Strategic HR digitalisation instead of makeshift solutions

You come back to the office from lunch with the new employee. Your chatbot assistant contacts you: it has recognised the lunch appointment in your calendar and asks for a photo of your receipt for expense recording. You upload the photo while simultaneously checking the job interviews for the afternoon. No stress: the recruitment system has already filtered out the best candidates for the position and automatically arranged the appointments with everyone involved. This leaves you as an HR professional free to focus on what matters - real conversations at eye level. Only the garlic sauce at lunchtime was perhaps not a good idea. 

A tangible reality, not utopia 

What sounds like a dream of the future has long since become reality. Such use cases are already in use today. Nevertheless, according to this year's market research study by HR Campus, only just under 6% of the 1,500 people surveyed consciously use AI tools in the workplace. In companies with over 1,000 employees, the figure is slightly higher, but here too, it is only just under one in ten people. 

Many HR teams that are looking into AI applications and testing initial use cases are quickly disappointed. The hoped-for efficiency gains fail to materialise. So, the technology is put aside again. People forget that AI cannot realise its potential without clean, structured data. The quality of the underlying data is crucial for success. You can compare it to building a house: if you don't lay a stable foundation, you shouldn't be surprised if the building wobbles. A solid HR database must be in place first. 

The challenges of HR transformation

Many HR organisations have been struggling with the digitalisation of their processes for years. Those who have been working on digital transformation in HR for some time (often with limited success) should act now at the latest. With the rapid development of AI applications, it has become essential for organisations to have a clean and consistent HR data landscape. We have been facing the following hurdles in HR for years.

• Outdated or incomplete master data: Employees change positions, departments or locations. However, many of these changes do not end up in the system. This leads to problems with role-based access rights and thus jeopardises data protection.
• Manual and inconsistent data entry: HR data is often entered manually into multiple systems. Typing errors and contradictory information are the result. One example: in one system it is called Project-Manager, in another PM or Project Manager. This lack of data synchronisation can mean that training courses must be laboriously assigned manually. Personalisation and automation fall by the wayside.
• Data silos and media disruptions: Applicant management, personnel administration, remuneration, talent development and other processes often run in separate software solutions that are not linked to each other. The systems do not talk to each other, even though the processes are closely interlinked. As a result, data flows and synergies remain unutilised.

Those who struggle with such challenges in the company will be disappointed by AI, as mentioned above. AI is not a magic spell. You can't just slap it over faulty processes and hope for miracles. If the data foundation is shaky, the AI roof will collapse sooner or later.

The five building blocks for an AI-capableHR IT landscape

People often talk about the potential of AI in HR. However, the real challenge lies not in the technology itself, but in the prerequisites it requires. Without a solid foundation, the use of AI remains ineffective. Five central building blocks are required for AI to work precisely, efficiently and scalably in HR: 

1. Clean HR database
   Without up-to-date data, AI functions cannot deliver reliable results. It is therefore important to maintain employee master data, organisational structures and skill profiles. Incorrect, outdated or duplicate data records need to be cleaned up. This requires clear definitions and standards within the company.
2. Digitised HR processes
   Processes and workflows need to be digitised. This does not simply mean switching from paper to Excel documents, but rather mapping processes cleanly and completely in systems and tools, especially those that can be easily accessed via interfaces (APIs). This applies to everything from HR administration and applicant management to performance appraisals and more. By engaging early with the right solutions, we can take advantage of AI functionalities already in place.
3. Networked HR systems
   AI requires access to all relevant HR data sources to function properly and actually reduce the workload. To utilise data across systems, isolated data silos must be avoided. Either different HR systems communicate with each other via interfaces or a standardised HR suite, that is as comprehensive as possible, is used. Here too: good HR suites already cover many AI use cases today or are currently working on developing them.
4. Scalable HR IT architecture on a cloud basis
   Flexible cloud solutions are needed instead of rigid, local systems. They enable access to up-to-date data, anytime and anywhere. At the same time, companies benefit from ongoing developments and greater security. The cloud is the foundation for rapid scaling and modern AI applications.
5. Real-time data availability
   AI thrives on current information. The more accurate and current the data, the more effective the analyses and predictions. In addition to cloud solutions, interfaces and regular data cleansing, self-service portals and digital time recording systems all help to keep data current. They ensure that employees can update their personal data, absences and competences themselves in real time - and are therefore always up-to-date.

By investing in these foundational elements, organizations enable more accurate analyses, streamlined HR processes, and smarter decisions powered by AI – building a strong base for future scalability.

AI is here - also for SMEs

AI applications are constantly being developed further, both by large HR system providers and innovative start-ups. This means, AI is not just meant for large corporations. SMEs can also benefit from AI. Many use cases can already be implemented on a small scale to optimise processes or increase efficiency in day-to-day operations. If you have a stable foundation and start early, you will be ready for more faster: precise fluctuation forecasts, automated scills gap analysis for strategic personnel planning and development, individual and personalised experiences for employees resulting in stronger commitment – we can see, there are (almost) no limits to creativity. 

More room for people

HR is and will continue to be human - provided that data management is strategically prioritized. The full potential of AI can only be exploited with a solid foundation - and this consists of the five elements mentioned above: a reliable HR database, digital HR processes, networked and integrated systems, a scalable, cloud-based HR IT architecture and the availability of real-time data. The result is more than just technological intelligence. The use of artificial intelligence creates the freedom to focus on emotional intelligence. For real encounters and what really matters: people are at the centre of everything we do.