more and more, and McKinsey shows that almost all surveyed organizations are already using AI in some form. On the other hand, most are still at the experimentation stage, and only a smaller part declares an impact on the result at the level of the entire organization. Gartner additionally points out that expectations for GenAI are lowered by high rates of failed proof of concepts and disappointment with the quality of some of the results.
This means one thing. The mere fact that a company "implements AI" does not solve anything. The value appears when AI is connected to a specific process, has a clearly defined business goal and works on data that actually helps make decisions or shorten work time. McKinsey describes examples where generative AI, analytics and classic digital tools were combined with real operational workflow, rather than treated as a separate gadget. Gartner also predicts that most GenAI business applications will be developed on existing data platforms to reduce complexity and speed delivery times.
Where AI truly removes cost and shortens work
Therefore, the most honest answer to the question of where AI provides value is yes. Not where it looks impressive in a presentation. Only where it removes a specific cost, shortens a specific stage of work or increases the quality of a specific decision. NIST, in its AI Risk Management Framework, emphasizes that organizations should approach AI as a system whose use, assessment and risks must be consciously managed, not as a magic add-on.
AI usually makes the most sense in areas where there is large volumes of repetitive work on text, data and documents:
- Customer service – classifying requests, proposing answers, searching for appropriate information
- Sales – lead qualification, conversation summaries, preparation of first versions of offers
- Operations – summarizing documents, detecting deviations, organizing data, support in analyzing the causes of problems
These are applications in which AI does not replace a process, but instead enhances humans in their real work.
ℹ️AI gives value where there is something to measure
If it is not clear what needs to be improved - time, cost, number of errors, number of clicks - then the implementation very often stops at the demonstration the capabilities of the tool, not on the business result.
A condition that most companies overlook
AI also provides value where the company already has data and an orderly operating system. If you have CRM, request history, documents, knowledge base, sales pipeline or well-described implementation stages - then the model has something to refer to.
However, if the process works mainly in people's heads, on Slack, in e-mails and in a few Excel files, AI will very often not solve the problem. It will only cover the bigger mess with a new layer of technology. This is why Gartner and McKinsey so strongly emphasize the gap between AI adoption and real enterprise value.
Practical applications with measurable value
A very good example of real value are implementations that shorten response time and improve the quality of work without building a large transformation program to start with. AI can help with:
- ✓Automatic ticket tagging and classification
- ✓Creating draft responses for the service team
- ✓Summarize meetings and extract data from documents
- ✓Generating checklists and operational reports
- ✓Searching for information in the knowledge base
Here the benefit is easy to measure - you can count the time, number of clicks, number of errors and speed of service.
Where AI is just a fad
Case 1: no defined goal. The project starts with the phrase "we also want to have AI", but no one can say what indicator should be improved. Gartner speaks directly about the growing pressure on predictable business value and the abandonment of some ambitious but hardly predictable internal projects.
Case 2: implementation without a process owner. If AI is intended to help sales, but sales does not take responsibility for process definition, data quality and subsequent adoption, the project begins to drift. The technical team provides the tool, but the business does not change the way it works. McKinsey describes that the execution and ability to work together between business and technology can be a bigger problem than the technology itself.
Case 3: Lack of security and control policies. NIST emphasizes that AI should be assessed for risk, trustworthiness, and use. In practice, this means mundane questions:
- ✓Who is responsible for the model output?
- ✓Does a human approve the answer?
- ✓Do you know what data the model uses?
- ✓Does the company not disclose sensitive information?
- ✓Is it possible to explain why the system suggested this decision?
Without this, AI may not only fail to help, but may even increase operational risk.
What a reasonable approach should look like
So the most reasonable approach for the company is not "we will implement AI everywhere". A better path looks different:
- You choose one process with a lot of manual work and repetitive decisions
- You count the cost of the current action
- You implement a small, measurable use case – preferably with a human in the loop
- Only later you expand the scale
This course of action is more consistent with both NIST's cautious approach to AI risk management and what current market research shows about value scaling issues.
Check out how we approach automation and AI for companies
In practice, AI provides the greatest value not when it sounds futuristic, but when it becomes an invisible part of a well-designed process. The customer gets an answer faster. The salesperson prepares the offer faster. The team finds information faster. The manager sees more quickly where the problem is. If this isn't there, you probably don't have an AI implementation yet - just an expensive experiment.
Let's talk about implementing AI in the company
10 processes that companies still do manually, even though they don't have to
Does AI in a company really increase profits?
Yes, but not automatically. McKinsey shows that many organizations see value at the level of individual use cases, but much less declares an impact on EBIT at the level of the entire company.
What's the best way to start implementing AI?
From a single process with a lot of manual work, a clear goal and a measurable result - for example, ticketing, quoting or work on documents. This is safer than a broad "company-wide AI" program.
When does AI make no sense?
Most often, when the company does not have a structured process, does not have an owner of the implementation, does not have good data or expects the tool to work itself will fix the operational chaos.
