The AI revolution is dramatically changing how organisations operate and, as it upends traditional views of work, talent and skills, there is a growing sense of urgency among HR and business leaders, to embrace the technology and proactively adapt to the profound changes that are ahead. Indeed, according to Gartner, 76 percent of HR leaders believe that if their company does not implement AI solutions in the next few years, they will be lagging in organisational success, compared to those that do.
Many companies are pouring substantial resources into AI, without laying the groundwork for successful adoption and implementation, resulting in critical oversights in policy and workforce strategy. As pressures mount, the fact that precious funds and resources are not being well appropriated is a significant cause for alarm, as it is crucial to successful and sustainable AI adoption and adaption. While this rushed approach may yield immediate benefits, critical oversights in AI policy development and workforce strategy pose significant risks to the integrity of AI initiatives in the long-term. To successfully navigate the complexities of AI adoption and integration and harness the full potential of the technology in the workplace, leaders must rectify AI investment gaps and address AI policy and workforce strategy deficits. It is those that prioritise strategic planning over speed who will be better positioned to achieve lasting AI success.
Make certain there’s a ‘why’ guiding the AI strategy: AI has become a competitive differentiator in today’s rapidly evolving business and talent landscape. Consequently, many companies are hastily adopting the technology merely because it’s expected, without proper direction or clear intention behind the investment. According to Cisco, 97 percent of companies say the urgency to deploy AI has increased, though only 14 percent are fully ready to integrate the technology into their business. This rushed approach to AI adoption and integration will only lead to wasted resources and failed AI outcomes. Successful AI strategies are thoughtful and deliberate and must always trace back to the fundamental question, “why do we need AI?” Whether the goal is to improve hiring efficiency, enhance business decision-making or create more personalised onboarding experiences, AI objectives should be established from the start and closely align with the company’s overall mission and strategy. It is also important that HR and business leaders do not assume AI is a silver bullet for difficult problems. AI is a tool, not a solution itself. The purpose of AI’s integration into the workplace should be to augment, not replace, humans. After all, all the things that make us human, like problem-solving and creativity, are key to enhancing how companies work with and derive value from AI.
Build a robust AI policy with governance frameworks: This step serves as the cornerstone for the responsible and ethical use of AI, although it seems many companies have neglected this critical piece of the AI puzzle in the race towards deployment. According to McKinsey, only 21 percent of organisations reporting AI adoption have established policies governing employee use of AI technologies at work. Additionally, despite the potential risks of AI adoption, few organisations report having safeguards to mitigate AI inaccuracies (32 percent) or cybersecurity threats (38 percent). It is imperative to rectify this oversight and HR leaders must collaborate with C-suite leaders – including the CTO and CIO – to establish a robust AI policy and governance framework that provides clear guidelines for the design, development and deployment of AI. These guidelines should focus on data collection and data privacy and set explicit parameters on mitigating inaccuracies, biases and discrimination in AI systems, through continuous monitoring, AI auditing and feedback loops. AI policies should be comprehensive, addressing various facets of AI implementation, while also remaining flexible to the dynamic nature of AI and evolving ethical, legal and social considerations surrounding its use. Looking beyond the confines of the organisation to measure AI’s impact and identify policy pitfalls is equally essential. By engaging with stakeholders and leveraging third-party expertise – like academics and advocacy groups – leaders can strengthen governance frameworks and reinforce their commitment to responsible and ethical AI practices.
Solicit employee input in early-stage AI decision-making: Part of effective AI policy development and governance also requires HR leaders to transparently communicate and collaborate with their broader ecosystem. By actively involving workers in discussions about the implementation of AI, employers can not only gain valuable insights into potential concerns and preferences, but significantly enhance employee morale and trust. Pew research reveals that workers who are most exposed to AI are more optimistic about the technology and its impact on their jobs. Despite the clear benefits, many companies have yet to prioritise soliciting employee input on AI, representing a missed opportunity to harness the collective intelligence of their workforce and build a transparent environment for the technology’s integration. Asana data shows less than one-third (32 percent) of employees feel their company has been transparent about AI use and only 44 percent of executives feel their company has exhibited AI transparency. Address AI driven knowledge & skills gaps: Given the widespread proliferation of AI, many leaders may assume their employees are already experimenting with AI and therefore, they are well-prepared for the technology’s integration at work. However, research indicates that is not always the case. Nearly 40 percent of workers are still apprehensive about AI’s use in the workplace, indicating that fears surrounding the technology persist despite its growing prominence. Leaders must take a people-centric approach to AI and harness the unique contributions of both machines and humans. To foster employees’ comfort and knowledge of AI, leaders should facilitate hands-on training programmes and workshops tailored to different skill levels. Providing practical experience with AI will help to demystify the technology and empower employees to utilise it correctly. By demonstrating a commitment to AI education, organisations can not only alleviate workers’ fears of job displacement and nurture human capital for long-term growth, but also ensure they can attract and retain employees equipped with the necessary skills to navigate and thrive in an AI-driven future.
As AI becomes increasingly prominent in the workplace, skills requirements have also evolved dramatically and it’s become harder than ever to retain talent. Indeed, data revealed that 51 percent of people are considering looking for a new job in 2024. As such, concerns are growing among HR and business leaders that their workforce may be ill-equipped to compete in tomorrow’s workplace. Research from Workplace Intelligence and edX, indicates that 87 percent of C-suite leaders are struggling to find talent with AI skills. As the race for skilled AI talent stiffens and HR leaders work tirelessly to prepare their companies for an AI-driven future. Keep one thing top of mind, there must always be a balance between technology and humanity. While AI builders – such as software developers and data scientists – are instrumental for AI innovation, a workforce comprised of technical workers alone won’t cut it. Companies must also employ workers with soft skills, who can ensure the human element remains at the forefront of AI initiatives. AI success demands strategy, not speed: In the past year, many companies have hastily ventured into AI deployment to remain competitive in today’s ever evolving business and talent landscape. While the payoff of this rushed approach to AI integration may seem plentiful now, organisations must recognise the risks and challenges that stem from AI investment gaps and strategy deficits. HR and business leaders must take proactive measures to bolster AI policy and governance frameworks and bridge workforce gaps to maximise opportunities for AI success in the future.
