I'm currently working with a client organisation to implement strategic workforce planning - an area which has great potential for AI implementation.
The workforce challenges facing my client is an opportunity for AI implementation in HR. The organisation's mission is ambitious, however, the organisation struggles to recruit and retain critical skills which impedes their progress. Reliant on the capability and capacity of critical skills to achieve its mission, I developed a strategic workforce planning framework to equip them with a mechanism to proactively manage their workforce needs.
The strategic workforce planning data collection, analysis and management decision-making process is potentially suitable for automation. Decision-making is reliant on workforce data and information about future work. There is an abundance of data held in multiple and disparate systems. The data analysis team frequently gather and analyse data on a routine and repetitive basis to inform management decision-making. The role requires logical-mathematical intelligence, characteristics of which includes an ability to identify logical or numerical patterns or risks and to analyse problems in the data. Given the complexity of the data and multiple sources, analysis is slow, cumbersome and error-prone.
AI has the potential to speed up the analysis of data and to recommend solutions to workforce risks. It will not automate all roles in the process but rather the routine and repetitive tasks and augment the decision-making process. For example, Alpha Go https://www.deepmind.com/research/highlighted-research/alphago which excels at problem-solving, planning and reasoning, demonstrates the potential to develop an algorithm to forecast the FTE trajectory of roles based on an analysis of the current supply and future demand of labour. The algorithm is conditional upon the availability of relevant big data for it to draw upon and state-of-the-art computer equipment. The human brain struggles to process big data compared to AI which is adept at quickly analysing vast amounts of data saving time and labour.
There is also a potential to automate a part of decision-making by training the algorithm to apply the predetermined interventions from the framework to each risk identified. Decision-making requires high levels of interpersonal intelligence and therefore the algorithm will not replace decision-making in its entirety but rather augment it. Management require an understanding of individual needs e.g. personal and career aspirations which requires verbal communication. Rather than replace the decision maker, AI can reduce the burden of data analysis and instead complement them and focus their efforts on exercising emotional intelligence. AI therefore has the potential to impact at an organisational rather than at a personal level.
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