DIGI SOCIETY

Through the years, HR has become more and more digital. In the last couple of decades, there has been enormous leaps forward in HR due to digitalisation. It also depends on the company to which extent digital solutions are put into use in HR practices. For example, on my career I have worked both in a company that revolved around paper and face to face interaction, as well as in a company that had no paper at all, and everything done digitally.

But what is HR? To put it simply, HR is all about managing people. But in reality HR can be a lot of different things, including recruitment and onboarding, training and development, compliance management, employee engagement or workforce planning. If you look more deeply into what these actually mean, it could be managing well-being, health and safety, recruitment, creating and managing surveys, helping employees with career planning, handling compensation and benefits, doing employer branding, solving conflicts, planning events, coaching managers, processing data, making sure the company follows the Collective Agreement and labor law, and so much more.

Because HR can be so much, I have decided to explore only a few different aspects from the digitalisation point of view.

Digitalising recruitment

Digitalisation has made a big impact on recruitment. Data is no longer stored on a piece of paper in some folder in one of the locked cabinets in some corner of the office, but digitally. In a way, digitalisation has made recruitment much more effective and targeted to correct audience.

The first stage of recruitment is creating a job description and advertising it. Open position ads are no longer posted on newspapers, but on digital platforms such as Duunitori or LinkedIn. You could even have AI draft the ad itself. The digital ads also reach a much wider audience than before, and can be targeted to the experts on that specific field, rather than hoping that someone in a specific area where the newspaper is printed would have the knowledge the company needs, that someone knows someone fit for a role, or that the right person would walk through the door asking for a job. It is possible to find an exact fit for a role 3000 km away.

The second step is called screening, handling applications, which mostly happens on digital recruitment systems. When applications come, HR or a recruiter gets a notification, and can view if the candidate is fit for a job. In some systems you could also use AI to filter the applications automatically: to reject or to move to an interview stage, based on for example if the applicant meets the minimum requirements of the job. No more drowning in a pile of paper CV’s for HR!

It is also possible to send automatic replies that are pre-defined. For example if you (or AI) categorises the reason for not hiring “Does not meet minimum requirements” or “Not a fit to the team culture”, the system can send an automatic rejection email that is tailored for that exact reason.

Interviews can be done remotely, through Microsoft Teams for example, from anywhere, and employment contracts signed digitally. The HR can transfer the candidate’s data directly into an HR information system, where an employee profile is created and employee data managed. Simple, right?

Where recruitment has become much more efficient and targeted with digital solutions and with the help of an AI, some might also argue that is has become cold, even brutal. There is a lot of competition now that anyone from anywhere can apply for a job and it is incredibly hard to stand out or land especially entry and junior level jobs where all candidates have a similar working history, or none at all. In addition, if a candidate happens to have one day less experience than what the minimum requirement for a job is, the application could be rejected automatically. AI does not have human judgment and might not recognise potential. Maybe the applicant has other qualities that would make them a better candidate than someone else that has 2 years more experience, but AI does not usually know how to evaluate this.

In the end, digitalisation has transformed recruitment into something way more efficient, but also more complex. Digital solutions make it easier to find the right people for a certain role, but on the other hand unfortunately also easier to overlook them. I believe that in the future it is important to find the balance between automation through AI and the human touch, empathy. When digital solutions and AI are used thoughtfully, they can make the recruitment process better and easier for all both HR and the candidate.

Digitalising HR data

We don’t need to go far back, when employee data was all over the place. Parhaps it was on a piece of paper in a folder somewhere in one of the locked cabinets. Perhaps it was on an spreadsheet somewhere in one of the computer folders. Some companies still have their information in many different places, but there are also better ways to handle this.

In many companies, employee data is stored in an HR system like Sympa, Workday or Mepco. These systems can combine all the information from different systems into one place. The systems might contain all the information from employment contracts to job descriptions, from training history to salary, from absences to annual reviews. The systems can also be taught to erase information according to GDPR legislation, to have local holiday planning rules written in, to give automatic notifications to managers when an employee has worked in a company for 10 years or to implement salary changes according to Collective Agreement, to name a few.

With this, information is easier to find and manage. The processes are a lot faster to handle, more transparent to employees and managers, and there is less human error.

There is also a lot of data, that was not possible to gather before. Or that was at least very hard to gather. Let’s say an HR would work in a company of 500 employees, and would want to know if people feel like their workload is manageable. Before, it was simply too much work to ask all the employees the same question individually and to gather all the data together to be analysed. Now, it is possible to send a survey to everyone in the company through email, and the survey program could combine all the results together: 53/300 felt like their work load is too heavy. And again, less human error. If an HR goes through 500 pieces of paper and marks all the results to another piece of paper to combine, I’d say there’s a pretty big chance that they mark or count something wrong.

Having digital, big data makes it so much easier to see the larger picture on any topic. As Kenneth Cukier argues in his Ted Talk: “Big data is an extremely important tool by which society is going to advance.” (Cukier 2014.) With just a few clicks it’s easy to figure how many employees have been in the company +10 years, who has their 50th birthday this year or whose work safety lisence expires next month. With this, it has become easier to focus on different topics than before: strategy, well-being, employee experience, culture or training for example, that can be used to advance our skills and the way we work in a way that was not possible when we were not able to use big data.

The future of digital solutions in HR

As mentioned earlier, not all companies implement these systems that make HR management easier and more effective. Why? From what I have learned, what is slowing other companies down is the pricing. A lot of these systems are expensive. It might cost thousands or even tens of thousands of euros per year. Not all companies see the system worth the money. Is there a point of spending 5000€ a year to a recruitment system if you have 10 employees and approximately 1 recruitment a year? Maybe not. For smaller companies it is also possible to manage data differently. If you need to ask only 10 employees how their workload is, it is very simple to gather the information and combine it. If you have 10 folders, one for each employee, it manageable. But digitalising HR is a huge benefit for bigger companies.

A couple of months ago I was in this event where the topic of possibilities of AI to HR were discussed. I was mind-blown. One company presented an AI based tool which they had created, which was already put into use in some other companies. It would combine everything together and automatise so many processes. The current HR systems on the market don’t answer to all requirements companies have: some systems have a better recruitment tool but might not have absence tool in it; some systems might fit a global company, but cannot take local practices into account. Almost all bigger companies need to have other systems that complement each other. And the AI tool I meantioned before was something, that made all the systems work together. You could teach the AI that if this thing happens, this needs to happen. If an employee reports that there is a mistake in their payslip, AI checks the information automatically from the payslip and compares it to a time tracking system and HR system which contains the salary information, and then either sends a message to employee replying that there is no mistake (with explanation) or sends a ticket directly to payroll that includes information like what is the mistake and what is the correct amount. Or for example If an employee feels like they would need a training on a specific topic, AI could map out the need with the employee, explore options and send a ready requisition to HR or manager to be approved or rejected. Anything would be possible.

I believe AI will revolutionise HR completely in the upcoming years. There will be no more admin work or updating information manually. I truly believe that the future in HR is focused on overseeing AI manage the tasks and making sure there is no error, and focusing on human things like strategy, human contact, the culture and noticing silent messages. In the future HR data is not reactive, but predictive. It is possible to identify trends and recommend personalised solutions to employees that are based on data, not a feeling (“I feel like this could be the right option” vs. “I know this is one of the good options”).

AI and digital solutions offer a lot of opportunities, but there is something to keep in mind. HR needs to be ethical with data use. HR needs to follow the legislation, such as GDPR, and make sure privacy is taken into account, but also get enough insights from data to work effectively and better. As digitalisation continues, HR data will not be only something that is stored but also used for strategic decision-making through helping organisations understand their people better, while at the same time maintaining trust and privacy. It is important to find the right balance between the two – privacy and insights.

There are countless opportunities that are created by digital solutions and analysing data. But as there are opportunities, there are also risks. It is possible that someone will steal your personal data or identity or that it might be leaked out non-intentionally if people are not being careful. To answer to the question of what can and cannot be done with data, European Union put in place a General Data Protection Regulation, known as GDPR. Since 2018 all organizations in EU had to follow the legislation of how data has to be collected, stored and used (GDPR.EU, n.d.).

As you can probably interpret from the chapter above describing digitalisation in HR, there is a lot of highly sensitive data that HR’s handle daily, including social security numbers, home addresses, working history, salary information or even health related information. This is why GDPR is crucial to HR. The legislation has made all the organisations to adapt, to make sure all data is collected, stored and used properly.

Working in HR I need to be really aware of what I can do with data. The positive side of GDPR in my field is that it builds trust between the employee and the employer. Everyone is aware what is happening with the data that is collected with their concent, and all actions are transparent. The negative side is that GDPR also increases administrative, manual work quite a bit, as all data related processes must be documented and have a strong reason behind it. I always need to check twice if I am working in a correct way with data. Can I store it here, is this secure enough? How do I send this forward, am I certain no one else can read this information? What data can I collect, where do I need it for? For example when we were implementing a global HR system in a company I have worked for, it was clear that not all information can be collected, because it doesn’t have a strong justification. Even if some other country might require the information of marital status (due to insurance policies), we in Finland have no reason to collect that information. What would the strong reason behind it be? There is none. Therefore, we cannot collect it as an employer.

GDPR is very important to me. I feel like a lot of people haven’t even heard of it before, and are really annoyed by the cookies question on every website they enter, all the “I Accept the Terms and Conditions” on every application they send or the account they create. But they are here to protect us. They are here to make sure no one takes our information without our concent and a real reason.

Personally, I use AI tools every day. Our company has a Microsoft Copilot lisence, meaning it is safe to use with our company matters. In addition, I use ChatGPT more in my daily personal life, since I find it a little bit better than Copilot.

For the purpose of testing how accurate AI tools are in HR, I asked both ChatGPT (attachment 1) and Copilot (attachment 2) the following complex question, that at one point I myself on my career had to research:

“According to Finnish legislation, if an employee has reached the lowest retirement age and would want to work a shorter week, what options are there? Please also describe which is the best option from employee’s and employer’s perspectives.”

ChatGPT referred to different pages like Finlex, Keva and Työeläke.fi, and made a list of four options that are legally possible, what legal limitations there are, gave a list of best options from employee’s and employer’s perspective, wrote practical next steps and wrote a summary of everything. By first look the answers seem very professional, but I immeadiately notice something that could be misleading. ChatGPT offers a legal option of partial old-age pension, and in the bullet point below, that describes the option more in detail, says: “If eligible – -“. There is no definition what it means to be eligible. From this list someone who doesn’t know the legislation that well, might feel like that is an option. Someone, who is familiar with the legislation, on the other hand, immediately knows that the person is not eligible, since the employee has already passed the lowest retirement age. This option is possible only if the person has not reached the lowest retirement age. ChatGPT is not completely in the wrong, since that is one of the options for retirement, but for the context which I set in the question (employee has reached the lowest retirement age), the answer is not correct.

Copilot refers to pages like Työeläke, Varma, Etk, Elo and Työsuojelu, and made a list of two different options that are legally possible, evaluation of best option from employee’s and employer’s perspective and gives a recommendation on the solution. Copilot offers the same option as ChatGPT of partial old-age pension, which the employee in the question is not eligible for, leaving one option. The description of that one answer seems accurate, but the list of different options is missing other options.

Comparing the two, ChatGPT generated a much longer answer offering more options than Copilot. Neither were 100 % accurate, giving options that the employee in the question is not eligible for. ChatGPT also used Finlex as a reference, which is the official database for Finnish legislation. All in all, I think ChatGPT’s answer was better.

What can be taken from the test I did, is that you cannot trust AI with everything. It is possible to get a wrong answer, or at least misinterpret it. Even despite of knowing that AI is not correct in everything, I use it to help me get more information on different topics. What I have found to be very important, is to challenge AI. Ask follow-up questions, e.g. “From which legislation is this information from?” or “What does it mean to be eligible for this option?” Check the references and evaluate their trustworthiness. AI gives the best answer it can with the information it has or finds, but it cannot always evaluate if the information is good or not, wrong or not.

There are so many opportunities with AI. It can help us if we don’t know where to start, or when we get stuck with a task. It can create new things, process a lot of data in seconds, even handle manual work automatically. AI is the future, but we need to stay critical towards what it gives us and not forget to use what makes us human – our brain.

Before this course, I don’t think I have ever stopped to think about how big of an impact digitalisation, data management and AI have on HR. Things have fundamentally changed during the past couple of decades. New opportunities have risen. New risks have risen with the opportunities. And so have the restrictions in a form of GDPR. The work has changed, and will also do so in the future.

What I really enjoyed in this first assignment, was hearing the Ted Talks of inventions that have been done and how the world has become, as well as reading the posts of other students on this course from completely different fields. It was eye-opening. To stop and think how much has changed, and how much is possible, in good and bad. Not only in HR, but also on other fields, such as healthcare laboratories or illustration and design.

This assignment was also a wake-up call on using AI. What it could offer, but also how it is not to be trusted blindly. In the future I want to learn more about efficiently and ethically using AI in my work. I feel like we are taking only the first steps with AI and can’t even imagine what lies before us. I wish to change with the world, to keep up. I also wish to learn how to manage data better in HR, and what we could actually use it for so that it would be meaningful and valuable to all, while being GDPR compliant at the same time. Insights and privacy.

References

Cukier, K. 2014. Big data is better data. Video. TED Talk video platform, published June 2014. Kenneth Cukier: Big data is better data | TED Talk. Referenced 17.11.2025.

GDPR.EU n.d. What is GDPR? Online publication. https://gdpr.eu/what-is-gdpr/. Referenced 13.11.2025.

Blogs I commented:

hetak – My blog site for Digitalised Working Environment course

inga´s site