AI Tools for Data Management: From Hype to Business Impact

Across Europe, employees are facing growing pressure to demonstrate to senior leadership that they can master the latest tools designed to streamline business practices. As organizations accelerate the adoption of automation, AI, cloud-based collaboration platforms, and advanced data analytics, competitiveness and growth are increasingly defined by the strength of a company’s digital toolkit.

For example, to provide data-driven insights, you first need the data to analyze. The web is a major source of various business-relevant data. However, web data collection takes time and depends on specialized web scraping knowledge, such as building scrapers and fixing parsers. Even if you have these skills, it will take time, and most analysts have neither the particular skills nor the time. However, now, you can prompt AI-based data collection platforms to do the work for you.

This is a specific use case with measurable impact. Businesses that want to leverage AI beyond chasing the trend should look for similar use cases in their workflows that AI can streamline and tools that match these use cases. Additionally, AI adoption in any organization depends on providing resources, training, and facilitating a culture of support to ensure that nobody is left behind amidst these shifting technological tides.

Demystifying the ‘AI hype’ to measure potential

To truly understand how AI tools can streamline a company’s operations, we must first recognise that ‘AI’ has become a media buzzword – one often surrounded by hype that leads to disappointment. AI frequently fails outside controlled environments, producing errors, hallucinations, or biased outputs if not regulated effectively.

As the term ‘AI’ covers a wide range of processes and advancements, skeptics argue that much of the hype around AI exaggerates its current capabilities, with media coverage and investment trends often inflating expectations beyond what the technology can deliver right now. For example, the talk of Artificial General Intelligence (AGI) is premature, given the fact that we haven’t even started solving some key problems related to it.

If we are to take a step back and look at tangible results, the most significant breakthroughs so far have come from Large Language Models (LLMs). Systems that excel in natural language understanding, text generation, code assistance, and reasoning, while most other AI applications remain largely experimental. And there are signs that the progress with LLMs will be slower moving forward, so we shouldn’t count on major breakthroughs coming every few months.

For companies aiming to excel, they must steer away from the hype and look for tools that utilize AI meaningfully instead of a legacy solution packaged in an AI wrapper. Given the abundance of the latter kind of solutions, it’s no wonder that 95% of AI projects at companies fail. When judging new products, businesses should investigate how thin the AI layer really is and whether it would be difficult to build the same solution themselves. A company adds value that goes beyond just using one of the major LLMs when they introduce novel ways of doing things that you can’t easily replicate, original ways to solve specific problems.

By investigating what the product actually is, not just what it says it can do, and matching tools with actual use cases where they can have a measurable impact, businesses will learn that there’s plenty of opportunity in what is already accessible.

AI tools and data usage

Data is one thing that all departments across an organization can benefit from. However, few actually do because acquiring and analyzing data requires time and resources, including the specialized knowledge of data professionals. AI tools can remove these barriers at least in some cases, making data collection and even analysis more accessible.

Some AI tools are able to understand and act on natural language prompts. An employee doesn’t need to learn coding if they can say in plain language what kind of data they need, and the tool will scrape it from the internet. For example, if a marketing team wanted to understand customer sentiment around a recent product launch, analysts can now retrieve the available online data quickly and without turning to scraping teams.

Additionally, building scraping pipelines is resource-intensive and time-consuming. If businesses don’t have these resources at their disposal, LLMs can be used instead. It’s faster to use an AI-based platform that can do all the functions of web scraping than to build a scraping pipeline from the ground up. So, even resourceful enterprises might prefer using an AI-based platform when they need to start collecting data as soon as possible.

Organizations should help to upskill employees of all disciplines to use such tools, with a wealth of knowledge and training available online. A lot of truly valuable information is available for free. Google and Anthropic offer free courses, plus, there are plenty of online forums where people share knowledge and ideas, and OxyCon 2025, a free online conference, will include my practical session on creating an AI-powered price-comparison tool without coding by using existing AI tools, namely Oxylabs AI Studio and Cursor.

The next frontier in business-relevant AI

The global AI market is expected to skyrocket from around $391 billion in 2025 to $1.8 trillion by 2030. However, the progress in LLMs might slow down as data and computational resources limit them.

Off the back of this, we can expect a rise in small language models that require fewer computational resources and are geared towards specific tasks. Another advantage of these models is that, being smaller, they can be hosted on internal servers, which helps address some of the data privacy and security concerns.

Ultimately, the future of AI in business will be defined not by sweeping promises of revolutionary change, but by how effectively organizations harness the tools already within reach. By cutting through the hype, focusing on practical applications, and investing in training and integration, companies can unlock real productivity gains today while preparing for the next wave of innovation.