IT HR trends and salaries in 2025: what we expect to see in the labour market

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2025 has begun on a cautiously optimistic note for Hungarian companies. According to Randstad’s latest HR Trends survey, half of the firms anticipate revenue growth this year, though salary and hiring plans remain more restrained. This outlook is especially relevant for businesses navigating digital transformation or seeking IT talent.

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More targeted labour demand in the IT sector

The IT and telecoms sector remains one of the most active in recruitment, though hiring intentions have declined by 11% year-on-year. According to the analysis, factors such as the global economic slowdown, reduced public investment, and shifts in the competitive landscape are all contributing to softer demand—particularly for senior tech roles.

Across industries, FMCG and HORECA companies are the most optimistic about their outlook, with many retail and logistics firms also anticipating revenue growth. As digitalisation continues to accelerate in these sectors, the demand for IT capacity is expected to rise further.

Meanwhile, Business Service Centres (SSCs/BSCs) are evolving towards higher value-added activities, as simpler, English-language-based tasks are increasingly outsourced to India—or taken over by AI. As a result, the sector remains one of the most active in recruitment, with growing demand for highly skilled professionals, particularly those with a strong technology background.

Unrealistic wage demands and lack of experience

Overly high salary expectations remain the top recruitment challenge, but this year a new issue has gained prominence: the lack of candidate experience. According to 69% of respondents, this has become a critical obstacle—especially in junior IT roles, where employers note that salary demands are rising faster than actual skill levels.

More and more HR professionals are recognising that competitive pay alone is no longer enough. Training and development programmes are becoming key not only for attracting talent, but also for boosting productivity and retaining employees over the long term.

What do the latest salary guides show?

Let’s take a look at some key salary benchmarks from the first pay round of the year:

  • Junior Java Developer: 650,000–1,150,000 Ft | Average: ~850,000 Ft

  • Junior DevOps / Cloud Engineer: 750,000–1,200,000 Ft

  • Senior Cloud / DevOps Engineer: Up to 2,000,000 Ft+

  • Tester (Manual vs. Automation): 600,000–1,050,000 Ft

  • Data Specialists:

    • Junior level: 700,000–1,100,000 Ft

    • Senior BI roles: ~1,500,000 Ft

What about wage increases? Salary raise plans are falling short of employee expectations. Most companies are planning increases between 6% and 10%, while 45% of employees are expecting a raise of more than 20%. This growing gap may pose challenges for retention and employee satisfaction in the year ahead.

Flexibility = bonus points for switching

In 2025, the number of home office days offered by companies continues to decline, with only 12% providing fully flexible working arrangements. This shift is especially relevant for IT professionals, for whom flexibility and work-life balance remain key priorities. As such, the home office option has become a compelling competitive advantage—companies that offer it are better positioned to attract and retain top tech talent.

What else makes a job appealing? According to the research, the most valued perks still include mobile phones, performance bonuses, travel reimbursement, parking, and company cars. A growing trend in 2025 is the expansion of fringe benefits, particularly the introduction of private health insurance. While training remains the most common non-cash benefit, coaching and mentoring programmes are also gaining traction as companies focus on long-term employee development.

Using AI: new challenges, new skillset

Most companies already support or are open to adopting AI—particularly in areas like administration, marketing, and HR. As organisations explore its potential, AI is expected to create demand for new skill sets and roles, not only in IT and data-driven functions, but across nearly every field. This shift highlights the growing importance of adaptability and continuous learning in the workplace.

2025 brings not only HR challenges, but also new opportunities. For companies, the focus is shifting toward productivity and real competencies, while for employees, salary expectations and the demand for flexibility remain high. Businesses that offer a credible career path, transparent salary structures, and meaningful learning opportunities will continue to hold a competitive edge.

We’re truly at home in the digital space—and we understand what it takes to attract and retain top IT talent or build digital capabilities within your team.

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Feel free to reach out—we’re here to support you.

AI and Social Equality: Supporting the Disadvantaged

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When discussing artificial intelligence, the business sector frequently comes to mind, primarily in terms of enhancing operational efficiency, reducing expenses, and boosting productivity. However, the potential of AI extends beyond the corporate sphere. How can artificial intelligence assist disadvantaged groups and foster opportunities for those who have been marginalized? Codecool has partnered with Google’s AI Opportunity Fund initiative to offer digital skills training to 5,000 disadvantaged Europeans.

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Under the AI Opportunity Fund: Europe program, Google.org and the Centre for Public Impact support organizations that offer AI training to bridge the digital divide. A principal contributor in this effort is Codecool: we’re going to deliver complimentary, localized AI training to disadvantaged groups across the CEE including Hungary, Romania, Poland, and Slovakia. As part of the initiative, a €15 million grant will fund the program, scheduled to run from 2025 until the end of 2026.

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Bridging the digital divide

We identified numerous compelling regional initiatives among the participants in the program. Here are some examples demonstrating how digital skills development assists disadvantaged individuals in reintegrating into the labor market and promoting equal opportunities.

The Danish HackYourFuture initiative focuses on 80 unemployed refugees and asylum seekers who lack access to traditional educational pathways and the Danish labor market. The project aims to equip participants with fundamental AI and technology skills necessary for successful entry into the IT labor market, while also fostering social inclusion.

In the Czech Republic, 300 disadvantaged women will receive AI and management training. The participants—primarily from rural areas and homemakers—will acquire basic AI skills and retraining support. This initiative aims to enhance their prospects in the increasingly sought-after ICT positions, thereby improving their career opportunities and economic resilience.

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Prague’s prg.ai project aims to provide basic AI training to 180 public servants, supporting the digital transformation of the government and public sector. Participants will gain a better understanding and application of AI solutions, enhancing the quality and responsiveness of public services.

The Croatian project will impact 400 individuals, including young rural jobseekers, women re-entering the labor market, and low-skilled workers, through digital skills training. The goal is to impart basic AI skills, which will not only help bridge the digital divide but also promote economic growth and social inclusion across the country.

Let’s also highlight the initiatives in Hungary! For instance, the Kecskemét Creative Knowledge Centre Public Foundation focuses on supporting those over 50. In Kecskemét, 360 workers and pensioners are being equipped with AI skills. This initiative enables older generations to keep pace with technological advancements and remain active in an increasingly digital labor market.

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A window to a more equal world with AI

The potential of AI extends far beyond: it not only opens new avenues in education and the labor market but also has the capacity to foster social equality, for instance in healthcare, when used responsibly and ethically.

Facing a shortage of doctors or diagnostic bottlenecks? Algorithms like those developed by Google’s DeepMind can diagnose diseases more swiftly and accurately, proving to be life-saving in regions where healthcare access is limited. Furthermore, AI-powered chatbots and telemedicine systems can offer medical advice to individuals who previously had no opportunity to consult with a specialist.

AI also enhances access to information for those unable to read or speak the local language. It can facilitate real-time interpretation between two languages, for example, in an office setting or during a medical consultation, act as a video sign language interpreter bridging communication between deaf and hearing individuals, and serve as a voice interface to communicate with the illiterate.

AI represents a significant opportunity, but its true value is realized only when its benefits are accessible to all. Initiatives like the AI Opportunity Fund and Codecool’s AI training programs demonstrate that AI can transcend its role as a mere tool for large corporations and become a powerful catalyst for social equality.

How to Attract and Retain Talented Junior Developers?

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Competitive Offer – It’s Not Just About the Salary

For junior developers, salary is an important factor, but it’s not enough on its own to make a company attractive. A competitive offer today includes growth opportunities, flexible work arrangements, and access to modern technologies. Young developers value remote work options, flexible hours, and projects that present real professional challenges. Companies must compete not only with base salaries but also with the kind of work environment and career development prospects they offer.

Additional benefits such as cafeteria perks and performance bonuses also play a role, but what matters most is that juniors see a long-term future with the company. A transparent career path, where advancement opportunities are clearly communicated, helps retain talented developers beyond the initial hiring phase. A well-structured junior training program or internal development initiatives can also be a cost-effective alternative to constantly recruiting new employees.

Mentorship and Growth Opportunities

One of the most important expectations of talented junior developers is the opportunity for continuous learning. If a company does not provide adequate mentorship and training, it risks losing its most promising employees. Hands-on learning and the ability to gain experience from senior developers are essential for juniors to gain confidence in their work. A well-designed mentorship program not only enhances their technical skills but also strengthens their loyalty to the company.

Internal training programs, hackathons, and company-wide knowledge-sharing events offer juniors a way to accelerate their professional growth. Supporting their learning—whether through training budgets, access to technical books, or funding certifications—can be an attractive incentive. Companies that actively invest in their employees’ development become far more appealing to young talent looking for a long-term place in a team.

Learn how to collaborate with one of Europe’s leading digital training centers and achieve your digital transformation goals faster and more effectively. Whether you need IT specialists or corporate training programs, we have the right solution for you. Click the link to explore our detailed offerings!

👉 Find out more now

Why Do Juniors Stay for the Long Run?

A strong team culture is just as important as technological challenges or competitive salaries. If junior developers feel that their work environment is supportive and inspiring, they are much more likely to stay with the company long-term. Beyond mentorship, collaboration opportunities, team cohesion, and a healthy work atmosphere play key roles in retention. A company that fosters an open and supportive community enables juniors to ask questions freely and integrate faster into their roles.

HR and management teams should pay close attention to how new developers feel within the company. Regular feedback sessions, team-building activities, and company events contribute to stronger engagement and a sense of belonging. Companies that prioritize employee well-being not only reduce turnover rates but also build stronger, more productive teams.

Beyond these, companies can also implement internal training programs that accelerate the development of juniors and mid-level developers within the organization. These programs not only increase the competitiveness of individual developers but also strengthen the company as a whole—especially in fast-changing environments where continuous adaptation is crucial.

Cybersecurity Takes Center Stage: Coding Is No Longer Enough

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Keeping Motivation High

Junior developers’ motivation largely depends on whether they feel they are contributing meaningfully to their team. If their tasks consist only of repetitive or low-impact work, they may quickly lose enthusiasm. That’s why it’s crucial for companies to involve junior developers in meaningful projects where they can tackle real challenges. A well-structured onboarding process helps them integrate quickly and effectively into ongoing projects.

For example, Codecool places great emphasis on ensuring that students learn through real-world projects. Such experiences prove invaluable later, as developers not only gain technical knowledge but also learn how to collaborate effectively, manage projects, and troubleshoot challenges. Companies that provide juniors with real-world challenges gain dedicated and motivated professionals in the long run.

5 Tips for Attracting and Retaining Talented Junior Developers

  1. Develop a Competitive Offer
    Don’t focus only on salary—offer growth opportunities, flexible work environments, and exciting projects. A clear career path and continuous learning options make a company much more attractive to juniors.
  2. Provide Mentorship and Training Opportunities
    A well-structured mentorship program and internal training sessions help juniors develop quickly. Hands-on learning through real projects enhances their practical knowledge and commitment to the company.
  3. Foster a Strong Team Culture and Supportive Environment
    An open, welcoming team atmosphere is a key factor in retention. Regular feedback sessions, team-building activities, and collaboration opportunities contribute to making juniors feel at home.
  4. Involve Juniors in Real Projects
    Assign challenging tasks that allow junior developers to make meaningful contributions. Giving them responsibility and autonomy accelerates their professional growth and increases motivation.
  5. Offer Long-Term Career Prospects
    Clearly communicate career progression opportunities. If juniors see a clear path for advancement and professional development, they are less likely to leave for another company.

Summary

Attracting and retaining talented junior developers is not just about offering competitive salaries—it requires a comprehensive strategy. Providing proper mentorship, training opportunities, and a supportive team culture are key to long-term engagement. Companies that offer a transparent career path and meaningful projects for junior employees have a much higher chance of keeping the best talent.

Speed up digitalization and take your team's skills to the next level!

Whether you need new tech professionals or want to upskill your existing employees, Codecool offers flexible and tailored solutions to keep your business ahead in digitalization and AI advancements. Explore our services and find the best training, reskilling, or IT talent solutions to fit your needs.

Discover what else Codecool offers!

Learn how to collaborate with one of Europe’s leading digital training centers and achieve your digital transformation goals faster and more effectively. Whether you need IT specialists or corporate training programs, we have the right solution for you. Click the link to explore our detailed offerings!

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Software Development Trends in 2025 – Where Is the IT Market Heading?

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AI-Powered Development: The Future of Coding or a Potential Risk?

AI-powered development tools like GitHub Copilot, ChatGPT, and Tabnine are transforming the software development process. These tools can automate repetitive coding tasks, optimize development time, and help developers learn new technologies more efficiently. AI is also improving in identifying and fixing bugs, significantly reducing development cycles.

 

However, integrating AI into development is not without challenges. Algorithms are still prone to logical errors and security vulnerabilities, which human developers need to detect and correct. Additionally, the long-term impact of AI on developer roles remains uncertain—will junior positions decline, or will demand for AI-assisted developers increase?

The Rise of Low-Code and No-Code Platforms: Will Everyone Become a Developer?

Low-code and no-code tools enable non-technical professionals to create functional applications. Platforms like Bubble, OutSystems, and Webflow are gaining popularity as they accelerate the development of prototypes and enterprise solutions. These tools offer an efficient alternative for small and medium-sized businesses by reducing development costs and lowering the barrier to entry.

 

While low-code and no-code solutions work well for certain projects, they come with limitations. Developing complex, scalable systems still requires deep programming knowledge. Companies need to carefully decide when to use a no-code solution and when to involve an experienced development team.

Learn how to collaborate with one of Europe’s leading digital training centers and achieve your digital transformation goals faster and more effectively. Whether you need IT specialists or corporate training programs, we have the right solution for you. Click the link to explore our detailed offerings!

The Impact of Remote and Hybrid Work on Development Teams

The remote work trend, which accelerated in the early 2020s, is not slowing down. The development market is increasingly adapting to hybrid or fully remote work environments. Companies are attracting global talent, and the best developers are no longer confined to local offices but are joining teams from different parts of the world. This flexibility benefits employees by improving work-life balance but also presents new challenges in team management.

 

Due to remote work, companies must place greater emphasis on communication tools, agile methodologies, and efficient project management. Asynchronous work and collaboration tools (such as Slack, Jira, and Miro) are becoming essential for successful development projects. Additionally, companies must foster a culture that supports independent decision-making and productive collaboration.

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Cybersecurity Takes Center Stage: Coding Is No Longer Enough

As online systems and automation become more complex, the importance of cybersecurity grows. In 2025, developers will need more than just coding skills—they must also understand data security, API protection, and ethical hacking principles. Companies are increasingly looking for developers who can write efficient code while minimizing potential attack surfaces.

 

With the growing emphasis on security-conscious development practices, the DevSecOps mindset is becoming the new norm. Software development processes must integrate security testing and automated vulnerability checks from the design phase. This not only protects company data and customers but also reduces long-term costs associated with fixing security issues and addressing cyber threats.

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What does DevSecOps mean?

DevSecOps is derived from the words Development (Fejlesztés), Security (Biztonság), and Operations (Üzemeltetés) and represents a software development approach where security is integrated into the early stages of the development process.

 

Previously, security checks were often performed at the end of the development cycle, which slowed down time-to-market and increased vulnerability risks. The goal of DevSecOps is to incorporate automated security checks, vulnerability testing, and code security analysis into CI/CD (Continuous Integration/Continuous Deployment) pipelines. This ensures that developers, IT security experts, and operations teams work closely together to deploy applications quickly and securely.

 

DevSecOps helps ensure that software not only functions properly but also meets the highest security standards from the beginning of the development cycle. This reduces the risk of cyberattacks and minimizes the cost of later security fixes.

5 Key Software Development Trends for 2025:

  1. The Expansion of AI-Powered Development
    AI tools are increasingly assisting developers in code generation, debugging, and optimization, reshaping software development workflows.
  2. The Growth of Low-Code and No-Code Platforms
    Simple business applications are now being built by non-developers, but complex projects still require strong programming expertise.
  3. The Standardization of Remote and Hybrid Work
    Development teams are becoming more global, requiring companies to adopt better communication and project management tools.
  4. The Increasing Role of Cybersecurity and DevSecOps
    Developers must go beyond coding and integrate security principles into software design and development.
  5. The Rise of Automated Development Processes
    AI and automation reduce manual tasks in software development cycles, accelerating time-to-market for new applications.

Summary

Software development in 2025 will be faster, more efficient, and increasingly data-driven. AI-powered tools and automation will reduce manual work, while low-code platforms will enable more business professionals to create their own applications. Hybrid and remote work will remain dominant, pushing companies to adopt new communication and collaboration methods. Meanwhile, cybersecurity will take on greater importance, as growing digitalization brings new threats. Developers and companies that keep up with these trends will gain a long-term competitive advantage.

Speed up digitalization and take your team's skills to the next level!

Whether you need new tech professionals or want to upskill your existing employees, Codecool offers flexible and tailored solutions to keep your business ahead in digitalization and AI advancements. Explore our services and find the best training, reskilling, or IT talent solutions to fit your needs.

Discover what else Codecool offers!

Learn how to collaborate with one of Europe’s leading digital training centers and achieve your digital transformation goals faster and more effectively. Whether you need IT specialists or corporate training programs, we have the right solution for you. Click the link to explore our detailed offerings!

👉 Find out more

You have 1 new team member! Generative AI at work

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Today, artificial intelligence has become accessible to nearly everyone. Instead of relying on complex coding, AI can use simple, everyday language. But what exactly can we use it for, and how can we harness its potential effectively? Authors from the Harvard Business Review have explored this new era of human-machine collaboration, highlighting research that shows AI is poised to transform more than 40% of work activities soon.

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Generative AI transforms jobs and the way we work

Generative artificial intelligence is set to revolutionize various jobs in the years ahead.

No longer exclusive to developers and IT professionals, AI tools empower anyone to provide instructions in plain, everyday language. Research from the Harvard Business Review authors reveals that generative AI has the potential to extend, automate, or reimagine most business functions and over 40% of all jobs in the United States. The most significant changes are expected in legal, banking, insurance, and capital markets, with additional impacts in retail, travel, healthcare, and energy.

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In the future, many people will find that their success at work depends on their ability to get the best out of large language models like ChatGPT—and to learn and grow with them.

3 fusion skills to use AI effectively

HBR’s thought-provoking article highlights three fusion skills essential for anyone looking to use AI effectively and achieve meaningful results.

The ability to intelligently prompt involves instructing large language models in ways that produce better outcomes. This requires understanding AI’s language and leveraging its capabilities by breaking down processes into clear, step-by-step instructions or visualizing multiple possible solutions.

For instance, a customer service representative in financial services might use AI to resolve complex customer queries. A pharmaceutical researcher could rely on it to analyze drug compounds and molecular interactions. Meanwhile, a marketer might utilize AI to examine data sets and determine optimal retail pricing.

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The integration of discernment involves incorporating human judgment—both professional and ethical—to ensure that the answers and solutions generated by AI are reliable and accurate.

Large language models often lack the technical or business background knowledge necessary to solve specific problems, and this gap is frequently filled with AI hallucinations. Identifying where the model requires further learning and determining credible sources to support this process is crucial, all while maintaining a strong commitment to data security.

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Mutual learning involves supplementing artificial intelligence with authoritative knowledge bases when necessary.

This process is reciprocal: as generative AI learns from the organization’s data and expertise, the human training it gains the ability to leverage AI for increasingly complex challenges. This dynamic turns the machine into a valuable team member—a co-creator.

Let me know if you'd like further refinements!

Empirical research consistently shows that ad hoc instructions—commonly used by most AI model users today—often result in unreliable or poor outcomes, particularly for complex reasoning tasks. This applies across all functions, from customer service and marketing to logistics and R&D. Bringing greater rigor to using generative AI in the workplace is essential for achieving dependable results.

How to ask AI smart?

Always think step-by-step: when prompting AI, break down the process into its individual components and focus on optimizing each step. Visualize a chain of thought that leads to your desired outcome. Empirical studies show that their performance improves significantly when generative AI tools are guided to decompose reasoning tasks in this manner.

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Even for the simplest tasks, including a “think step-by-step” prompt is beneficial. This encourages the AI to explain how it arrived at the result rather than merely providing an answer from a black box.

Mutual learning should also take place in stages

For complex tasks requiring expertise or business context in human-machine collaboration—such as law, medicine, R&D, or inventory management—you can achieve better results by integrating AI into the workflow gradually and in stages.

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MIT researchers, for example, explored the feasibility of developing an “AI scientist” capable of integrating diverse experimental data and generating testable hypotheses. They discovered that ChatGPT 3.5-Turbo could be fine-tuned to learn the structural biophysics of DNA when the researchers divided this complex task into a series of manageable subtasks for the model to master.

This approach is equally practical in non-scientific areas, such as inventory management. Subtask stages can include forecasting demand, collecting inventory data, predicting reorders, evaluating order volumes, and assessing performance. We can leverage our expertise and information for each additional subtask to teach, test, and validate the model.

Make room for creativity

Many workflows, from strategy design to product development, are open-ended and iterative. To maximize human-AI interaction in these activities, it is beneficial to guide machines to explore multiple solution paths and respond less linearly and binaryally.

This approach to intelligent querying can also enhance AI’s ability to make accurate predictions about complex financial or political events. Research by Philipp Schoenegger and Philip Tetlock demonstrated this in an experiment where human analysts and forecasters were paired with GPT-4 assistants to create “super-forecasters.”

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This collaboration aimed to assign probabilities and uncertainty intervals to possible outcomes while listing arguments for and against each scenario. The researchers found that predictions generated by the trained assistants—on topics ranging from the Dow Jones closing figures on a specific day to the number of migrants arriving in Europe via the Mediterranean in December 2023—were 43% more accurate than those produced by commonly used, untrained GPTs.

Critical thinking and judgement

Integrate the RAG

Not only can large language models be prone to hallucinations, but the information and data sets used to train them are often outdated. As a result, working with these models usually requires human judgment to determine how critical it is to ensure the outputs are reliable, relevant, and up-to-date. In domains where fresh and accurate information is essential—such as healthcare or finance—retrieval-augmented generation (RAG) can be a valuable tool. This method enables large language models to access and utilize external data sources not part of their original training.

Pay attention to data security

If your work with AI involves confidential data or proprietary information, avoid using open-source or public language models. Always rely on corporate-approved LLMs that operate securely behind corporate firewalls.

Watch out for distortions

Pay close attention to distortions that may arise in your prompts. For example, suppose a financial analyst asks an LLM to explain how yesterday’s quarterly report indicates that the company is ready for a five-year growth cycle. In that case, the response may exhibit recency bias—the tendency to overestimate recent information when predicting future events.

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Large operators are actively developing solutions to help users address these challenges. Microsoft and Google are introducing features in their services to give users better control over harmful prompts and responses. Meanwhile, Salesforce is building an AI architecture that safeguards confidential customer data by capturing it during prompt creation, preventing its sharing with third-party LLMs. This system also evaluates outputs for risks such as toxicity, bias, or privacy violations and gathers feedback to refine prompt templates.

Always investigate suspicious answers

Hallucinations and errors are inevitable, even with careful planning, as current research shows. Therefore, always approach AI responses with caution and scrutinize them for errors or suspicious signs.

 

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When users encounter an incorrect output, they often reflexively prompt the model to try again repeatedly, which can gradually degrade the quality of the response, as UC Berkeley researchers Jinwoo Ahn and Kyuseung Shin have shown. Instead, the researchers recommend identifying the specific step where the AI made a mistake and using another large language model to address that step.

This involves breaking the problem into smaller, individual components and using the new output to fine-tune the original model’s response. For example, imagine a scientist using OpenAI’s ChatGPT to develop a new polymer through stepwise computations. If an error occurs in the chain, they could turn to Anthropic Claude to break down the problematic step into smaller subproblems and provide an explanation.

This refined information can then be fed into ChatGPT to improve the answer. By applying chain-of-thought principles, this technique enhances the quality of outputs that initially appear incorrect.

 

You and AI: Master and Disciple

As LLMs grow in size and complexity, they may develop advanced new skills that were not explicitly part of their training but emerge after being customized with contextual data or knowledge. To foster this development, consider the following steps:

Provide AI with patterns of thinking

Before assigning a problem to a large language model, you can train it to adopt a specific way of thinking. For example, you can teach it the “least to most” approach, which involves breaking down a complex challenge into smaller, more straightforward tasks. Solve the least difficult task first, then use that solution as a foundation for tackling the next challenge, and so on.

Denny Zhou and his team at Google DeepMind demonstrated that this “least to most” method improved AI output accuracy from 16% to 99%.

 

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Take, for example, a marketing manager at a sportswear brand seeking AI assistance to plan a new collection. Here’s how the problem can be broken down for AI:

  1. Target Audience: Identify fitness enthusiasts who could be potential customers—a relatively straightforward task, especially for a model trained on the company’s customer data.
  2. Messages: Develop messages that emphasize performance, comfort, and style—this is a more complex and creative task that builds on the identified target audience.
  3. Channels: Select social media platforms, fitness-specific blogs, and influencer collaborations to effectively deliver these messages to the target audience.
  4. Resources: Allocate the budget—often the most debated aspect in any organization—according to the chosen channels.

Teach AI new processes

You can delegate task execution to AI by guiding it with examples within the context of your prompts.

For instance, researchers reported in Nature that they trained large language models to summarize medical information by providing examples such as radiology reports, patient queries, status updates, and doctor-patient dialogues. Their findings revealed that 81% of the summaries generated by the models were equivalent to or better than those produced by humans.

You can also train AI by providing contextual information and guiding it through questions until it solves the problem.

Take two software companies, both aiming to increase their sales. At the first company, the sales team struggles to forecast software license demand effectively. Their manager starts by supplying historical sales data to the AI and asking about the expected demand for the next quarter. Next, they provide details on customers’ software upgrades and annual budgets, inquiring about the effects of seasonality. Finally, they add detailed statistics from CRM systems and marketing reports, asking how marketing campaigns impact sales.

At the second company, the sales team focuses on improving customer selection. Their manager provides specific financial data and asks the AI to rank customers by spending. They then proceed with follow-up questions about geography, technical expertise, and other relevant criteria.

At every step, both managers teach the AI and fine-tune its ability to perform tasks within their company’s sales strategy. Incorporating organizational and industry expertise into their interactions enables the AI to adapt. The AI gains more experience with each company’s unique sales processes, producing increasingly accurate and valuable responses.

Learning new fusion skills

The widespread adoption of generative AI skills will require significant organizational investment as well as individual initiative, learning, and hard work. While some companies already offer relevant training, most have yet to develop widely accessible programs.

HBR’s research underscores this point: in their 2024 survey of 7,000 professionals, 94% of respondents expressed willingness to learn new skills to work with generative AI, yet only 5% reported that their employers were actively providing training in this area.
As a result, many individuals will need to take the initiative and stay ahead of the rapid advancements in AI.

The AI revolution is no longer on the horizon—it’s here. Leading companies leverage this transformative technology to rethink how industries, functions, and jobs are performed.
Generative AI has significantly raised the stakes, demanding that humans collaborate, act consciously to ensure trust, and continuously refine the technology and themselves to achieve better outcomes. No other major innovation in history has spread at such a rapid pace. Work will evolve faster and more profoundly than many of us can imagine. Get ready: the future of business won’t be shaped by AI alone but by the people who can harness its potential most effectively.

IT trends 2025: The most valuable skills in the tech sector

The demand for IT skills is constantly growing worldwide. Of course, the world of IT can also be divided into sub-areas, where technological trends develop and innovations appear. Thus, those who develop these areas will have a strong competitive advantage in the labor market.

Data science and artificial intelligence: driving the future

Data science and artificial intelligence (AI) represent two of the fastest-growing fields in information technology. Through data science, organizations can analyze massive datasets to inform strategic decision-making. This data-driven approach enables precise planning and helps companies identify emerging market opportunities.

AI serves as a cornerstone of automation, catalyzing transformation across industries. AI-powered solutions, particularly machine learning algorithms, excel at rapid data processing and complex pattern recognition. These capabilities boost productivity and enable innovative business models previously impossible to implement.

The convergence of data science and AI enhances organizational efficiency and creates promising new career paths. Professionals who develop expertise in AI development and data science position themselves advantageously in an increasingly competitive job market.

Cybersecurity: protecting the digital world

Cybersecurity has become a critical priority as digital technologies become increasingly central to business operations. Organizations face an evolving landscape of sophisticated cyber threats, requiring robust security frameworks and proactive risk management strategies.

Cybersecurity professionals are responsible for safeguarding sensitive information, detecting and responding to security incidents, and implementing comprehensive defense measures. The widespread adoption of cloud computing and remote work models has unprecedentedly elevated the importance of network security and data protection.

In 2025, cybersecurity expertise will be indispensable for tech professionals. Practitioners versed in ethical hacking, risk assessment, and incident response will be particularly valuable, as these competencies form the foundation of modern digital infrastructure protection.

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Cloud computing and DevOps: flexibility and efficiency

Cloud technologies remain central to the evolution of IT infrastructure. Organizations increasingly adopt cloud-based solutions to enhance business agility and reduce operational costs. Cloud services enable seamless scaling of data and applications, a critical advantage in today’s dynamic business environment.

DevOps approaches continue to gain traction by bridging development and operations processes. DevOps automates and optimizes the software development cycle while fostering inter-team collaboration. This methodology accelerates time-to-market and improves software quality.

By 2025, cloud computing and DevOps expertise will remain highly sought after. Professionals skilled in leading cloud platforms like AWS, Azure, or Google Cloud, along with proficiency in CI/CD tools, will maintain a significant competitive edge.

UX design and blockchain: the intersection of user experience and data security

User experience (UX) design is essential for the success of digital products. Users expect applications to be intuitive, fast, and visually appealing. UX designers focus on creating functional interfaces that deliver a seamless and satisfying user experience.

Blockchain technology, often associated with cryptocurrencies, offers significant data security and authentication advancements. Its applications extend to areas like supply chain tracking and digital identity protection. Decentralized systems enhance data security while minimizing misuse risks.

The intersection of UX and blockchain presents exciting opportunities. Enhancing the user experience of blockchain-based applications can drive broader technology adoption. Professionals with expertise in both fields are increasingly valuable in today’s job market.

Summary

In 2025, the most valuable IT skills will center around data science, artificial intelligence, cybersecurity, cloud computing, DevOps, UX design, and blockchain. These fields are critical for technological progress and achieving business goals.

Professionals who excel in these areas will be highly sought after. They play a key role in driving innovation and enhancing operational efficiency. The future of the IT sector relies on continuous learning and adaptability, where both technical expertise and business acumen are essential for success.

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Almost time for 2025 – Let’s recap this year in tech!

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The year has barely started, and it’s almost over! What a change! A “new” iPhone, an even newer Galaxy, a new Nvidia record, different AI capabilities in gadgets for different continents…

Of course, the business side was interested in slightly different developments. We have collected some of them!

The rise of Artificial Intelligence (AI) and Machine Learning (ML)

In 2024, AI and ML technologies will have further strengthened their presence in the enterprise. Companies increasingly integrate these solutions into their processes to increase efficiency and competitiveness. The application of AI has expanded to predictive maintenance, customer service chatbots, and data analytics, enabling companies to make faster and more accurate decisions. Copilot, Joule, Gemini, so many names in the hive, all busily buzzing, searching, and conceptualizing – for us.

The dominance of cloud services

Cloud solutions have remained at the heart of digital transformation. Companies have used hybrid and multi-cloud strategies to build more flexible, cost-effective IT infrastructures. Cloud providers compete on security, performance, and scalability of services to meet business needs.

Cybersecurity and data protection at the forefront

Unrelated to cloud growth, the rise in cyber attacks has made cybersecurity a top priority for enterprises. To comply with new regulations and GDPR, companies are investing in advanced security solutions such as zero-trust architectures and endpoint protection systems. The cyber security market has grown significantly as companies seek to protect their data and systems from threats.

Industry 4.0 and the rise of automation

The concept of Industry 4.0 has evolved, and automation has become more deeply embedded in manufacturing processes. The integration of robotics, IoT, and AI has enabled real-time monitoring and optimization of production lines, reducing downtime and increasing productivity. Companies have invested in smart manufacturing systems to gain a competitive advantage in the market.

Data-driven decision-making and Big Data

The value of data has continued to grow, and companies have increasingly relied on Big Data analytics for strategic decision-making. Data-driven approaches have enabled more accurate forecasting of market trends, better understanding of customer needs, and optimization of internal processes. In data management and analytics, companies have invested in the right tools and expertise to harness the potential of data.

Workforce trends and pay in the IT sector

In 2024, wage growth in the IT sector will slow down slightly, both in Hungary and internationally. The salaries of Hungarian IT professionals have risen differently across disciplines and positions compared to the previous year, with salary increases of up to 10-15% depending on the level of expertise and experience.

This year has not been a breakthrough year for quantum... or has it?

In 2024, IBM and Google continued their race to develop quantum computers, with both companies reaching significant milestones during the year. In late 2023, IBM unveiled its new “Condor” quantum processor, which now has a capacity of more than 100 qubits, while Google announced a breakthrough in error-correction technology that brought quantum computers closer to practical business applications. These developments have generated massive interest in the B2B sector, particularly in financial modeling, logistics optimization, and drug development.

The OpenAI GPT version is now at version 5

OpenAI announced the GPT-5 model in 2024, further revolutionizing natural language processing (NLP) and automated content generation in the enterprise sector. The new model can analyze complex business texts, process contracts, and accurately conduct automated customer service interactions. Many B2B companies, especially in the financial, legal, and healthcare sectors, have started using GPT-5 to reduce operational costs and improve the quality of their services.

Meta's new B2B strategy: Metaverse for business

In 2024, Meta (formerly Facebook) unveiled its new B2B-focused strategy focused on developing metaverse business environments. Meta’s latest platform, Horizon Workrooms, enables companies to create virtual meeting rooms, exhibition spaces, and collaboration spaces. Several large companies, such as Siemens and Accenture, have already started testing these solutions and have reported promising results in increasing the efficiency of virtual work.

Dell and Intel strategic partnership

One of the most important industry events of the year was the strategic agreement between Dell Technologies and Intel to develop AI-based data center solutions. The two companies work together on systems that can optimize enterprise IT infrastructures, reduce power consumption, and increase computing capacity. This partnership has significantly impacted IT purchasing decisions in the B2B sector. The year has also been buzzing with announcements of AI collaborations and alliances.

+1. TikTok's entry into the B2B market

TikTok, mainly in the consumer sector, announced its first B2B solutions in 2024. The new platform, TikTok for Business Pro, will allow businesses to create and distribute creative short-form ads. The platform has become particularly popular among e-commerce and technology companies, which have quickly recognised it as a new communication channel to reach business customers. However, it is another area where a Chinese upstart could catch and overtake US market leaders. It is feared that 2025 will be all about this rivalry, and we can only be glad if these conflicts occur only in social and cyberspace…

CSS, the website designer, is now in its 30s!

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Now in its third X, Cascading Style Sheets, or CSS, has come a long way. But how did it emerge?

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The history of CSS goes back to a time when the way websites looked was minimal. Håkon Wium Lie, who was working at CERN then, came up with the first idea for CSS in 1994. The aim was to make web pages separate from HTML and easy to format. In 1996, the World Wide Web Consortium, or W3C, adopted Lie’s idea, which, simple though it may seem, revolutionized the web. Interestingly, Microsoft Explorer was the first browser to support the development. One can imagine that this is why it quickly became so popular in the market. Problémát jelentett azonban a CSS számára, hogy egyes böngészők más és más módon jelenítették meg a weblapokat. Over the years, however, it has been updated to meet modern needs and now allows complex animations.

Artificial intelligence supports work at Google

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Nowadays, AI is available to the average person anywhere, anytime, and is evolving rapidly. Therefore, it is not surprising that companies like Google have jumped on board the “AI train.”

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Alphabet CEO Sundar Pichai revealed that a quarter of Google’s source code is now written by artificial intelligence. Although the code generated is tightly controlled even by flesh-and-blood software engineers, using the technology saves developers a lot of time and effort, which they can devote to more complex projects. Pichai said, “The use of AI is increasing production and efficiency,” and confirmed that, although fewer software engineers are needed, more is still required. As with everything, the use of AI has its drawbacks and advantages. In fact, with active use, the knowledge that professionals still have may be eroded in the future, and difficulties may arise in filtering out possible errors in computer-generated code.

Stack Overflow has carried out a survey on this topic: https://survey.stackoverflow.co/2024/ai

75% of the programmers surveyed use or plan to use artificial intelligence in their work. Check out our talents and inhouse trainings!

If you code with AI, errors will also be AI-like

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An interesting op-ed has been published in InfoWorld and reflects on how mistakes evolve in an AI-powered world.

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The article discusses the relationship between generative artificial intelligence (AI) and software development, highlighting that while it can be a huge productivity tool, the use of generative AI also poses a number of new challenges. Mistakes in AI-generated code can be significantly different from mistakes made by human programmers, and recognizing and correcting them requires a new kind of training and approach on the part of developers.

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The article suggests not letting artificial intelligence code autonomously but using a “man-in-the-process” approach to ensure code quality. While generative AI tools can help automate coding, human intervention is needed to ensure that AI-generated code doesn’t lead to severe security or functional issues. After all, what if you rely on data produced during programming with the same hallucinations that you find in LLMs? Or what if, in the meantime, AI develops its own language or builds in a loophole through which it can access the code for easier repair, not counting that this intrusion could be made by malicious human developers?

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Programmers of the future may play more strategic roles, where the purpose and functions of code are defined, while artificial intelligence does the more tedious part of creating code, but for now, its creativity and intuition only play a controlled role. Human programmers will be tasked with ensuring the quality of code and addressing AI-generated problems.

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The generated code can cause “alien” errors, such as creating imaginary modules or using non-existent frameworks. These defects are often difficult to detect using traditional methods. To recognize AI errors, programming managers must be trained to identify new types of errors that differ from human errors. The handling of errors caused by AI requires special preparation, and according to the author, it is easier to immediately train for this task for those who are not yet familiar with filtering out traditional human errors but can immediately specialize in AI-generated errors. In filtering out mistakes, we must overcome an essential human quality: if we encounter an error we would never make, we tend to look down on others.

In the case of artificial intelligence, such hair-raising single-minded steps will have to be expected for a long time, even though this technology beats us light years in many activities. We need to recognize that the pattern of mistakes is entirely different from that of humans. Still, it is also our responsibility to decide what mistakes we teach artificial intelligence to make.

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