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Ojomo Olorunfemi Olutayo, Head Human Resources, Julius Berger NigeriaWhen examining the etymology of “Human Capital”, can be traced back as reported to the 1960s when two American Economists by the names Gary Becker and Jacob Mincer made the word popular. They used it in describing the mixture of skills, knowledge, experience, habits, and personality in each of us which can be put to productive use. Should we decide to give this claim some careful consideration, we can conclude that the first reports on human capital will be from the period when the Tower of Babel was being built. However, pundits can also postulate that the right period is neither of these two historical references.
The essence of this article is to explore the subject of Human Capital Reporting and the potential of having AI thrive in all areas related to it. Human Capital can be dissected by looking at the meaning of each of the two words involved. Human is all about the being; us the people who are formed in similar likeness. Capital is the relativeness of the worth of a thing that can be channelled into motions to derive value-add.
In describing these two words “Human Capital”, it is safe to say that it is the worth embedded in us the human to transform time, space, and resources into value. In this instance, value connotes monetary and non-monetary advantage that benefits the partial owners of such human capital.
The concept of reporting gives an account of events and happenings as they might concern distinct subjects. For this to be the case, there must have been some elements of tracking, recording, and processing. Human Capital Reporting is the deliberate venture to identify
key human capital metrics, and introduction of the right systems that can ensure that the trends of activities as they concern the human assets of a business are followed, captured, cleaned and reported for next-stage activities.
This sounds like a lot; a lot of moving parts that require commitment, skills and resources. Indeed, it does. However, the overarching benefits of this venture supersede the tears of getting the Human Capital Reporting System working within the average organisation.
"The advent of AI makes it easier for the personalisation of Talent engagement outside of the traditional notions of what keeps an employee engaged in an organisation"
Sizes do not matter in justifying the importance and benefits of Human Capital Reporting in an organisation. However, focused metrics/KPIs can be determined by the size and stage of growth of an organisation. Prevention is better than cure takes the trophy; more apt in this context as always referenced in the Health’s. Reporting on Human Capital lays the track for preventive measures within organisations. The need for predictive and prescriptive analysis can only be achieved when data that speaks to Human Capital metrics are tracked, collected and subjected to the required analysis. The scope of coverage in terms of reporting on Human Capital cannot be capped to some select metrics/KPIs. In exploring some possibilities within this context, provisions of KPIs as referenced by ISO 30414 and other thought-relevant KPIs will be treated.
The possibilities of AI delivering value across these areas of Human Capital reporting cannot be overemphasised. Artificial Intelligence in its simplest descriptive will be systems that combine Computer Science and robust data sets to enable problem-solving. It is an s system that leapt and has taken flight from the inputs of its beginning as provided by humans. It can think like a human, surpass human thought processing and provide insights that are born out of algorithms. It gets the job done.
In exploring the employee life cycle, the point of entry recruitment is critical. Myriads of data can be collected at every new hire’s touch point with the organisation. Infusing AI makes it possible to derive predictive models that will be useful in making critical decisions on types and kinds of hires that are suitable for the organisation and across different job families. Within the scope of recruitment, it will not be out of place to explore metrics that help to determine the quality of hire for some types of the job based on many indices, the thriving capabilities that have proven to be the best fit for the organisation and the future possibilities of such capabilities within the trajectory of the business, the generational mix subjects and attending issues that shape and mar Talent configuration at the different stages of business growth while also being observant of the industry and business terrain. AI is well positioned to deplore its algorithm; putting datasets through different probabilities and providing predictive and insightful prescriptions that are fit for the present and the future. With this, Talent Management errors are considerably minimised or eliminated.
Employee productivity and managing of employee performance is a critical part of Human Capital that needs to be tracked and reported. The application of AI algorithms in this aspect of Human Capital reporting will help yield unimaginable benefits for any organisation. With AI, it becomes a lot easier to decipher behavioural patterns of Talents as it relates to productivity and their likely triggers. This makes interventions for scaling productivity spot on through its customisation. In focusing on metrics that matter, organisations can track EBIT/Revenue/Turnover/Profit per Staff, Human Capital ROI and Performance across different classifications and variables.
AI is also in a better position to help predict accurately when productivity from Talents will peak; reach its maximum. This allows for proper planning and Talent utilisation. The outcome of individual performance or non-performance can be supported with AI personalised feedback and coaching-focused areas which are a product of individual performance data that are layered over the appropriate AI algorithms.
Employee engagement is a critical ingredient for productivity and performance. The advent of AI makes it easier for the personalisation of Talent engagement outside of the traditional notions of what keeps an employee engaged in an organisation. It has become easier to personalise incentives and administer rewards and recognitions based on the outputs of AI prescriptions. With the adoption of AI, it makes life easier and quicker to spot the not-so-good trends and that worth reinforced.
It is fair to also speak to the limitation of AI within the scope of Human Capital reporting. Behind the smooth operations of AI is the element of machine learning which entails learning behaviours and gaining insights over time based on the datasets which has since given flight to AI algorithms. Should there be a wrong inputting or sourcing of data, it opens the algorithm to spitting out the wrong diagnostics, making wrong inferences for predictability and prescription. Though it stands the chance of self-correction based on advanced AI possibilities, immediate damage would have been done. A flip side to AI prescription on data findings will be the absence of empathy and emotional feelings which the human would have consulted before making some decisions. Prescriptions from AI as a result of Human Capital reporting needs to be subjected to psychological and behavioural evaluation; adjustment or consideration before adoption as fit for use. The presence of dominant demographics is often time misconstrued as a representation of facts by algorithms, which is often time, not the case. As such, organisations deploring AI algorithms need to be mindful of this and ensure that the denominators are well captured for clear differentiation.
Prompting OpenChatGPT, the pictorial below shows a few recommendations for AI-enabled platforms that support in-depth Human Capital Reporting:
Finally, a lot can go wrong if AI is not properly channeled in Human Capital reporting. However, working within the ambits of legal frameworks, regulations and ethics will make AI one of the many wonders that have and will continue to happen to Talent Management and by extension the field of Human Resources Management.
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