AI in practice: How to make tools a competitive advantage

Organizations that use AI targeted and systematically can innovate, save costs and create better products or services. Recent surveys show that companies that have adopted AI earlier achieve faster income growth and better return to investment than their competitors. This confirms that AI is no longer just a trend, but a tool that brings a real competitive advantage.

However, it is important to realize that AI itself does not bring a competitive advantage, it creates the way it is used in everyday processes. It is not enough to create a company account in a popular AI platform and expect instant results. Organizations that achieve success are thoughtfully looking for areas where AI can solve real problems: from accelerating customer support to more efficient financial analyzes to faster marketing of new products.

AI as productivity accelerator

One of the greatest AI benefits is the ability to eliminate time -consuming and recurring tasks. Instead of manually processing data, writing the first versions of documents or finding information, employees can focus on tasks that bring higher added value. In practice, this means that AI acts as a "super-assistant" that is always ready to help, such as summarizing a meeting, designing a marketing campaign or analyzing customer feedback.

This change has a major impact on the work routine and the overall efficiency of teams. Common tasks such as summary of meetings, analysis of large volumes of data, sorting e-mails, or elaboration of contracts can be performed today by AI automatically or assisted, often within seconds.

In the case of Morgan Stanley, thanks to AI, the AI ​​managed to shorten the time of searching for financial advisors from a few hours to minutes, so they have more space to devote themselves to the individual needs of clients.

An interesting example is Klarna, who deployed AI assistant for customer support. Today it handles two thirds of all cottages and saved tens of millions of dollars, while customer satisfaction remained the same.

This trend is also confirmed by Anthropic analysis. More than a third of jobs are already using AI at least a quarter of their tasks, while in areas such as programming or writing texts is even higher.

Six main ways of using AI

Experiences from companies show that most successful AI deployment cases fall into six basic categories: content creation, research, data analysis, automation, programming and strategic planning.

• Content creation: AI helps to generate blogs, emails, campaigns, graphics or subtitles. For example, in marketing, it allows for quick testing of different versions of advertising texts or translating content into multiple languages, taking into account the corporate identity and tone-of-voice. Promega saved 135 hours of work in six months only when writing e-mails and campaigns.

• Research and analysis of information: AI can browse thousands of pages of documents, summarize key points, propose further action, or create competitive reports. An example is the analysis of market trends or evaluating feedback from real -time customers.

• Data analysis: AI allows you to work with large datasetes without the need for advanced knowledge of SQL or Python. It can quickly visualize data, identify patterns and design optimization steps. Example: CFO in POSHMARK uses AI to automated generating sales results every week.

• Task automation: Various routine tasks (reports, summary of the minutes, sorting invoices) can be completely automated. With advanced agents, AI can take over the entire workflow, eg. Creating documents for meetings, sending notifications or automatic answers to repeated customer requirements.

• Programming and IT: AI generates code, reveals errors and helps with documentation. At the same time, it also facilitates the work of non-programmers who can only prepare scripts or visualizations on the basis of textual assignment.

• Strategic planning and ideas: AI can generate ideas, evaluate the strengths and weaknesses of projects, simulate customer discussion, or prepare strategies based on input data. Example: The Brainsta marketing team will indicate campaigns and have the key arguments for new market segments.

How companies are turning AI into a competitive advantage

The difference between experimentation and real benefit lies in a systematic approach. According to OpenAI, the most successful companies follow several principles: they start with clearly defined evaluations (evals), test specific use cases and gradually expand the scope. At the same time, they are not afraid to adapt AI to their own needs, for example by training models on their own data.

The first step is to identify the processes where it has the greatest benefit. These are often tasks that are either repetitive or require a lot of manual processing time. In practice, companies create process “maps” and find out where AI can replace humans, where it can complement them and where it can only speed up existing workflows.

After the initial pilot phase, AI is scaled and integrated into other parts of the business, with user feedback and ongoing evaluation of the results (e.g. quality of responses, reduction of error rates or speed of request processing) being the key.

For example, Lowe’s used fine-tuned models to improve product search accuracy in its online store. The result was 20% more accurate product labeling and fewer search errors. Such targeted model customization helps differentiate the company from competitors that rely on generic solutions.

Other companies are going even further, integrating AI directly into their products. Indeed, for example, has created an AI engine that recommends jobs while explaining why the job is a good fit for the candidate. Such personalized recommendations lead to higher application acceptance rates and user satisfaction.

People as the key to success

While AI can automate many tasks, its greatest value lies in complementing human capabilities. Anthropic’s analysis shows that more than half of AI’s use is augmentation, that is, expanding human capabilities, not replacing them.

Those who involve their employees in the process from the beginning have the best results. They offer them training, encourage experimentation (hackathons, workshops) and create platforms to share the best prompts and tutorials.

For example, BBVA made AI tools available to all departments. Employees could create their own mini-apps, automate reporting or personalize communication with clients without having to wait for the intervention of the IT department. The result? Shorter project cycles, fewer errors and greater satisfaction in teams.

Similar strategies are also emerging in companies like Mercado Libre, where the AI ​​platform accelerated the development of new applications and made innovations accessible to non-technical teams. The key is to allow experts who understand the domain to experiment with AI without unnecessary technical barriers.

The future belongs to the prepared

AI is becoming a standard part of work, similar to how computers or the internet once were. The difference will be how individual organizations are able to use it. Those that start today to specifically map out processes suitable for AI deployment, set clear rules and systematically prepare their people will create a lead that will be hard to catch up with.

The key is to create your own “roadmap”, identify areas with the greatest potential, set success metrics and continuously reassess priorities as the technology develops. It is equally important to set ethical rules, pay attention to data protection and constantly educate users about the possibilities and limits of AI. Examples from the most successful companies show that AI is not just about reducing costs, but especially about the ability to innovate, come up with new ideas and respond to the market faster than the competition.

SOURCE: Pravda