I would like to share a few thoughts around numerous new staff roles and jobs that have presented themselves in recent times due to the rapid adoption of AI & ML within industry. This situation has been made possible with the digital revolution that is incessantly unfolding in the world of business I will attempt to stimulate thought with a practical business example to help relate to roles presented at the end of the article.
Applications of AI& ML techniques span many industries including banking, insurance, healthcare, logistics, manufacturing, marketing, oil & gas, pharma, retail, telecommunications, and transportation, among others. Use cases are centred around ways to improve operations, maximize revenues, minimize costs, enhance profitability, drive competitive edge, improve customer experience, and so on. The drivers for such initiatives are hidden deep in the data that is generated through the execution of related enterprise business processes. For context, let us walk through a simple business process example i.e., the hiring process in organizations.
It is common knowledge that strategy-focused organizations would be staffed with the right people and the right skills at the right time to further the objectives of the company. The recruitment process is also critical to attracting the right candidates. The efficiency of organizational processes reflects how an organization is set up. In many organizations with good systems, it is reasonable to expect the recruitment process is well instrumented to capture critical data about each position and the candidate. The data covers all aspects from job requisition to onboarding.
Applying AI & ML to the data generated by this process can reveal opportunities to improve the state of the process and its outcome. Some key questions that stakeholders are interested in finding answers to are:
1. How many days ahead should we plan to fill a position?
2. Can we predict the likelihood of closing an offer?
3. Can we predict the time it will take to fill positions?
An AI & ML project in this area entails several activities and require a team of individuals with different skills and competencies to collaborate on projects aimed at answering the above questions. It requires data acquisition, data cleansing, through modelling and analysis. It requires a variety of skills including data engineers, data administrators, data architects, ML engineers, project managers, business subject matter experts, and so on.
Author : Vijay Sai Vadlamudi