Artificial Intelligence (AI) could soon assist the IAF in planning the postings of officers to various units and establishments that is expected to bring in greater transparency while cutting down on manual processes and human subjectivity.
As part of revamping its human resource management, the IAF is looking at developing an AI-based system that can help optimise the process of transferring officers as per organisational requirements as well as personal career progression.
The system is envisioned to reduce the tedious manual work, subjectivity and time involved. The present process is bogged down by high costs in terms of manpower utilisation on administrative tasks like collection and analysis of data, communication with individuals, approvals from the chain of command and coordination. Factors like human bias and errors also creep in.
AI-based system would be designed to automate and streamline the posting planning process by using advanced data processing and analytics techniques. It would improve the accuracy and fairness of posting decisions by using objective and transparent criteria and by considering multiple factors and constraints, such as organisational needs and goals, officers’ performance, qualifications and preferences and the relevant policies and regulations.
The system would undertake routine posting tasks on the basis of historical data, policies, guidelines, correspondence, skill mapping and an individual’s ability to achieve the tasks, besides presenting multiple probable solutions and an analysis of the impact of a particular movement on other locations.
The IAF has an officer strength of around 13,000 and they are governed by policies issued by the Defence Ministry and Air Headquarters. Deficiencies in cadre strength, which vary from year to year also has an impact on deciding the postings of officers or the duration of stay at a particular station.
There have also been instances in posting orders getting embroiled in litigation over perceived unfairness on the part of the individual affected or incorrect interpretation of relevant policies.
