Artificial intelligence (AI) and machine learning (ML) use cases continue to expand daily, bringing change to virtually every industry. Across most industries and in functions such as customer care, finance, and HR, AI and ML solutions are being deployed to augment decision-making capabilities and increase levels of automation to optimise the power of human-machine teaming.
But the technology comes with its potential challenges ranging from upholding user rights on privacy practices to stoking fears of displacing workers. Governments across the world including in Southeast Asia are taking steps to address AI challenges. In Singapore for example, the government launched the AI Verify Foundation earlier this year, to harness the global open-source community to develop AI testing tools for the responsible use of AI.
Balancing the risks and benefits of AI and ML can be a tricky process but when optimised in the right way, AI and ML can be leveraged for economic growth, and to foster innovation, elevate service delivery, and enhance customer experience. This is no truer than in human capital.
With people being the bedrock of any organisation, CEOs and HR leaders play a vital role in shaping the workplace of the future, leveraging the strengths of AI and ML while keeping humans at the centre. In a recent Workday survey, 65% of business leaders noted that their existing deployment of AI and ML solutions has played a key role in improving the employee experience – a driving factor towards a more productive and adaptable workforce.
Leveraging AI to gain a holistic view of workforce skills and talent
Traditionally, organisations attract, hire, and retain talent by focusing on previous and current positions, experience, and degrees. However, with the evolving nature of work and the ongoing war for talent, there is a massive shift in how organisations approach talent with a growing focus on skills.
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Organisations are quickly realising that a skills-based talent strategy is essential to bridging talent gaps and providing opportunities for career growth. Skills are quickly becoming the new currency on which talent and career development, performance management, and succession planning are based.
In fact, it has been reported that 80% of recruiting professionals in Southeast Asia are prioritising a skills-first hiring approach for their respective organisations in 2023 and 2024. In November this year, Singapore also introduced a handbook, providing best practices and guidance for employers to attract, assess, and develop tech talent based on competencies rather than academic qualifications.
The pivot to a skills-based organisation requires technology that can help automate manual processes and surface relevant skills data. For example, skills are granular and dynamic, and skill levels are often not clearly defined, which can be challenging for organisations and people leaders. Managing and maintaining worker skills across the business is time-consuming, difficult to scale, and can lead to questionable data.
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This is where embedding AI and ML into HCM solutions can help fill gaps. Having ML and deep learning technologies work with an ontology of skills and skills-related data can move the needle in identifying, mapping, and matching people to roles or gigs based on the skills they have.
At the same time, AI and ML can provide candidates with equitable job and learning recommendations based on their skills. AI and ML can also help organisations understand how the skills of their workforce relate to one another, and how these skills can evolve into other adjacent skills. This is especially critical as skills, like the business landscape, are constantly evolving and changing.
AI can improve workplace culture and employee engagement
Winning the hearts and minds of employees has in recent times evolved from being the remit of the HR department to a C-level priority. Rightly so, employees are now demanding more from their employers and are less hesitant to pursue other roles should they perceive a mismatch between their expectations and an organisation’s ability to meet those expectations. Companies will therefore need to do a lot more to keep existing employees engaged, professionally challenged, and content with their roles.
It is universally acknowledged that engaged employees are more productive and can have a positive impact on the profitability of companies. However, having an accurate and timely assessment of employee engagement levels requires far more than the archaic and periodic employee satisfaction surveys, or a gut-feel assessment emanating from a corridor conversation.
Measuring employee engagement is now as much a science as it is an art. HR leaders and line managers will need an immediate and continuous pulse check to determine employee sentiments along multiple dimensions. Engagement, diversity, and inclusion need to be tracked to obtain a complete, real-time picture of an employee’s well-being and experiences. Interactive approaches to addressing employee concerns have been found to dramatically improve engagement and foster psychological safety.
Static, periodic surveys now can be replaced with dynamic, intelligent employee listening and interaction tools that provide the insight people leaders need to take meaningful action. For example, AI and ML can empower organisations with faster, more accurate ways to gather insights from employee feedback and even help predict and pre-empt employee attrition based on their engagement scores over a period of time.
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Responsible AI is a non-negotiable to help ensure accurate insights and uphold trust
While AI is certainly reshaping the role of HR and many other functions, CxOs, including CEOs and CHROs, cannot afford to take a passive approach to driving AI adoption in their organisations. In addition to the possible challenges from implementing business-driven use cases, AI technology and security issues are often not easy to comprehend, and this results in a deceleration in adoption once the peer-driven hype cycle subsides.
For AI to be harnessed to its full potential, businesses need to strongly commit towards robust data governance as the ability of an ML model to perform tasks such as conducting predictive analytics, is largely dependent on the quality of the data collected. Poor data hygiene will only result in highly flawed and potentially damaging business and financial outcomes.
Equally important is the emphasis on trust, security, and accuracy of the outputs. A recent Workday survey revealed 43% of business leaders have concerns regarding the trustworthiness of AI and ML, with 67% of CEOs wary of potential errors as a top risk of AI and ML integration. These findings reinforce the workforce’s lack of trust in such digital solutions.
Robust data governance mechanisms need to be put in place to help ensure that an organisation’s data is secure. Additionally, businesses should also establish responsible AI programmes that include detailed documentation as well as review processes which allow for human input to be incorporated. This would minimise bias generated by the ML algorithms and improve the accuracy of the output, thereby producing results which can be trusted for business automation or decision-making.
Conclusion
Public sentiment on AI has revealed that a strong proportion of respondents believe that AI is a force for good in improving key business indicators. At the same time, the importance of a strategic, responsible, and human-in-the-centre approach to AI adoption cannot be overlooked. When deployed as intended, AI and ML can help drive productivity and enable people to be better skilled, more engaged, and significantly more productive.
Raghu Prasad is the vice president of Solution Consulting for APJ at Workday
