AI for Talent Management Professionals

In this two-part series, we will share a few things we have learned while experimenting with and learning about Artificial Intelligence. In this post, we will provide some basic information for talent management professionals around the concepts of AI. In our second post, we will explore some possible applications and tips to deploy AI in the talent management space.

What is Artificial Intelligence?

The late John McCarthy, a Stanford American computer and cognitive scientist, who in part coined the term Artificial Intelligence, defines “it as the science and engineering of making intelligent machines, especially intelligent computer programs”.

If we take that a little further, Artificial Intelligence (AI) is a branch of computer science that involves creating machines that can perform tasks that typically require human intelligence, such as understanding natural language, recognising objects and patterns, making decisions, and solving problems. AI systems use algorithms and mathematical models to analyse data and learn from experience, allowing them to improve their performance over time. Essentially, AI involves building machines that can "think" and "learn" like humans, and use this knowledge to make decisions and solve problems.

Types of Artificial Intelligence? 

There are several types of artificial intelligence, which can be broadly classified into the following categories:

1. Reactive Machines - These are basic types of AI that do not have memory and only react to current inputs. They cannot learn from past experiences or make predictions about the future. For example, an industrial robot arm that is programmed to perform a specific task, such as assembling parts in a factory. The robot arm does not have any memory or ability to learn, and it only reacts to the current inputs from its sensors to perform the task.
2. Limited Memory AI - These types of AI have limited memory and can use previous experiences to inform current decisions. They can recognise patterns in data, but their learning is not continuous. For example, the fraud detection systems used by credit card companies. This AI system is trained on a large dataset of historical transaction data and is able to recognise patterns of fraudulent behaviour based on that data.
3. Theory of Mind AI - These types of AI can understand the emotions and intentions of others and make decisions based on that understanding. They can predict how others will react to their actions and adjust their behaviour accordingly.
4. Self-Aware AI - These types of AI have a consciousness and can think about their own existence. They can reflect on their own thoughts and emotions and have a sense of self-awareness.
5. Artificial General Intelligence (AGI) - AGI is the hypothetical form of AI that can perform any intellectual task that a human can. It would be capable of learning and adapting to new situations, reasoning, problem-solving, and understanding language and emotions. It's important to note that the development of AGI is still in its infancy, and it currently does not exist. 

Most of the AI we interact with today falls under the categories of reactive machines and limited-memory AI.

Implications for Talent Management

The development and deployment of AI in an organisation can be both positive and negative. Below are some of the key implications from two perspectives:

1. Direct Implications for Talent Management Professionals 

• Efficiency and productivity - AI can automate many routine and administrative tasks in Talent Management, such as candidate screening and assessment, allowing HR professionals to focus on higher-value tasks.
• Skills gap - AI requires specialised skills and expertise, and the use of AI in Talent Management may create a skills gap for HR professionals who do not embrace and experiment with the applications of AI.
• Redundancy - The use of AI in Talent Management may lead to job losses for HR professionals who perform tasks that can be automated by AI.

2. Implications for the Work Talent Managers in Organisations

• Employee experience - AI can help to enhance the overall employee experience by providing personalised recommendations and opportunities for career development, and by automating administrative tasks that can be time-consuming and frustrating for employees.
• Employee privacy - As AI systems collect and analyse large amounts of personal data, there is a risk of violating employee privacy. Therefore, it is important to ensure that any AI systems used are designed with strong data protection measures and employee privacy in mind.
• Personalisation - AI can help to personalise the Talent Management experience for employees, by delivering customised learning and development programs, feedback, and recognition.
• Predictive analytics -  AI can analyse large amounts of data to identify patterns and trends, which can help HR professionals to make more informed decisions about talent management and workforce planning.
• Talent acquisition - AI can help to improve the talent acquisition process by identifying top candidates more quickly and accurately, reducing time-to-hire and cost-per-hire, and improving the candidate experience.
• Continuous learning - AI can help to support continuous learning and development for employees, by delivering personalised training and development opportunities based on individual employee needs and preferences.
• Job displacement - As AI replaces human involvement, it is important to have programs in place to help retrain and redeploy people in the organisation.
• Strategy around AI - An opportunity exists for training employees on how they integrate AI into their work. For example, what are the organisational policies around AI, and how can AI augment work in an ethical way.

Overall, the implications of AI for Talent Management are complex and multifaceted, and it is essential to carefully consider the potential benefits and risks of using AI in this domain. By approaching the development and deployment of AI in a thoughtful and ethical manner, we can ensure that AI is used to improve the employee experience and support organisational goals.

References

• Britannica n.d., Alan Turing and the beginning of AI, accessed 5 April 2023, https://www.britannica.com/technology/artificial-intelligence/Alan-Turing-and-the-beginning-of-AI
• Burns E, Laskowski, N and Tucci, L 2023, artificial intelligence (AI), accessed 5 April 2023, <https://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence>
• IBM n.d., What is artificial intelligence (AI)?, accessed 6 April 2023, https://www.ibm.com/topics/artificial-intelligence
• Joshi, N 2019, 7 Types Of Artificial Intelligence, accessed 5 April 2023, https://www.forbes.com/sites/cognitiveworld/2019/06/19/7-types-of-artificial-intelligence/?sh=1756c29d233e
• Monash University n.d., Using artificial intelligence, accessed 6 April 2023, https://www.monash.edu/learnhq/build-digital-capabilities/create-online/using-artificial-intelligence
• Wikipedia 2023, John McCarthy (computer scientist), accessed 5 April 2023, <https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist>