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Tomasz, CTO of a Warsaw-based fintech company, spent the last eight months searching for an ML engineer. The requirements seemed standard: minimum five years of experience in production machine learning systems, knowledge of PyTorch or TensorFlow, experience with MLOps and cloud infrastructure. The company offered competitive compensation, a flexible work model, and interesting projects related to real-time financial fraud detection. Nevertheless, after eight months of intensive searching, over two hundred reviewed CVs, and twenty technical interviews conducted, the position remained unfilled.

This story is not an exception. It is the new norm in the IT market in 2025. A strategic project to implement an anti-fraud system, which was supposed to bring Tomasz’s company savings of several million zlotys annually, is at a standstill. Each month of delay means not only lost financial benefits but also the growing advantage of competitors who have already implemented similar solutions. Tomasz’s story illustrates a broader problem that most technology organizations in Poland and Europe are facing today: a structural, deepening shortage of specialists in the two most critical areas of modern IT - artificial intelligence and cybersecurity.

How deep is the AI and cybersecurity talent crisis in 2025?

“The global cybersecurity workforce gap has reached 4 million professionals, with demand growing at 12.6% annually.”

— (ISC)², Cybersecurity Workforce Study 2024 | Source

The scale of specialist shortage in artificial intelligence and cybersecurity areas reached a level in 2025 that can be described as a structural labor market crisis. Data from the latest industry reports paint a picture of fundamental imbalance between supply and demand that is not only not decreasing but systematically deepening. According to the World Economic Forum “Future of Jobs 2025” report, AI and ML specialists are at the top of the list of most sought-after competencies in the world, and the global shortage in this area is estimated at over 4 million specialists.

The situation in the European Union is equally dramatic. The European Commission in its Digital Economy and Society Index (DESI) 2025 indicates that 54% of European enterprises encounter serious difficulties in recruiting IT specialists, with positions related to AI, machine learning, and cybersecurity being the most difficult to fill. Poland, despite being one of the largest IT markets in the Central and Eastern European region, is no exception to this rule. According to data from the Polish Economic Institute, for every job posting in the ML engineering area, there are on average only 0.3 qualified candidates, which means that theoretically more than three job postings are needed to find one suitable person.

Cybersecurity is in a similarly critical situation. The global (ISC)2 Cybersecurity Workforce Study 2025 estimates the worldwide shortage of security specialists at 3.4 million people. In Poland, according to various estimates, there is a shortage of 15 to 25 thousand IT security specialists. Importantly, this is not just about quantity, but primarily about quality and specialization. Organizations need not so much general security specialists, but experts in specific, narrow fields: pentesters with experience in testing financial applications, cloud security architects familiar with specific providers, incident response specialists with GCIH or GCIA certifications, experts in OT systems security in industry.

The problem is compounded by the fact that demand for these competencies is growing at a rate significantly exceeding the pace at which the education market is able to produce new specialists. Every company implementing AI solutions, every organization subject to NIS2 or DORA requirements needs the same scarce competencies. As a result, a spiral is created where increasing competition for talent drives up salaries, but even the highest pay cannot magically create specialists who simply do not exist in the market.

Why is traditional recruitment unable to solve the specialist shortage problem?

The traditional recruitment model, based on searching for candidates through the HR department or external agencies, publishing job postings, and conducting multi-stage selection processes, was designed for a reality that no longer exists. In a world where for every qualified AI or security specialist there are a dozen, and sometimes several dozen, competing job offers, standard recruitment methods become strategically inadequate.

The first and most obvious barrier is time. The average recruitment time for an ML engineer position in Poland in 2025, according to Bulldogjob data, ranges from 4 to 8 months. For highly specialized roles, such as MLOps engineers with production system experience or cloud security architects, this time can extend to a year or longer. Each month represents not only the direct cost of an unfilled position but primarily lost business opportunities, delayed projects, and growing frustration among teams that must work understaffed.

The second barrier is global competition. AI and cybersecurity specialists are not geographically limited in their employment opportunities. An experienced ML engineer from Warsaw can just as easily work remotely for a company in Silicon Valley, London, or Berlin, often for significantly higher compensation. Polish companies, even those offering attractive conditions by local standards, compete not only with each other but with the entire global market. This competition is asymmetric - global technology corporations have incomparably larger recruitment budgets and brand attraction power.

The third barrier is the cost of ineffective recruitment. According to various industry estimates, the total cost of a failed recruitment can range from 50% to even 200% of the annual salary for a given position. For highly specialized AI or security roles, we are therefore talking about amounts in the range of several hundred thousand zlotys. This cost includes not only direct expenses for the recruitment process but also onboarding costs, reduced team productivity during the onboarding period, and in case of early departure - the need to start the entire process from scratch.

The fourth barrier is the problem of cultural and project fit. Even if a specialist with the right technical competencies is found and hired, there is no guarantee that they will fit the specifics of the company’s projects and organizational culture. In the traditional employment model, the company discovers this only after several months of work, when both financial and emotional costs of separation are already very high.

What are the real costs of unfilled AI and security positions for organizations?

The costs of unfilled positions in AI and cybersecurity areas extend far beyond simple calculations based on lost work hours. This is a multidimensional problem whose true impact on the organization manifests in strategic, operational, and reputational areas. Understanding the full picture of these costs is crucial for justifying investments in alternative talent acquisition models.

Direct costs are the most obvious, although often underestimated. An AI project that cannot start due to a lack of ML engineers means not only a delay in schedule implementation but primarily a postponement of return on investment. If implementing a predictive system is supposed to bring the company savings of 10 million zlotys annually, each month of delay is nearly a million zlotys in lost value. In the case of unfilled cybersecurity positions, costs can be even higher - a single security incident in a company without adequate response resources can cost tens or even hundreds of millions of zlotys in regulatory fines, remediation costs, and lost revenue.

Indirect costs include the overloading of existing teams that must take over the responsibilities of unfilled positions. This leads to burnout, decreased quality of work performed, and increased turnover. According to Gallup research, burned-out employees are 63% more likely to look for a new job. In a situation where it is already difficult to find specialists, losing existing team members due to work overload creates a vicious cycle that can be very difficult to break.

Strategic costs are the most difficult to measure but often the most important. A company that is unable to implement AI solutions loses competitiveness compared to those that have succeeded. An organization that cannot ensure an adequate level of cybersecurity risks losing the trust of customers and business partners, may not meet regulatory requirements enabling entry into new markets, and in extreme cases may become a victim of a cyberattack that threatens its continued existence.

Consider a specific example from the Polish market. The average salary of an ML engineer in Poland in 2025, according to No Fluff Jobs data, is approximately 28-35 thousand zlotys per month on a B2B contract. With a six-month search period, the direct cost of an unfilled position is approximately 200 thousand zlotys. However, if this specialist was supposed to work on a project generating 500 thousand zlotys in value per month, the actual cost of delay is 3 million zlotys. Added to this is the cost of team overload, potential loss of other employees, and difficult-to-estimate costs of lost business opportunities.

What is staff augmentation and how does it differ from traditional outsourcing?

Staff augmentation, or personnel augmentation, is a collaboration model in which external IT specialists join the client’s internal team, working under their direct supervision and becoming an integral part of the organization for the duration of the project or longer. This is a fundamentally different approach from both traditional recruitment and classic outsourcing, although it is sometimes confused with both.

In the traditional outsourcing model, a company commissions an external provider to perform a defined scope of work or manage an entire project. The provider is responsible for results but retains full control over how they are achieved - they select their own team, apply their own methodologies and processes. The client has limited influence on implementation details and often little insight into how the team actually works. Outsourcing works well for clearly defined, repetitive processes but creates problems when a project requires close integration with the internal team, rapid adaptation to changing requirements, or deep understanding of the organization’s business specifics.

Staff augmentation solves these problems while maintaining the key advantages of working with external specialists. Experts acquired through augmentation work directly within the client’s structures - they participate in the same meetings, use the same tools, and are subject to the same processes as internal employees. The difference is that their formal employer remains the company providing staff augmentation services, which handles all administrative aspects: salaries, benefits, legal and tax matters.

Key differences between staff augmentation and outsourcing include several dimensions. First, control over the specialist’s work. In the staff augmentation model, the client decides what the specialist works on, what methodologies they use, and how they integrate with the team. Second, responsibility for results. In outsourcing, responsibility for results lies with the provider; in staff augmentation - with the client, who manages the specialist like their own employee. Third, flexibility. Staff augmentation allows for easy scaling of the team up and down, changing the specialist’s scope of responsibilities, or even replacing them if they prove unsuitable for a specific project.

This model is particularly valuable in AI and cybersecurity projects, where close collaboration between teams, access to sensitive data and systems, and deep understanding of business context are required. An external ML engineer working in the staff augmentation model can participate in all phases of the project - from understanding the business problem, through designing the solution, to its implementation and maintenance - just like a full-time employee, but without a months-long recruitment process.

How does staff augmentation solve the AI specialist shortage problem?

The shortage of AI specialists represents one of the greatest challenges for organizations pursuing digital transformation. Staff augmentation offers a concrete, practical solution to this problem, addressing each of the main reasons why traditional recruitment fails.

The first and most important benefit is a radical reduction in the time to acquire a specialist. A partner specializing in staff augmentation, such as ARDURA Consulting, already has an established, verified database of AI specialists ready to work. Instead of starting from scratch - publishing a job posting, waiting for applications, conducting screening - the company can present suitable candidates within days or weeks. The technical verification process has already occurred, competencies and references have been checked. The client only conducts final interviews to confirm fit with the project specifics and organizational culture.

The second benefit is access to a global talent pool. A staff augmentation partner is not limited to the local labor market. They can acquire specialists from around the world, leveraging their contact networks, presence in various regions, and knowledge of specific market characteristics. For a company looking for, for example, an expert in recommendation systems with e-commerce experience, this means a much larger pool of potential candidates than with independent recruitment limited to Poland or even Central Europe.

The third benefit is the ability to acquire highly specialized competencies that the company may need only for a specific period. Many AI projects require different skills at different stages. At the beginning, a data scientist may be needed for data analysis and preparation, then an ML engineer to build models, later an MLOps engineer for production deployment, and finally a monitoring engineer for system maintenance. Hiring all these specialists permanently may not make economic sense. Staff augmentation allows flexible engagement of appropriate competencies exactly when they are needed.

The fourth benefit is risk reduction. In traditional recruitment, a company makes a long-term commitment based on a few hours of job interviews. In the staff augmentation model, a specialist works with the team for a specified time during which both parties can assess mutual fit. If they prove unsuitable, replacement is much simpler than with a full-time employee. Many staff augmentation agreements also include a try-and-hire option, which allows for later hiring of the specialist as a permanent employee if the collaboration works well.

The fifth benefit is knowledge transfer and best practices. Experienced AI specialists acquired through staff augmentation have often worked in many different organizations and projects. They bring not only technical knowledge but also familiarity with proven solutions, typical pitfalls, and industry standards. This external perspective can be invaluable for organizations that are just building their competencies in the AI area.

How does staff augmentation address cybersecurity challenges?

Cybersecurity presents organizations with specific challenges that make staff augmentation a particularly attractive model for acquiring competencies. The nature of threats, regulatory requirements, and the pace of technological change in this area create an environment where flexibility and speed of access to specialists become critical factors.

The first specific cybersecurity challenge is the enormous diversity of required specializations. Security is not one competency but an entire spectrum of narrowly specialized fields: penetration testing, malware analysis, incident response, application security, cloud security, network security, compliance and GRC, identity management, cryptography, and many others. No organization, apart from the largest corporations, can afford to permanently employ experts in all these areas. Staff augmentation allows acquiring specific specialization for the time of a specific project or need - conducting an audit, implementing a SIEM system, responding to an incident, or preparing for certification.

The second challenge is regulatory pressure. The NIS2 directive, DORA regulation for the financial sector, GDPR requirements for data protection - all these regulations impose specific cybersecurity obligations on organizations. Many companies discover they lack internal competencies to meet these requirements within designated timeframes. Staff augmentation enables rapid acquisition of compliance experts who will help guide the organization through the process of adapting to regulations, without the need to build these competencies internally on a permanent basis.

The third challenge is the unpredictability of needs. A security incident can happen at any time and requires immediate, specialized response. Organizations cannot afford to start a recruitment process when a security breach has occurred. Staff augmentation partners offer the ability to rapidly expand the team in crisis situations - experienced incident responders can join the team within days, and sometimes even hours.

The fourth challenge is the continuous evolution of threats and technologies. The cybersecurity landscape changes faster than any other IT area. New attack vectors, new defense tools, new platforms requiring security - all this requires continuous updating of competencies. Security specialists acquired through staff augmentation, working with many different clients, are up to date with the latest trends and threats. They bring current knowledge to the organization that the internal team, focused on daily operations, may not have had time to absorb.

The fifth challenge is the sensitivity of the area and trust requirements. Security specialists have access to the organization’s most sensitive systems and data. A professional staff augmentation partner conducts thorough candidate verification, including background checks, reference verification, and certification confirmation. They also offer appropriate agreements protecting the client’s interests, including confidentiality clauses and non-compete restrictions. This layer of verification and formalization gives organizations confidence that would be difficult to achieve with independent recruitment in the local market.

What financial benefits does staff augmentation provide compared to traditional recruitment?

Financial analysis of staff augmentation requires looking beyond simple calculations of hourly or monthly rates. The true picture of costs and benefits reveals itself only when considering the total cost of ownership (TCO) and the value a specialist brings to the organization.

Let us start with recruitment costs. According to various estimates, the total cost of recruiting a senior IT specialist ranges from 30 to 60 thousand zlotys, including the time of the HR department and managers, fees for recruitment agencies, and costs of job postings and employer branding. For difficult-to-fill AI and security positions, where the process takes many months and often ends in failure, these costs can multiply. Staff augmentation eliminates most of these expenses - the company pays for a ready, verified specialist without the costs of searching for them.

The next element is onboarding and training costs. A new employee, even the most experienced one, needs time to fully understand the organization’s specifics, its systems and processes. During this period, their productivity is reduced, while they also require support from other team members. It is estimated that full productivity of a new IT employee is achieved after 3-6 months. Staff augmentation specialists, accustomed to quick onboarding in new environments, achieve efficiency much faster - their work model is based on the ability to adapt quickly.

Administrative costs constitute another element of the equation. A full-time employee generates a constant administrative burden: payroll calculations, tax and social security settlements, benefits management, vacations, sick leaves, health and safety training, and much more. In the staff augmentation model, these obligations remain with the service provider partner. The client receives one invoice and does not need to build internal competencies for administrative handling of additional employees.

Financial flexibility is another benefit. Traditional employment is a fixed cost - salary must be paid regardless of whether there is currently a project requiring the given competencies or not. Staff augmentation transforms this fixed cost into a variable cost, closely linked to actual business needs. The organization can engage specialists exactly when they are needed and end cooperation when the project is completed or needs change.

Finally, the opportunity cost of an unfilled position must be considered. If a company spends six months looking for an ML engineer for a project that was supposed to bring millions of zlotys in annual savings, the cost of this delay can far exceed any difference in rates between a full-time employee and a staff augmentation specialist. In many cases, faster project initiation through staff augmentation brings savings that completely compensate for the higher rates of external experts.

How quickly can specialists be acquired through staff augmentation?

Speed of specialist acquisition is one of the key advantages of staff augmentation over traditional recruitment. The difference is not marginal - we are talking about reducing time from months to weeks, and in some cases even to days.

A professional staff augmentation partner already has an established database of verified specialists. These are not random CVs collected from job portals, but carefully selected experts with whom the partner maintains regular contact, knows their competencies, project preferences, and availability. When a client reports a need, the partner does not start the process from scratch - they reach into the existing pool and identify candidates matching the profile.

A typical process of acquiring a specialist through staff augmentation proceeds in several stages. In the first days, requirements analysis and candidate database search take place. The partner consults with the client on details regarding required technical competencies, industry experience, and work model. Based on this, they identify potential candidates from the database and, if necessary, launch additional sourcing.

In the next step, candidate presentation occurs. The partner presents the client with 2-5 carefully selected profiles, along with information about competencies and availability. Then client verification takes place - usually 1-2 rounds of interviews with selected candidates. Unlike traditional recruitment, where each round of interviews can take weeks, in staff augmentation we are dealing with a small number of already verified individuals.

After the decision is made, finalization and onboarding follow. The partner handles all formalities, and the specialist can start work almost immediately. The total time from reporting a need to the specialist starting work typically ranges from 2 to 4 weeks.

Compare this to traditional recruitment, where publishing a job posting and collecting applications alone takes 2-4 weeks, initial screening takes another 2 weeks, a multi-stage interview process takes 4-8 weeks, negotiations and formalities take 2-4 weeks, and the notice period at the previous employer often adds another 1-3 months. In total, we are talking about 4-8 months from the decision about the need to the new employee starting work, assuming the recruitment is successful on the first attempt.

Which AI and cybersecurity roles are most frequently filled through staff augmentation?

Staff augmentation works across the entire spectrum of AI and cybersecurity roles, but certain positions are particularly often filled in this model due to their specialization, recruitment difficulty, or temporary nature of the need.

In the area of artificial intelligence and machine learning, the most frequently sought-after roles include ML Engineers - specialists responsible for building, training, and optimizing machine learning models in a production environment. This role requires a combination of data science and software engineering competencies, making it particularly difficult to fill.

Data Scientists are another frequently sought-after role - experts in data analysis, building predictive models, and extracting business knowledge from data. Although this role is somewhat broader and less specialized than ML Engineer, high-quality data scientists with experience in specific industries remain in short supply.

MLOps Engineers, specialists responsible for infrastructure and processes enabling deployment of ML models in production - this is a relatively new role that combines DevOps competencies with knowledge of machine learning system specifics. There are very few such specialists in the market.

NLP Engineers are experts in natural language processing, particularly sought after in the context of large language model (LLM) implementations and conversational systems. Computer Vision Engineers specialize in image and video recognition systems, sought after in manufacturing, automotive, and retail.

AI Architects are experienced specialists responsible for designing the architecture of entire AI systems, integration with existing infrastructure, and defining technology standards.

In the cybersecurity area, the most frequently filled roles include Penetration Testers - security testing specialists who identify vulnerabilities in systems, applications, and infrastructure. Their work often has a project nature, making staff augmentation a natural collaboration model.

Security Architects are experts who design an organization’s security architecture, define standards, and select technology solutions. This is a role requiring deep experience, often sought for specific transformation projects.

SOC Analysts, security operations center analysts, monitor threats and respond to incidents. Organizations often supplement their SOC teams with external specialists, especially for night and weekend shifts.

Cloud Security Engineers are specialists in cloud environment security - AWS, Azure, GCP. This is one of the fastest-growing specializations due to widespread migration to the cloud.

GRC Specialists, experts in governance, risk, and compliance, help organizations meet regulatory requirements such as NIS2, DORA, or ISO 27001. Incident Response Specialists are specialists in responding to security incidents, often engaged in crisis situations.

How to effectively implement staff augmentation in an organization?

Effective implementation of staff augmentation requires a thoughtful approach that ensures maximizing benefits while minimizing potential problems. Organizations that achieve the greatest success with this model treat it not as an ad hoc solution to staffing problems but as a strategic element of talent management.

The first step is precisely defining needs. Before an organization approaches a staff augmentation partner, it should accurately determine what competencies it is looking for, for how long, and what specific tasks need to be performed. The more precise the brief, the more accurate the candidate proposals will be.

The second step is selecting the right partner. When choosing, it is worth paying attention to specialization in specific technology areas, experience in serving similar organizations, the candidate verification process, and references from other clients. A partner with expertise in AI or cybersecurity will be much better able to assess candidates’ competencies than a generalist provider.

The third step is preparing the organization to receive external specialists. This includes technical matters - system access, equipment, workstation - but also organizational and cultural aspects. The team should know that new members are joining, understand their role, and be prepared for collaboration. The manager responsible for the specialist should have planned time for their onboarding and ongoing supervision.

The fourth step is effective onboarding. Although staff augmentation specialists are accustomed to quick onboarding in new environments, the organization should provide them with necessary support: project documentation, access to the right people on the team, clear definition of expectations and goals. Well-conducted onboarding shortens the time to full productivity and increases satisfaction for both parties.

The fifth step is ongoing management and communication. A staff augmentation specialist should be treated as a full-fledged team member - participating in meetings, receiving information about the project’s business context, having the opportunity to ask questions and raise issues. At the same time, it is worth maintaining regular contact with the staff augmentation partner, who can help resolve any problems and ensure that cooperation proceeds according to expectations.

The sixth step is long-term planning. Staff augmentation does not have to be a one-time solution. Organizations can build lasting relationships with partners who, over time, better understand their specifics and are able to provide increasingly accurate candidates. It is also worth considering try-and-hire options for particularly valuable specialists and planning which competencies the organization ultimately wants to build internally and which will remain the domain of external cooperation.

Why is staff augmentation becoming a strategic tool rather than just a tactical solution?

As the IT talent crisis deepens and becomes a permanent element of the market landscape, staff augmentation is evolving from an ad hoc staffing solution into a strategic organizational management tool. Business and technology leaders increasingly recognize that a flexible competency acquisition model is not just a way to fill a gap in the team but the foundation of a modern approach to building organizational capabilities.

Strategic thinking about staff augmentation means treating it as an integral part of the company’s operating model. Instead of asking “how quickly can we fill this position?”, organizations ask “what is the optimal mix of internal and external competencies that will allow us to achieve our business goals?”. The answer to this question requires analysis of which competencies are key to competitive advantage and should be built internally, and which are supportive or project-based in nature and can be more efficiently acquired externally.

In the AI and cybersecurity area, this analysis often leads to a hybrid model. A company might, for example, build an internal AI team with a few key specialists who understand business specifics and maintain knowledge continuity, while supplementing them with external experts for larger projects or specialized initiatives. In cybersecurity, an internal CISO and core team can be supported by external specialists in areas such as penetration testing, compliance, or incident response.

Such an approach brings a number of strategic benefits. First, it increases organizational resilience to market volatility. The company is not solely dependent on its own recruitment capability, which may fail in times of particular talent shortage. Second, it ensures access to the latest knowledge and best practices. External specialists, working with many different organizations, are up to date with technology trends and can transfer proven solutions between projects. Third, it enables flexible scaling of IT competency investments in line with business cycles and strategic priorities.

Staff augmentation is also becoming a risk management tool. An organization that relies solely on internal competencies is exposed to significant risk in the event of key employees leaving. A diversified model, combining an internal team with flexibly acquired external experts, is much more resilient to individual staffing events.

How does ARDURA Consulting support organizations in acquiring AI and cybersecurity specialists?

ARDURA Consulting has specialized for over a decade in providing the highest quality IT specialists for organizations in Poland, Europe, and the Middle East. In the area of staff augmentation, we have built a particularly strong position in the two most scarce market segments: artificial intelligence and cybersecurity.

Our advantage stems from three pillars. The first is deep technological specialization. Our recruitment team consists of people with technical backgrounds who understand the difference between an ML engineer and a data scientist, between a pentester and a security architect. We can conduct substantive conversations with candidates and precisely assess their competencies, so clients receive carefully selected specialists rather than random CVs.

The second pillar is a global talent network. Over the years, we have built relationships with AI and security specialists from around the world. Our database includes experts from Poland, other Central and Eastern European countries, as well as specialists ready to work remotely from any location. This allows us to provide candidates with competencies that simply do not exist in the local market.

The third pillar is a partnership approach to the client. We do not treat staff augmentation as transactional service sales but as a long-term relationship based on understanding the client’s business. Our dedicated Account Managers remain in constant contact with clients, monitor the progress of cooperation, and proactively identify areas where we can deliver additional value.

In practice, this means that when a client comes to us with a need to acquire an ML engineer, we do not limit ourselves to presenting a few CVs. We conduct a detailed conversation about project goals, technologies used, and organizational culture. Based on this, we search our database and present carefully selected candidates whose competencies we have verified in technical interviews.

We offer flexible collaboration models: short-term engagement for a specific project, long-term team support, try-and-hire with the option to take over the specialist as a permanent employee, as well as building entire project teams. Our goal is to deliver real business value - not just filling a position, but ensuring that the specialist brings competencies that translate into project success.

If your organization is struggling with a shortage of AI or cybersecurity specialists, if strategic projects are stalled due to lack of workforce, if traditional recruitment is failing - we invite you to talk. Our experts will help identify the optimal solution and provide the competencies you need in a timeframe that cannot be achieved by other methods.

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