Feature topic
payroll records in an AI environment. AI products can be ethically developed but unethically deployed. It will be essential to form a strong relationship between company and AI provider. Jaspal Randhawa: It’s important companies understand how data is being gathered and realise that AI-based output is prone to inaccuracies and bias. With this in mind, ask yourself what is the problem you are trying to solve. For example, how often does a process or task need to be carried out? Also, AI offers consistency, therefore, will this benefit the process or task? To what extent will manual intervention still be required? After all, payroll records contain sensitive data, therefore accuracy and legal compliance is essential. When considering the output of AI-driven data, who is the recipient of this data? Do they need to be educated on how this data has been gathered and the purpose it is intended to serve? Answering these questions will ensure the appropriate solution has been selected and necessary controls established, allowing companies to create the appropriate policies and training material. Going forward, it is paramount AI data output is reviewed to ensure it’s still meeting its intended purpose, that controls are still adequate and there are no compliance issues, such as a GDPR breach. Any reputable AI solution provider will have robust policies in place backed up by a compliance team, who will work with engineering and product teams to ensure solutions are designed within a compliant and ethical framework. Once a solution has been released or updated, any supporting release notes should confirm the intended use and include a section on ethical use of data (which should have been signed off by their legal/compliance team). If the AI solution provider also offers a consultancy service for deployment of solutions and/or software updates, then consultants should receive the appropriate training. This best practice knowledge should be shared with customers through training material, which will provide more detail than release notes and would help those companies who are new to AI. Solution providers should ensure customers have the appropriate means to raise any bugs or issues detected with the AI solution. If any of these bugs highlight a compliance breach or identify
an unethical use of data then the solution provider will need to resolve this issue as swiftly as possible. For example, they may need to hard code part of their software to ensure customers cannot manipulate or unintentionally produce prohibited data outputs, although the solution providers should have taken rigorous quality checks prior to releasing software. There is an argument that suggests all payroll professionals should learn how to calculate a payslip manually in order to understand the fundamentals of the calculations. I firmly believe this is essential Solution providers could help further by offering health checks to customers; this service would ensure the AI solution is still fit for purpose and the value of the product maximised. We’re living in a world where technology is the driver to providing instant outcomes, and where AI is applied correctly, it will result in a better experience for employees as well as company decision makers. Good payroll professionals are often developed through learning the operational aspects first. Is there a risk that too much automation can leave knowledge gaps? AB: For the intelligence to run smoothly, a payroll professional needs to train it. For instance, stopping actions and preventing the system making wrong decisions that lead to costly errors. So, payrollers still need a comprehensive understanding of the operational ‘basics.’ However, thanks to increased automation from AI, they’ll also
have more time to carry out the strategic elements of payroll. PG: This is something I have discussed with fellow payroll professionals many times. Those of us who have lots of knowledge and experience are now decreasing in the workplace. There is not perhaps the same desire to learn the nuts and bolts of things. Research has shown younger generations have a different way of learning to older generations. This does not just apply to payroll, of course. However, as part of this generational change, the payroll industry does need to maintain knowledge, including how to calculate payroll manually, and how to prioritise calculations. When I recruit to my team, I always test that they can do manual calculations. My team need to be able to do this so we can then tell engineers how to program the software. If this knowledge is lost, then the quality of software produced will decrease. Automation should help payroll professionals, but it should not be left to totally replace knowledge and experience of qualified payroll professionals. SH: There is an argument that suggests all payroll professionals should learn how to calculate a payslip manually in order to understand the fundamentals of the calculations. I firmly believe this is essential. I recently took my car to a garage. When pulling away from a standing start sometimes there would be little power and no matter how much I stepped on the ‘gas’, it had no effect. Three mechanics examined the car, but were lost for an explanation – that is, until an elderly fitter walked across and within minutes found the problem and put things right. How did he know? The difference was he had worked on car engines long before they became electronic masterpieces. There was a knowledge gap in those first three mechanics. Dawn Jackson: Generally, I have found that most integration and automation is not designed to replace payroll professionals’ knowledge, but to be used alongside them and their extensive knowledge, helping to simplify processes, save time and mitigate the need for the re-keying of data from one system to another. When I first started in payroll, it was a very manual paper-based process, but a payroll product these days is expected to receive data from a variety of places,
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| Professional in Payroll, Pensions and Reward |
Issue 77 | February 2022
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