AI has not only significantly optimized cash flow at the company, data and client requests are also delivered far more efficiently than before, resulting in faster turnarounds.
When Anand V, CIO of Randstad APAC, joined the company, he was given a huge job to change back-end IT systems. The digital journey, which started with the transformation of the payroll, is still on. In a free-wheeling conversation with FE CIO, Anand throws light on how GenAI is affecting the hiring and onboarding, and making it easier for the finance and IT departments to work." Below are the edited excerpts.
When you joined Randstad India a few years back, what kind of IT transformation programme were you asked to undertake ?
When I was appointed Chief Information Officer (CIO) for the Asia Pacific (APAC) region at Randstad, with an additional portfolio for India, we conducted a portfolio analysis of the IT systems in the APAC region. We executed the complete transformation programme of our biggest application -- the payroll systems.
Historically, the various applications in the APAC region within Randstad were shared with each other. The payroll application in India was, thus, using the same code base as the other countries in the region. To accomplish the payroll application renewal, Randstad India collaborated with a cloud-OEM.
We not only explored the technology part of the application but also checked the payroll process from different vantage points. While a system level study was done, along with the payroll process team, we also closely looked at the payroll process and cleaned up some elements there, thus looking at the application from multiple angles, while not just restricting ourselves to the technology aspect.
As a consequence, we were able to make the payroll process much more effective and time-efficient. This was made possible by the parallel compute and other capabilities of the cloud native infrastructure atop which the application has been hosted. After having completed the half-way journey, we plan to cover the entire ambit of the application by 2025-end. We have completed around 50 percent of our transformation, yet still achieving most of our objectives.
How have you leveraged GenAI in the onboarding side of the hiring process?
One main aspect of the ‘hiring’ process is that it involves ‘hiring’ for our clients also. The payroll part of the transformation aside, ‘onboarding’ is another area where we have done extensive work on the hiring side. After the introduction of GenAI in the form of e-onboarding by Randstad India’s ELM (Employee Lifecycle Management) team, we have become capable of carrying out the entire verification process along with ensuring that the most appropriate talent is allocated to the most relevant customer by means of GenAI. The process of placements has now become much faster as a result of the speeding up of the whole process, and thus we are able to make the placements much faster to the delight of both our customers and talents.
In addition, the tool GenAI is used by approximately 40,000 Randstad employees all over the world for their daily jobs.
Apart from hiring, which are the other business functions that are using the generative AI tool the most as of now?
The GenAI tool, apart from the hiring process, is majorly being used by the finance and internal IT teams. Our finance team is using Agentic AI to reduce the Days Sales Outstanding (DSO), a financial metric - an indicator of the days taken to collect the payments after the sales is done. In an organisation which operates at the scale that we do, if done manually, the payment collection process would generally take longer, thus impacting our revenue figures. After Agentic AI adoption, the heavy lifting -- collection, validation and formatting of documents is done by the GenAI agents, thus reducing the DSO significantly, resulting in improved cash flow.
The internal IT teams at Randstad India are also extensively using GenAI, which has helped them to fasttrack the development of IT applications. In fact, the IT development lifecycle time - from requirement gathering to business use case delivery has been reduced by 30 percent-40 percent.
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