The pace of change organizations have experienced in recent years has accelerated dramatically. An HR function focused mainly on operational process execution can no longer respond effectively to emerging challenges. What’s needed is a shift toward HR as a strategic partner – one that actively supports business decisions and organizational growth.
This is not a matter of intuition or a passing trend. It is clearly confirmed by analyses and reports from leading organizations such as Gartner, McKinsey, Deloitte, and ADP. Their conclusions are consistent: HR can no longer operate as a set of isolated processes and initiatives. Instead, it must function as a coherent, integrated system – driven by data and technology, and grounded in a deep understanding of the organization’s current needs as well as those that will emerge in the years ahead.
Artificial intelligence has quickly become part of everyday work in organizations, including HR, where it is applied in areas such as recruitment, data analysis, and employee communication. However, in many companies, the rapid adoption of AI tools has far outpaced the development of clear guidelines for their responsible use. As a result, AI is often used in a fragmented and uncontrolled manner. Managers and employees may adopt available solutions on their own, without the involvement or oversight of HR or IT teams. This phenomenon, known as Shadow AI, carries real risks: sensitive personal and company data may be exposed, and personnel decisions can be influenced by algorithms whose workings are neither fully understood nor monitored. Furthermore, such practices may conflict with applicable regulations and raise serious ethical concerns.
This is especially critical in HR, one of the most sensitive areas of any organization. HR decisions affect people – their salaries, development, promotions, and departures – and are subject to legal regulations as well as high ethical standards. Coupled with new EU regulations on AI, the absence of clear rules is not a sign of innovation, but a genuine systemic gap. The solution lies in establishing consistent guidelines for AI use across the organization. In practice, this goes beyond policies or regulations – it means creating a shared decision-making framework that clearly defines:
In many organizations, AI is implemented in a piecemeal way – as a collection of individual tools or initiatives – without a coherent vision, alignment with business goals, or consideration of its impact on the HR operating model. As a result, rather than improving efficiency, AI can increase decision-making chaos and blur accountability. The solution is not to block AI, but to give it a clearly defined role within the decision-making system. This requires a human-in-the-loop approach, a consistent organizational framework, and centralization of tools within a single ecosystem. Such a model minimizes the risks of Shadow AI, enhances transparency, and makes it easier to monitor and audit the use of AI across the organization.
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In many organizations, decisions about recruitment, development, or project assignments are still made without a full understanding of the competencies available within the company. Despite significant HR investments in talent acquisition and development, the question of what skills are truly present – and how well they align with the organization’s future growth needs – often remains unanswered or relies on intuition. At the same time, the way we work is evolving. More and more tasks are carried out in project-based or temporary teams, with fluid roles that don’t fit neatly into traditional job descriptions. This makes managing competencies through conventional organizational structures even more challenging.
The solution to these challenges is adopting a skills-based HR approach, where competencies become the core unit for work planning, development, and resource allocation. A shared competency vocabulary that is clear to HR, managers, and employees is essential – one that describes skills consistently and comparably, regardless of formal job titles. With this foundation, organizations can systematically map both current competencies and those needed for the future. Teams can then be understood based on the actual work they perform in projects and roles, rather than through static organizational structures. Linking competencies to roles, projects, development, and internal mobility enhances organizational flexibility and enables data-driven decision-making.
Ultimately, this approach transforms HR’s role from simply administering positions and training to architecting a competency system that actively supports the organization’s adaptability and growth.
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For years, many organizations managed salaries in a fragmented way – different rules applied to different teams, decisions were often discretionary, and criteria for promotions and raises were unclear. As long as salary information remained largely confidential, these inconsistencies went unnoticed. Today, however, salary transparency is no longer a distant trend; it has become a real operational and strategic challenge for HR. Regulatory changes in the European Union, coupled with growing employee expectations, mean that compensation can increasingly no longer rely on informal arrangements or opaque decision-making.
In an era of increasing transparency, it is essential to move beyond reacting to individual salary issues and instead build a consistent, logical remuneration system. This system is founded on clear pay structures and ranges linked to roles and required competencies, along with transparent criteria for pay and promotion decisions that align with performance appraisals and competency development. In this model, HR becomes the steward of the remuneration logic, providing managers with a clear decision-making framework and supporting arguments that enable predictable, fact-based conversations with employees. Such a system not only ensures compliance with regulatory requirements, but also strengthens trust, reduces the risk of conflicts and turnover, and allows for strategic management of labor costs. Transparency thus becomes a natural outcome of well-defined rules – not an end in itself.
The Pay Transparency Directive is an EU regulation (EU Directive 2023/970) that aims to strengthen the principle of equal pay for women and men for the same work or work of equal value. It introduces obligations regarding pay transparency and mechanisms for enforcing equal treatment in organizations.
EU Member States have until June 7, 2026, to transpose the directive into national law. By that date, organizations should have structured pay scales, transparent criteria for remuneration decisions, and consistent data to comply with the new requirements.
In many organizations, HR data still primarily serves a reporting function. Reports tell you how many employees were hired, how many left, what the absenteeism rate is, and what labor costs look like. While this information is important, in today’s fast-paced business environment, it is often not enough. Relying solely on past data is no longer a sufficient basis for effective decision-making.
Historical data tells us where the company has been, but it does not answer the question of what will happen in three, six, or twelve months. Without predictive analytics, HR reacts late to key phenomena such as increasing turnover, declining engagement, or growing competency gaps. As a result, HR actions are reactive and are only taken when the problem is already visible and costly. This, in turn, shifts the HR function to an administrative role, focused on fixing the situation rather than consciously shaping it.
Shifting to this model requires moving from reporting to forecasting. Mature HR functions integrate data from recruitment, development, absenteeism, performance, and engagement into a single analytical ecosystem. The key is not just collecting data, but interpreting it in the context of trends and correlations. This approach enables HR to identify risks early – such as potential waves of key employee departures, drops in team productivity, or gaps between current competencies and future organizational needs. At the same time, predictive analytics helps pinpoint the optimal moments for investments in development, structural changes, or role adjustments. As a result, HR moves beyond intuition and hindsight, providing management with actionable forecasts, scenarios, and recommendations that directly support strategic decision-making.
All of the challenges described share one key characteristic: they don’t concern individual processes, tools, or regulations, but the organization’s entire decision-making system. Artificial intelligence, competencies, roles, remuneration, and data are increasingly interconnected. A change in any one area immediately impacts the others, either strengthening or weakening the coherence of the system as a whole. The year 2026 doesn’t herald a sudden revolution in HR, but it makes one thing clear: a model based on reacting to immediate needs, fragmented initiatives, and looking backward is no longer sufficient. Organizations that want to remain stable and adaptable need HR that thinks systemically, designs decisions, and drives strategic outcomes – rather than merely servicing processes.
Modern HR increasingly integrates data on work, competencies, salaries, technology, and regulations into a single, coherent system. This approach gives HR greater responsibility and a real influence on the organization’s strategic direction. The future of HR is not just about implementing new tools or reacting to crises – it is about consistency, predictability, and the quality of decisions that affect people. In the years ahead, these decisions will build employee trust, enhance organizational resilience, and determine the company’s long-term capacity for growth.
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