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AIM 3.0: A UX that incentivizes reporting and improves talent management

by Kyle Paget


The AIM marketplace has rescued the Army “from its industrial age pay and personnel system that only sees rank and branch,” putting in its place a “talent management-based approach.” AIM is built as a decentralized marketplace that allows Officers and command teams to engage directly, removing the need for a job gatekeeper at HRC.


AIM encourages users to reveal information about themselves that is not otherwise captured on an OER/ORB, helping command teams better screen the talent it recruits. It also gives command teams an opportunity to compete for talent with job descriptions that outline how an Officer could develop a unique skillset or advance faster in a less prestigious role or less desirable location.


AIM 2.0 does not, however, generate the kind of structured big data necessary to employ precision workforce analytics. The Army must grow its senior leaders internally over 30 year careers. Therefore, the talent management decisions and investments it makes today will determine the quality of talent leading the organization in the year 2050. What follows is a proposal for a phone application that would capture valuable structured talent data while benefiting the individual officer and command teams.


How it works


You arrive at your first unit and are put into a duty position, platoon leader. You download an app on your phone and make a profile about yourself. The app contains a list of hundreds of common platoon leader responsibilities: run a range, organize H&F, counsel soldiers, etc.


Let’s say you have a range coming up. You open the app and click a little tile that says “run a range”. A checklist of the subtasks needed to run a range appears and a “run a range – started” tile appears on your profile.


You run the range.


When you’re finished, you log it as “completed” on your profile and input a couple data points about how it went. You press submit. The commander or training officer receives an alert and verifies the task was completed - potentially providing feedback or rating performance. You continue adding tiles/tasks to your profile throughout your rating period.


How you could benefit


You get recognized for all the work you do and get more frequent feedback from your leadership. Subtask checklists associated with each tile would also help accelerate the onboarding process for new LTs. You could receive a point for coaching a local sports team, reading a book off the chief of staff reading list, etc. If the right data is collected, each officer’s profile would quickly develop unique characteristics, ideally enabling the Army to better match your knowledge and skills with opportunities. It could also improve its professional development pipelines to target the subpopulations which emerge.


How command teams could benefit


The Command team or Army could gamify the app by assigning different values to the completion of different tasks, in order to incentivize focus on certain tasks at different times. Commanders could also set development goals for subordinates through the app (attend UMO class, become a certified ACFT instructor, take a public speaking class).


How the Army could benefit


Armed with structured data, the Army could use Machine Learning to cluster profiles to better understand its different talent pools and match talent with unique career progressions. It could also improve Officer training and schools to better meet the needs of its unique Officer subpopulations. This data is currently unstructured in OERs, support forms, and AIM 2.0 preventing the Army from extracting and analyzing it.


Potential Issues


Beyond the upfront costs for development and implementation, a couple problems jump out at me. I don’t think this could work in a deployment setting because I assume downrange tasks are especially nonstandard.


There’s also the issue with measuring intangibles like presence and character. A simple fix would be to still include an end of year wholistic evaluation. Or, each profile could have badges you award to others like LinkedIn skills. The value of the badges could be proportional to the number of badges you give out, to preserve data quality.


If enough quality data is collected, however, that second issue might resolve itself. While not measuring character or presence directly, perhaps other elements of a profile could provide a signal value that when aggregated hints at a profile’s likelihood of possessing some intangible trait.


The Army has taken a very important step by introducing market forces to its internal labor market. As AI continues to disrupt traditional industries, the Army should consider how it can generate the quality structured data necessary to employ AI algorithms and continue to lead the fight on the War for Talent.


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