- Obstacles to harnessing the power of (generative) AI
- Can socially-skilled algorithms teach human-machine collaboration?
- Are (almost fully) automated customer support departments the future?
The intersection of artificial intelligence and human resource management (HRM) is redefining the future of work in unprecedented ways. As AI continues to evolve, it promises to transform HRM from a primarily administrative function into a powerful tool for strategic decision-making, talent optimisation, and employee engagement. In a world where the agility of a company often defines its success or failure, the integration of AI into HRM processes is no longer a matter of choice but a necessity. As businesses strive for competitive advantages, the automation and data analysis capabilities of AI offer unparalleled opportunities to streamline operations, make more informed decisions, and create tailored employee and client experiences. But what about the human factor? How will employees adapt to an increasingly automated workplace? Will AI technologies replace human roles in HR, or can they co-exist in a symbiotic relationship that enhances the efficacy and reach of human decision-makers? And how do we manage the technical skills challenges when it comes to working with — and leveraging — this new technology?
Here’s some numbers: As corporate leaders start to realise the vast potential of AI, its adoption is gaining momentum within businesses — albeit cautiously. A 2023 IBM report, which polled 3,000 executives on generative AI, found that 43 per cent already rely on AI for key decision-making, while 75 per cent believe the technology could give their company a competitive advantage. However, there’s still a hesitation among the workforce: 48 per cent of those surveyed cited concerns over bias and 57 per cent expressed worries about data security. In this article we will delve into this intriguing confluence of AI and HRM, explore some of the ways in which AI is currently being used, take a look at the evolving landscape of AI in HRM, and what it means for the future of work.
Obstacles to harnessing the power of (generative) AI
Despite the transformative potential of generative AI in streamlining operations, enhancing productivity, and even generating new business models, employees often struggle to harness its full power in the workplace. The reasons for this challenge are manifold. On one hand, there is the issue of a steep learning curve, as most employees are not trained data scientists or machine learning experts. On the other hand, the integration of AI into existing workflows often requires a culture shift that many organisations are unprepared for. Additionally, ethical considerations around data usage and algorithmic decision-making can create hesitancy. The lack of intuitive interfaces and user-friendly tools can also serve as an obstacle. This struggle to adapt to and effectively use AI poses a significant barrier to realising the technology’s full potential within an organisation.
Some more numbers: according to research from IBM, a sizable 63 per cent of people surveyed identify insufficient technical skills as a roadblock to rolling out AI solutions in their organisations. This problem is accentuated when executives, without the appropriate skill set and tools, take the helm of AI initiatives. Confirming this predicament, 82 per cent of respondents in a study published by MIT Sloan Management Review indicate that they’re stymied in their efforts to move AI applications from the experimental phase to actual deployment. The situation is further complicated by the lack of in-house data science talent. A 2018 Deloitte study by David Schatsky projects that the US will face a shortfall of 250,000 data scientists by 2024, based on the current supply and demand metrics. This lack of expertise hampers AI’s functionality, as the technology is heavily reliant on the availability of the right kind of data. Without sufficient and accurate data, AI algorithms are handicapped, if you will, rendering them ineffective in tasks as basic as making recommendations — a vital flaw when AI is expected to be a cornerstone in business decision-making processes. It is thus increasingly apparent that the successful deployment of AI is directly tied to the quality and volume of data that an organisation can handle. To accomplish this, HR departments should initiate AI training programmes, collaborate with IT to identify user-friendly tools, and form cross-functional teams to encourage AI adoption. Ethical guidelines and continuous feedback loops should also be established to ensure responsible and effective use. By taking these steps, HR can pave the way for a more agile, informed, and productive workforce, fully capable of leveraging the transformative power of generative AI.
“The end goal is that we understand the mathematics behind cooperation with people and what attributes artificial intelligence needs to develop social skills. AI needs to be able to respond to us and articulate what it’s doing. It has to be able to interact with other people.”
Jacob Crandall, Brigham Young University
Can socially-skilled algorithms teach human-machine collaboration?
As our future increasingly integrates artificially intelligent systems, ensuring these diverse entities seamlessly collaborate with humans and other machines becomes crucial. A research team led by two well known computer scientists, Iyad Rahwan from the Massachusetts Institute of Technology and Jacob Crandall from Brigham Young University, has made strides in this direction by creating an algorithm with built-in ‘social skills’. Known as S# (pronounced S-sharp), the algorithm was tested in human-machine and machine-machine interactions through a variety of two-player games. S# consistently outperformed humans and other algorithms in fostering beneficial relationships. One test involved the Prisoner’s Dilemma, a game that often highlights the shortcomings of rational collaboration between two people. Another test was a complex block-sharing game. S# demonstrated an ability to communicate and understand human cues, enabling it to excel in unexpected scenarios. Interestingly, the AI is designed not to blindly collaborate; it could express ‘displeasure’ if its human counterpart did not behave collaboratively.
Despite fears that AI may pose a threat to humanity, these new findings suggest that machines can be programmed to follow ethical and legal principles, much like Isaac Asimov’s famed Three Laws of Robotics. The team noted that effective AI collaboration requires versatility across different scenarios, the ability to forge beneficial relationships without prior knowledge, and skills to encourage collaboration even from distrustful entities. Moreover, the algorithm must be capable of quick learning, reasoning, and adapting to find mutually constructive solutions. In summary, this new algorithm not only has the potential to bring us closer to the day when machines can seamlessly collaborate with humans, but it also redefines the role of AI in complex social settings. It represents a pivotal step in understanding the mathematics behind human-machine collaboration, reaffirming that AI can indeed be designed to interact responsibly and effectively with humans and other machines. “The end goal is that we understand the mathematics behind cooperation with people and what attributes artificial intelligence needs to develop social skills”, says Jacob Crandall. “AI needs to be able to respond to us and articulate what it’s doing. It has to be able to interact with other people.”
HR departments can leverage socially-skilled algorithms like S# to facilitate smoother human-machine collaboration in the workplace. By using these algorithms in training simulations or real-world scenarios, employees can gain hands-on experience in collaborating with AI, learning how to communicate intentions and interpret machine responses. This can be particularly useful in industries where human-AI interaction is frequent and mission-critical, such as healthcare, finance, and manufacturing. Deploying socially-skilled algorithms also offers the benefit of training AI systems to adhere to ethical and legal norms, making them safer and more reliable collaborators. Ultimately, the successful integration of these algorithms can lead to more effective and harmonious work environments, bridging the gap between human capabilities and machine efficiencies.
Are (almost fully) automated customer support departments the future?
(Almost fully) automated customer support is no longer a distant vision but an attainable reality. In the future, customer service departments will see even more seamless integration between human agents and AI tools. Customer service reps will soon be able to focus solely on intricate, high-stakes issues while AI handles routine inquiries, all the while learning from human agents to offer increasingly sophisticated solutions. Take Intercom, for instance, a San Francisco-based technology company specialising in customer communication software. The company has been at the forefront of incorporating artificial intelligence (AI) features to enhance customer service operations. One of these AI-driven features includes intelligent chatbots that can handle customer queries around the clock. These smart bots are capable of understanding context and nuances, thus providing more accurate and personalised responses. This not only improves customer satisfaction but also frees up human agents to deal with more complex issues. Intercom has recently rolled out an array of new AI-driven features. These include a feature that expands bullet points long-form content, a composer equipped with tone and phrasing adjustment capabilities for more nuanced messaging, a utility that generates articles from shorthand notes, and conversation summarisation tool that distills lengthy chats into key points. Intercom is invested in refining them further by enhancing their contextual understanding of conversations and adapting to natural human speech patterns.
The introduction of fully automated customer support departments like the ones made possible by Intercom also completely reshapes the role of human resources management. The need for hiring and training in customer support may decrease, redirecting HR focus towards roles requiring human expertise like R&D or strategy. Existing support staff may require re-skilling, necessitating new HR-led training initiatives. The traditional hierarchical structure could flatten, leading to adjustments in reporting lines and requiring HR to manage issues of job security and employee morale. Key Performance Indicators may also shift from quantity to quality-based metrics. HR will need a contingency plan for technical glitches that automated systems can’t handle, involving a rapid response team of human agents. Compliance with legal and ethical standards, especially concerning data protection, will remain a critical HR responsibility. With this type of large-scale automation triggering a cultural shift, HR’s role will transition from traditional functions to becoming a critical change agent. It will be responsible for aligning employees with new organizational goals and technologies, likely through substantial internal communication and cultural realignment programmes. The focus will thereby be less on routine staffing and more on strategic involvement in organisational transformation.
As AI continues to mature, its role in human resources management will continue to grow as well, ensuring that HR will no longer merely be a supportive backstage function, but that it will play a leading role as a forward-looking strategist in shaping the future of organisations and entire industries.
In closing
It’s astounding to think that just a short while ago, the idea of AI engaging in human-like dialogue and crafting creative content was reserved for the world of science fiction. Yet now, it’s becoming an everyday reality in more and more industries. Though the future remains uncertain, it’s a reasonable expectation that AI’s influence in human resources management will also continue to expand. As the technology becomes increasingly sophisticated, its applications are expected to proliferate across a variety of fields, ranging from healthcare and banking to media and academics, and its ripple effects are bound to be even more profound in the years to come. In the short term, we’re witnessing increasing operational efficiency and improved customer satisfaction, thanks to automated processes and data analytics that provide personalised services. But the long-term implications are even more exciting. AI technologies will enable the creation of entirely new products and services that we can’t yet fully imagine. Moreover, they will drive the development of new operational processes that streamline productivity in unprecedented ways. Whole new categories of jobs and services will emerge, alongside companies that will rise to prominence in newly minted business sectors, collectively restructuring the global economic landscape. And as AI continues to mature, its role in human resources management will continue to grow as well, ensuring that HR will no longer merely be a supportive backstage function, but that it will play a leading role as a forward-looking strategist in shaping the future of organisations and entire industries.
