How Artificial Intelligence is Reinventing Human Resources Management
Forbes reports that artificial intelligence (AI) is expected to replace 16% of HR jobs within the next 10 years. While the impacts of AI in HRM- and recruiting-related roles are in many ways still unclear, we don’t believe that today’s HR professionals will necessarily find themselves without a position 10 years hence. In fact, we believe that AI in HRM and automation will help many shift time from administrative work and other low-value tasks to evaluative and creative work that will prove to be more meaningful and impactful. Artificial intelligence in HRM will improve performance on a wide range of the types of work that makes HR such a rewarding, and important, part of any organization, and in so doing, will drive cost and performance benefits that will help companies to be more successful in attracting, hiring, developing and retaining the talent they need.
AI in HRM will change work and stimulate HR innovation in many ways, but, broadly speaking, most of the changes to come will increase efficiency in terms of both time and resource usage, as well as improving managerial and corporate decision-making through more wide-spread and sophisticated usage of analytical methods and tools.
AI in HR will improve efficiency in manual tasks
Manual administrative tasks – coordination activities, scheduling calendar invitations, data entry, rote communications – are a source of significant wasted time for many professionals, but especially in HR, with the process focus and attention to detail required in areas like compliance, hiring, benefits, and payroll. Many such tasks require no judgment, simply time and energy to complete the task. As software and artificial intelligence in HR systems become more sophisticated and better integrated with legacy systems and with one another, humans will be able to spend more of their time on subjective tasks. Employees who today must spend a portion of every day calendaring meetings or transferring data from one system to another will spend more time on analysis, interpreting data collected, identifying personnel-related insights, and bringing additional context to help managers and employees in their businesses make better, faster decisions. In many organizations, analytical tools and methods that previously would have been impossible or impractical because of the complexity, scale and coordination of the analysis required will become standard practice as HR professionals seek to efficiently hire the most suitable candidates for every position; onboard and train employees effectively; and encourage working cultures that will result in high employee retention.
[Read More: How to reduce your time-to-hire by 50%]
AI in HR will increased and improved ssage of analytical methods and tools
Beyond the reduction in time spent on manual tasks and the corresponding redistribution of working time toward higher-value activities, AI in HRM can also help to improve human performance at complex tasks like evaluating interviewee suitability for a role or helping surface the strongest, most engaged candidates in the hiring process. In such work, human judgment is difficult to replicate fully in computer systems, but consistent, automated data collection and analysis can surface comparative insights and quantified expressions of factors that humans would normally attempt to evaluate qualitatively or intuitively. In constantly updating success metrics like hiring rate, employee retention and post-hire performance data; and in assessing predicted outcomes against independent data (e.g. do candidates respond promptly, do they show up for their interviews, spell correctly, use punctuation, etc), AI in HR can improve human decision-making by helping to sift and surface the candidates for human reviewers to assess, among the many hundreds of applicants a job may receive.
The challenges of AI and HRM
Despite its promise, the introduction and usage of automation and AI in HR processes poses many challenges to companies and the staff who would benefit from them.
Practical challenges of artificial intelligence in HRM
Even with the best intentions, strong organizational alignment and support, and the most seemingly simple applications of a new technology, modifying business processes in disciplines like HR to incorporate new technologies can be complicated and difficult. Employees must be retrained in how to do core elements of their job, use new tools, and make use of additional data and insights that are newly available to them. In some cases this is quick and straightforward, but in others the changes that are enables are quite substantial and may result in some HR jobs changing into unrecognizable versions of their former selves (e.g. recruiters who can process thousands of applicants every month instead of a few dozen will work very differently and rely on a very different set of tools; the skills needed of such a person may even be so different that you would hire someone different to do it in the new environment, or existing staff may struggle and need extensive training).
[Read More: Automation doesn’t replace recruiters – it makes them better]
Ethics and Legality of AI in HR
Ethically and legally, it’s essential to avoid algorithmic biases for AI in HR that mirror human biases, to avoid improper uses of data that may be gathered from candidates or employees for legitimate reasons, and to comply with all the relevant privacy and data security laws and regulations. This is made more complicated as new regulations are promulgated and in areas where government action and guidance have not yet been forthcoming to help the private sector take the right steps. In hiring and in HR it is especially important to subject every business process change and new set of technology tools to a level of scrutiny that includes assessments on whether its usage is both legally compliant and ethically above reproach.
Technological challenges of AI in HRM
Technologically, the challenges of artificial intelligence in HRM are simply around the time and investment required to capture the potential that AI and automation present; usage of the term “AI” generates a lot of buzz, but to actually incorporate it at a meaningful level into a company’s business practices takes significant investment. Most companies today do not have the in-house staff, expertise or R&D budgets to invest in all the ways that AI in HR might benefit their business, so it behooves them to think critically about where their business might benefit the most and, in each case, whether a “build” or a “buy” approach is the most appropriate.
Best practices for making AI in HR work well for you
For those just beginning to think about how automation may play a role in improving your business, or still wondering how AI can benefit your HR department, It’s important to focus on very specific business problems and follow a hypothesis-driven approach within a framework that clearly weighs business costs and benefits across all the relevant categories.
If the benefit of AI in HR to a given company is the reduction of one HR staffer’s time spent on a certain task, it is almost certainly not worth the expense and effort to hire and use highly-trained and more expensive engineers to solve the problem. Better to use an off-the-shelf tool that a third-party company has developed for a more generalized purpose.
By the same token, however, in a company that takes a too-simplistic approach to making a hiring team incrementally more efficient, there may be significantly larger improvements in productivity available if the right approach is taken. For instance, rather than simply automating interview scheduling, it may be possible to rely on pre-recorded video interviews and document collection that will remove entire steps in the process and make things easier, quicker, and more objective for both the candidates and the hiring organization.
For companies willing to invest the time, organizational energy, and, of course, funds, automation can improve performance and employee engagement in HR and hiring roles. But it behooves them to make sure that the solution fits the problem, and to thoroughly explore some of the issues discussed here, before taking the plunge into this new, promising space.
