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The gap between finishing a course and actually being useful on a team, Denis Brovarnyy has seen it from both sides. And in an era where AI is reshaping every technical role, that gap is getting more expensive to ignore. Companies are no longer experimenting with AI. They are implementing it. And they need people who can contribute from day one, not after six months of onboarding. He spent years as a software engineer and then an engineering manager in Israel, building products and leading teams. He knew what it took to hire someone junior and get them productive fast. He also knew how rarely training programs produced that person.

When he lost his job, he didn’t immediately look for the next one. He sat with a question most people skip: “Is there a better use of what I know?” The answer became AIT Technology School, and a decade-long project to build education that actually translates into employment.

From engineer to educator

Denis has a background in computer science and systems analysis. In 2006, he earned his Bachelor’s degree in Computer Science. He later did research in mathematical modeling and GIS-based infrastructure systems. After moving to Israel, he worked in technical and engineering management roles for several years.

The layoff that prompted his pivot was a clarifying moment, uncomfortable at the time, useful in hindsight. He realized he had two choices: he could return to a familiar career, or try to fix the disconnect between formal education and what employers actually need. He chose the more difficult option. 

He joined an existing IT school in Israel and immediately started dismantling how it worked. Lectures went down. Real projects went up. Students worked in teams with actual deadlines, built portfolios with output that employers could evaluate, and learned what it felt like to ship something under pressure. It’s not a radical idea, it’s just rarely executed properly. Most programs say “hands-on” and mean exercises. Denis meant: you’re building something, it has to work, and someone is going to look at it the way a hiring manager would.

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Building AIT Technology School: Practical, Market-Driven, Employer-Focused

That philosophy became the core of AIT Technology School. The school runs programs focused on training AI engineers, a profession at the intersection of IT and artificial intelligence, helping companies automate routine work, improve customer service, and implement AI at scale. It is one of the fastest-growing and least-served roles in the market today. Feedback from managers is used to rebuild Curricula.

Under Denis’s leadership, AIT Technology School has trained more than 1,500 graduates and at one point managed more than 700 active students simultaneously. The growth came primarily from a single-minded focus on employment outcomes. When graduates get hired and perform well, the school’s reputation follows. 

Denis stays closely involved across curriculum, partnerships, marketing, and operations, not as a figurehead, but because he believes the moment leadership loses touch with the actual product, the product starts drifting from reality. Every program is evaluated against one question: “Can this person contribute on day one?

Expanding Across Borders

Taking AIT Technology School into Germany and then the United States wasn’t a matter of duplicating the model. Each market has its own hiring culture, its own expectations of what “ready” looks like, and its own pace.

In Germany, structured and specialised training carries more weight. Employers there tend to want depth in a defined area and a clear credentials trail. The market moves faster in the U.S., and range, the ability to pick up new tools and slot into different team structures, is valued. 

Denis and his team adjusted the core approach to fit each environment without abandoning the underlying principle: practical readiness, real projects, measurable outcomes.

This international expansion also reflects something about the people AIT Technology School serves. Many of its students are globally mobile, professionals who have moved across borders and need to re-enter a labor market in a new country, often in a compressed time frame. AIT’s model, with its emphasis on professional portfolio work and employer-aligned skills, is particularly well-suited to that challenge.

Education in the AI Era

Education is no longer about the knowledge people accumulate, it’s about how quickly they can put it to work.​​​​​​​​​​​​​​​​ AI has shifted the hiring calculus in ways that are still playing out. Technical knowledge alone no longer differentiates candidates the way it once did. What employers want now is someone who can pair foundational skills with modern tools, adapt quickly when those tools change, and produce results before they’ve had time to settle in.

Denis has been steering AIT Technology School toward exactly that. The emphasis is less on memorizing frameworks and more on building the problem-solving instincts and execution habits that hold up as technologies keep evolving. Most programs sell knowledge. AIT Technology School is built around a different premise: the labor market doesn’t pay for knowledge, it pays for the ability to solve real problems. That means enough hours, real projects, team work, and preparation for actual interviews. A sequence, not a course. That’s why AIT Technology School has shifted its focus to training AI engineers,  one of the fastest-growing and least-served roles at the intersection of technology, product, and business. 

In his view, the question education should be answering right now isn’t “what does this person know?” but “how quickly can they deliver results?”

In fast-changing fields, knowledge alone is no longer enough,” he says. “Traditional education often moves more slowly than the labor market, while real careers are shaped by how quickly people can deliver results in actual teams, workflows, and products.”

What He’s Actually Building Toward

Denis measures AIT’s success by one thing: did the graduate get a job, and could they do it? Not course completion rates. Not certificates issued. The outcome.

He wants to integrate education, employment, and entrepreneurship. Denis feels people need to keep on learning, even after starting their careers. 

He completed executive and entrepreneurship programs at institutions including York Entrepreneurship Development Institute, Technion, Israel Institute of Technology, and Stanford University. These experiences encouraged him to think about how education and company-building could be connected more intelligently.

Now based in Miami Beach, Florida, he continues to lead AIT’s day-to-day operations while driving its international growth. The path from software engineer to school founder wasn’t a reinvention. It was a direct application of everything he’d already learned about what it actually takes to build something that works.



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ZDNET’s key takeaways

  • Your ability to exploit data relies on strong underlying processes.
  • AI can be your best friend when it comes to data integration.
  • Focus on consistency, orchestration, capabilities, and culture.

As many as 63% of business leaders describe their organizations as data-driven. However, only one in two executives is confident about its ability to deliver timely business insights.

If your business is going to make the most of its information, it’s going to need a technique to make its data available and accessible. So, step forward emerging technology, which increasing evidence suggests can be the key to unlocking value from information.

Also: 5 ways to use AI when your budget is tight

Whether it’s integrating platforms, merging companies, or working across geographies, professionals must manage a complex range of sources. Here’s how experts say that AI and automation can help.

1. Drive internal consistency

Joel Hron, CTO at global content and technology specialist Thomson Reuters (TR), said his organization uses AI to overcome data and system integration challenges in software engineering.

“We’ve found great benefit across various modernization and migration activities,” he said. “We heavily use AI tools to help ensure compliance with accessibility standards and things like that.”

That pioneering work continues at pace. Hron said TR’s corporate development teams are currently creating an internal AI system for due diligence to drive more consistency in deal evaluation, risk assessment, and potential risk mitigation.

“It’s really a super powerful idea,” he said. “They’ve been building that for the last month or two, coupling it quite nicely with a legal operations product that we sell in the market called HighQ.”

Also: 90% of AI projects fail – here are 3 ways to ensure yours doesn’t

Hron said TR is an acquisitive company that spends time integrating systems. While the benefits of these AI-enabled developments for his company are clear, could the tool above one day be used by external clients? Maybe, he said.

“If we can create something really useful for us, why not bring it to market? But at this point, our focus is about how we make this tech useful for all of our M&A activity and help drive not just speed and efficiency, but consistency in the deals that we do.”

2. Orchestrate your insights

Miko Chen, lead data engineer at Create Music Group, uses data and AI to improve her company’s operational processes, and she advises other professionals to explore leading-edge tools.

The Los Angeles-based music technology specialist uses AI and orchestration capabilities in Astronomer’s Airflow service Astro to manage over 600 data pipelines.

Create has used Astro to integrate its BigQuery and Google Cloud Storage technologies, and APIs from Spotify, YouTube, Apple Music, and Amazon Music, into a layer that manages data pipelines for operational activities, such as analytics and financial forecasting for labels and artists.

Also: 5 ways you can stop testing AI and start scaling it responsibly in 2026

“We want to provide better data to help our clients make decisions, instead of randomly thinking about what they should do,” she said.

“For example, if they want to host a concert, they can use our insights to consider which city they should pick versus which city they can select next time. So, with our data, our artists and our clients can make this proactive decision.”

Create is an acquisitive business, and Chen said her team also uses Astro to consolidate data.

“With Astro, we can easily move data around different places, such as from one organization to another, or from one country to another,” she said.

3. Explore current capabilities

Huy Dao, director of data and machine learning platform at Booking.com, said it’s important for professionals to understand the technological capabilities in their existing data stacks.

Booking was already a Snowflake customer when Dao joined the firm in August 2023. While this platform was proving its worth, he knew the technology offered additional capabilities, particularly for creating AI-enabled services.

“We now use it for way more than the warehousing component,” said Dao, who explained to ZDNET his team’s direction of travel during the past two and a half years.

“All our sensitive data access goes through Snowflake, and we are also using the latest available capabilities, both Cortex AI and Cortex Analyst. We’re exploring the Snowflake Semantic View, and we are also interested in the Horizon Catalog, which can interconnect with other data catalogs.”

Also: How to build better AI agents for your business – without creating trust issues

Rather than just providing a consolidated source of information, Dao said Snowflake provides an AI-enabled solution that helps professionals solve intractable business challenges. In short, business users now have the power to enact change, thanks to AI.

“The platform reduces the barrier to entry. So, instead of having only 200 users who can access and use our data, we can have 2,000 users because Snowflake makes it easy,” he said.

“With some of the AI capabilities, you don’t even have to write SQL to query the data. Things like that make it easy for people who traditionally did not have data skills.”

4. Focus on marginal gains

Richard Corbridge, CIO at property specialist Segro, told ZDNET that AI and automation can play a crucial role in helping his company bring disparate data assets together. He gave ZDNET the example of cross-European sustainability data.

“We need to monitor, by law, our carbon footprint and sustainability stats. In Poland, they send the meter readings as PDFs. In Germany, they come as digital, automatic readings. In the UK, they might come as photos of utility meters,” he said.

“We’ve got to work out how we take all those disparate ways of reporting on energy usage, put them into one place, and turn it into a Segro report on carbon footprint and energy use.”

Also: Worried AI agents will replace you? 5 ways you can turn anxiety into action at work

Previously, that laborious task was completed by a human specialist across Excel spreadsheets. Now, with the help of AI and process automation, Corbridge’s team is using AI to free up human resources and create business benefits.

“We’re building AI capability to go out, bring the data back, put it into the database, find out when it’s not right, and point out illogical elements, such as where the meter reading is the same as last month, so there must be something wrong,” he said.

“It’s cool stuff in a small, impactful area. It’s fascinating to see a bit of a geek on this one. The results have been super exciting.”

5. Reduce the management load

Ankur Anand, CIO at global technology and talent solutions provider Nash Squared, said the biggest impact AI has on data management is mapping and normalization.

“AI reduces your integration effort by almost 30% to 40% and gives you far more accurate results compared with the traditional approach of using Excel,” he said.

Using BlueGecko, an AI-enabled data management platform from technology specialist Nextgenlytics, Anand’s team has automated time-consuming data-mapping processes, particularly during post-M&A activities.

Also: 5 security tactics your business can’t get wrong in the age of AI – and why they’re critical

He explained to ZDNET how the system produces accurate results at key stages, such as during ETL (Extract, Transform, Load) processes.

“Blue Gecko understands the data, maps the data, explains how the two systems are talking to each other, and the values within those systems, and through that approach, the technology helps to accelerate your work around ETL development,” he said.

Anand’s advice for other professionals looking to integrate data and systems is to focus on culture.

“Think about people who have been using other tools,” he said. “What are the change management processes that you can bring in? Success is not just about deployment; adoption is also important.”





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