Among GCC businesses, one of the key challenges is to recruit the right talent to navigate the choppy waters between strategy and a sustainable future. The HR department has become more integral to this journey than ever.

In today’s economies, when we talk about overcoming real-world challenges, we inevitably discuss the digital tools we must procure to ensure success.

Artificial intelligence — particularly its flashy, agile variant, generative AI — can be enormously helpful to recruiters. Of course, it can do what it does for other business functions — automate the mundane to free up talent for innovation. But it can go way beyond this, fulfilling the role of tester, investigator, and judge. Then after onboarding, AI can play a pivotal role in employee retention. 

So, let’s take a dive into how this works, starting with AI-tuning for the recruitment process.

AI can be of use even before HR starts trawling through resumes. Using demographic and other data from broadcast and print-media organisations, AI models can be built to discern not only the optimum place to advertise a given role but the ideal messaging for attracting the best candidates.

Better communication between employer and candidate from the outset makes for a smoother process. Generative AI can be used to build rapport with potential employees before their ideal job even becomes available.

Leveraging AI tools for efficient interview scheduling and assessments

When the recruitment cycle gathers steam, chatbots and other tools can act as schedulers, negotiating interview slots with candidates and generating reminders for all parties. Other systems can create interview questions that fit job descriptions, and advanced natural-language models can make judgements about candidate responses.

Using these approaches means the HR department’s technology suite can contact ideal candidates through top recruitment sites as soon as a job vacancy goes live. And AI can get involved in assessing the personality and skills of those candidates, setting tests for them but also drawing on historical data about them. The referee system has always been time-consuming and error-prone. And independent third-party investigators are costly. AI tools can uncover the hidden truths behind multiple CVs in a matter of minutes. 

Once the AI-equipped HR team has onboarded the ideal candidate, the next challenge comes. Retention. Assuming you got the right person for the job, how do you keep them happy? Or more precisely, how do you monitor and understand the relevant data points that allow you to gauge the level of satisfaction of any one employee?

Machine learning and predictive analytics are adept at keeping an ear to the ground on employees’ morale and their opinion of the workplace and the brand. Tools exist to measure engagement and detect dissatisfaction. Data on vacation and sick days, on progress reviews, and on team-leaders’ performance, can all be used to identify every mindset between boredom and burnout. With these insights come opportunities to shake things up before attrition becomes widespread.

Mitigating AI risks: Privacy, bias, and responsible data use

AI is a powerful tool but as with all powerful tools, it needs to be handled with care. Everything is at stake, from worker safety to the opinion they hold of their employer. AI in general is tied to many concerns about data safety and whether personal information is being used responsibly.

If AI assesses competing CVs against job descriptions, that is one thing, but if it delves into other public data about a candidate, privacy concerns may arise. Employers eager to build a brand that is a magnet for quality talent would be well advised not to wait for legislation on these issues but to act as privacy advocates now.

HR departments should be leading the charge on such public positions, including calling for care to be taken to avoid AI bias, both in recruitment and in professional development. Remember that AI models reflect the data on which they were trained. If that data shows an inordinate favoring of, say, men for engineering jobs, then the AI model will behave accordingly unless responsible humans intervene.

To arrive at an AI-infused environment that leaves nobody behind, where everyone uses AI with enthusiasm and trust — what one might call an Everyday AI organisation — takes commitment and vision. Education is important but it must be combined with transparency.

Trust in AI can never be realised if the technology still suffers from the black-box problem where results arrive fully formed with no supporting information about how the system arrived at the answer. Meanwhile, one solution to the education challenge is to take the learn-by-doing approach and allow disparate teams to explore AI in the context of their business function. HR professionals could either work on a project-by-project basis with data experts and IT teams or they could create dedicated IT-specialist roles within HR.

Creating an everyday AI culture in the workplace

And to hold this new Everyday AI culture together, a robust governance framework must be established. This will ensure that the scaling up and operationalisation of ML and AI solutions can occur sensibly. HR has a significant role to play in the establishment of such policies and it should insist that any procured technology is flexible enough to allow the enterprise to pivot when necessary to accommodate talent pools and employees.

Everyday AI is the foundation of agility and innovation for all present-day business functions. Organisations across the region are recognising how generative AI and other tools can make life easier for HR staff and make talent recruitment outcomes more favorable for the hiring department.

Starting with the empowerment of HR staff by teaching them the basics of AI, organisations can quickly move to ensure that all stakeholders are versed in issues of interpretability, data privacy, and ethics. As time goes on, the digital natives that swim in the talent pool will be attracted to employers that have used AI to make recruitment and professional development fairer.