AI can help mitigate talent risks: Pramod Sadarjoshi
Pramod Sadarjoshi is Senior Director, HCM - Strategy and Transformation at Oracle. An HR specialist with over 25 years of experience, His professional experience runs across organizations such as JP Morgan Chase, IDBI bank, Computer Sciences Corporation, Cognizant Technology Solutions, Oman International Bank, Microsoft Corporation, HDFC Bank and Citibank.
In this interview, he talks about how machine learning can inform workforce management, the key HR functions that are being impacted by the disruption. And the key challenges that the function needs to overcome.
Q1. About two years ago, conversations in HR Tech were centered on SMAC and Big Data. These days the focus is on AI, Machine learning, automation and ChatBots, how are these technologies changing the HR technology ecosystem?
HR all over the world has reached an inflection point where everything they have done so far can be disrupted. While the human mind can be subject to work pressure, unconscious bias and cultural influences, AI and allied technologies bring in a rigorous perspective that is backed by data.
Machine learning is about identifying patterns within the data. And programs backed by machine learning help achieve two things:
- Granular inputs to inform employee workforce engagement
- Meeting the business outcomes, whereby HR is able to deliver value to the CEO and CIO in terms of measurable metrics.
That’s the broader context I want to share. There are three to four function areas within the HR where the impact is being felt: 1) Talent acquisition, 2) Talent review 3) Compensation and benefits and 4) Overall employee engagement. If you see through these functions, the constituency that matters to the HR is the employees and the senior management, who are asking for metricized HR interfaces.
For talent acquisition, for example, the focus will be on measures like cost per hire, time to hire and quality of hire. These data points will help the talent acquisition specialist sift through granular data at economies of scale to provide the right person for the right job at the right time at the right cost.
Similarly, with training, investments need to be backed up with the ROI. Especially when it comes to behavioral programs on culture and change management. What AI and machine learning are capable of doing is to break down the human psyche and understand various indicators. It takes into account 42 aspects of human personality by linking it back to– 1) Individual, 2) The role and 3) The business. Therefore, it helps align all HR programs to both CHRO and CFO at a very measurable dimension of cost, return and investments.
The best thing that AI does is the mitigation of risk. There can be multiple risks that affect a decision, and it may be related to talent, geographical, political reasons. In the context of HR, mitigating talent risk is one of the top job priorities for HR professionals. Machine learning can give you a clear idea and information for you to mitigate issues. For example: As a customer, you may be looking at certain conditions while you purchase products – You will ask if it is cheap? If it is good? And is it fast? And often the market tends to promote two options; if it is good and fast, then it will not be cheap. But with any HR function – whether it is training, planning, employee engagement, by leveraging AI, one can have all the three features – faster, cheaper and the best.
Q2: What are some of the top challenges that you are seeing your customers face while embarking on this journey to AI?
AI and machine learning are desired by everyone but when in reality there are certain challenges. In my experience, the CEO and the management team often lack the expertise needed to drive a vision. There are not many people, both technically and behaviourally who are aware of AI, machine learning, and data science. One way to tackle this challenge is to turn to external expertise. There are B-Schools that are building the academic expertise. I think recruiting talent from B-schools and also from the market as lateral hires, can help tackle the challenge.
A lot of companies do not invest in the change management and in finding out the requirements of stakeholders. There is a need for a six sigma kind of implementation when you undertake projects involving AI. It should be universal throughout the organization. Even if one segment of the organization is not on-board, then, the situation can result in culture eating strategy for breakfast.
Another challenge is that very few organizations are making the time to invest in ensuring the proper infrastructure. There have been times when I’ve found that a company has a great strategy, vision and is an industry leader but their employee data is not digitized or changed at all. In some cases, to meet the deadline for digitization, people compromise on several things.
And the last challenge is – most change happens in the year of job loss or redundancy in the reduction of imports. One should be careful to make sure that the management team is taking into account the need to remove fear and insecurity. If there is resistance, you must approach it from top to down. It should be a culturally and humanly involved process.
Q3: There are many social collaboration tools, sensors and allied technologies at the workplace today. Reports claim that employees are starting to feeling overwhelmed. What is the specific role of HR in managing this change?
I think there needs to be a uniform and unmistakable message that we are on an AI machine learning path. Irrespective of the industry, whether it is cement, paints, ice cream or evergreens, the business ecosystem is going to be led and driven by technology. So, its impact is not an option which means adopting these technologies or AI is not an option.
Employees should be coached and trained. Even after all the efforts, if there is resistance, shyness or apprehension, we need to speak, train and bring about the necessary rigor and shift.
The second important thing is to have large investments in terms of architecture for training, onboarding as well as recurring training interventions. The resources, infrastructure, teams, and training are critical. This should be backed up with behavioral and 180-degree or 360-degree competence assessments. People should be brought along to bring change in technology and techniques.