How GenAI is shaping the future of the flex industry

The Netherlands in the lead with AI use by companies
The global rise of artificial intelligence (AI) is undeniable. 13% of companies with at least 10 employees in the Netherlands utilised at least one form of AI technology last year, according to figures from the European Commission. This is higher than the wider EU average of 8% of companies. The EU has set a target of at least 75% of companies using at least one form of AI technology by 2030. In this article, we examine the impact of generative AI (GenAI) on the revenue model of flex companies, consisting of temporary employment agencies, recruitment agencies and secondments.

GenAI differs from AI in that it can create new results and content based on existing data. GenAI models are not programmed by hand, but trained using historical data. Research shows that there is great potential for GenAI, especially among companies that are already largely digitised. Within business services, this mainly concerns specialised service providers such as lawyers, accountants and consultants. Even in the flex industry, a significant part of the work can eventually be carried out by AI technology. However, despite a structurally tight labor market, AI is not yet a strategic priority for most recruiters in the flex industry worldwide. First, the necessary steps have to be taken in the field of digitisation.

AI technology takes over part of the work
The core activities of flex companies include job placement, recruitment and selection, the administrative relief of companies (such as time and wage administration) and the provision of HR advice to companies and employees. In recent years, a lot has already been automated in the administrative field by flex companies. Particularly in the field of administrative processes and HR services, such as mediation and recruitment and selection, GenAI can already take over a lot of work from those acting as intermediaries.

GenAI can job match faster and at a higher quality
In the flex industry, GenAI is now being used to write vacancy texts and job profiles and to match candidates with vacancies or assignments. Based on a CV or a job profile, GenAI can find suitable vacancies, assignments or suitable candidates. AI technology can do that much faster than a recruiter because it can analyse large amounts of data in a short period of time. As a result, the matches are often of better quality than those of an intermediary.

Human touch remains necessary for the time being
While GenAI is able to create matches more efficiently, the final match is often still carried out by an intermediary. This is because current GenAI models are not always able to execute this part flawlessly. All too often, the same actions are carried out but produce different results. There's also a lack of emotion to consider – AI cannot draw assessments as to whether an applicant would make a good fit within a team or the wider company culture. However, as the technology develops, GenAI's matching skills will continue to improve in this area as well.

Enriching the database
With the help of GenAI, the database is getting richer and is increasingly filled with data. When a CV comes in, GenAI can – for instance, via Whatsapp or a chatbot – fill in missing basic data, such as 'do you have a driving license?', 'what is your maximum travel time per day?', 'when are you available?' etc. This can be done almost 24/7, ensures a well-filled database and saves workers a lot of time.

More data-driven working
GenAI not only ensures that work can be carried out more intelligently and efficiently, but it also offers opportunities for further growth. As more high-quality data becomes available, the work will in turn become more data-driven. The next step in the use of GenAI is that it can predict market developments based on data analysis, e.g., answering questions such as 'what were the most successful placements last month?', 'what skills does a company need?', 'what might the staffing needs of a company look like next month?'. This information provides flex companies with new strategic insights for further growth. This allows them to proactively approach companies to fill in during peak periods and attract or train candidates for certain skills that are in high demand.

Higher added value
The use of GenAI primarily leads to time savings in the flex industry. Simple administrative tasks, such as writing job descriptions and completing missing data, can now be done by GenAI. This makes jobs more attractive to recruiters as it provides more room for work with higher added value.

For example, there is now more demand from companies for HR-related services. Career guidance, training and retraining of candidates and other employees is also becoming increasingly important, as work in many sectors is changing or being taken over by AI. For this, flex companies can ask for higher rates, which ultimately leads to better profit margins. Strong competition has put them under pressure for a long time, particularly among recruitment agencies.

Improved labour productivity
In the long run, the use of GenAI leads to more efficient work processes and higher labour productivity. Despite the automation of administrative operations in recent years, some of the work in the staffing industry, such as matching, remains labour-intensive. Contrary to the general trend, labour productivity in the temporary employment sector has therefore declined over the past 20 years. This means that despite the same number of employees, less is produced today than 20 years ago.

Meanwhile, labour productivity has actually increased in the business services sector as a whole. Other sectors in business services, such as accountants and consultants, are generally further advanced in the field of digitisation. By investing more in new technologies such as AI and data analysis, work can also be carried out more efficiently in the flex sector and, in turn, labour productivity can increase.

More revenue through faster and higher quality matches
GenAI's matching skills also ultimately leads to more sales. After all, the average time-to-hire goes down, allowing more matches – and therefore more sales – to be made. In addition, GenAI can also be used to better identify unused workforce potential. This can be done, for example, by reactivating dormant candidates in the database. GenAI can approach these candidates to ask whether they are available for work.

In a growing economy, which increases the demand for temporary workers, AI technology also can quickly place more candidates. This in turn leads to more revenue. An important condition for this is that business processes are adapted to be able to carry out the additional work that GenAI generates.

Data quality

The biggest challenge in using GenAI technology is data reliability. To achieve good results with GenAI, the quality of the data must be sound. If the system is not properly powered, it will work less efficiently. Moreover, the more data that is available, the better GenAI works. Flex companies that are already well advanced in the field of digitisation have a head start and will be able to improve their productivity levels faster.

Discrimination and hallucinations
Because GenAI uses historical data, the risk of discrimination and prejudice is high. For example, according to GenAI, a nurse or teacher will most often be a woman, simply because in the past these professions were more often taken up by women than by men. Hallucinations are also a possibility. This is where GenAI provides false or misleading information, which is then convincingly presented as truth. In order to prevent discrimination and/or hallucinations, it is therefore crucial to regularly monitor the results and adjust them where necessary – and this should be deemed a priority. As AI technology gradually improves, control and adjustment will liekly become less necessary than is currently the case.

Privacy and ethics
A third risk in the use of GenAI is the leaking of confidential and/or privacy-sensitive information. This certainly applies to sectors where a lot of personal data is available, such as in the flex industry. This allows GenAI to access sensitive information that falls under the General Data Protection Regulation (GDPR) privacy legislation. It is therefore important that companies have clear guidelines that comply with the legislation, protect sensitive data properly and are transparent about how they work with GenAI technology. The European AI Regulation also came into force on 1 August 2024 to protect consumers. It contains rules for the safe, reliable and efficient use of AI by governments and companies within the European Union.

GenAI largely takes over the intermediary role of flex companies...
The impact of GenAI on the revenue model of the flex industry is still relatively limited in the short term (zero to two years). GenAI technology is now mainly a useful and productive assistant that makes the work of recruiters more fun and interesting, but cannot yet completely replace them. However, as the technology evolves, AI’s functionalities are set to grow.

For the longer term, the impact of GenAI is more far-reaching for the flex industry. While it's difficult to look too far ahead given the rapid technological developments, GenAI is eventually expected to largely take over the intermediary role of many traditional staffing agencies. Recruitment, selection and the placement of candidates will become much more data-driven than is currently the case, and over time, the human dimension will become less important in this area.

…Leading to a two-tiered temporary employment market
In the future, it is expected that there will be a division in the flex market between companies that completely transform into platforms (specialised or not), and those that specialise in services with higher added value, where the human touch remains necessary for making a substantial difference. For the larger players in the market, a combination of both is possible. Flex companies that use GenAI in a timely and successful manner will have a competitive advantage because they work more efficiently and productively – and in turn, become more future-proof.