AI Use Case 1: HR Hiring and Staff Attrition Step 1 Knowing Current State

In this new set of series I will post and reflect on engagements that I have had with organizations around the world.  I will discuss what they are attempting to do with various AI related technologies and then provide updates on the projects as they progress.  Like all AI projects they tend to start smaller in scale and more importantly with a specific use case or business outcome needed.  For reasons of confidentiality I will not name or describe the actual organizations in any detail.  What is important is to learn from their approach and what succeeds.

Step 1: Understanding their current state

The organization is a large law firm in the US.  They are currently attempting to improve their hiring process to ensure they can hire the most talented people for the various law practices.  Currently they use standard HRMS technology to track skills assessment and performance metrics; but only once a person is hired into the firm.  They currently use descriptive analytics to run standardized reports on historical data only.   They now want to improve how they hire lawyers.

To hire the best talent is a highly competitive process.   Each year the number of law students is limited, and the process is controlled by the law societies.  Therefore, this firm wants to be better at quickly identifying the top talent so that they can interview and potentially offer them a summer placement before their competition does.  The current process is extremely manual in nature and therefore they wish to automate the process which can help reduce the costs of hiring.

Strategic alignment of the use case to the organizations corporate strategy is key in ensuring successful deployment of the AI technology.  The external strategy of the organization is to differentiate themselves with their industry experience and team focus.  Internally the strategy is to optimize cost in the recruitment and retention process. The investment that the law firm makes in hiring and training a lawyer can be high; therefore, they wish to maximize their investment in a person that is hired and employed with them.

Current state is a highly manual process.  The hiring team has to read thousands of resumes and supporting material and summarize and review each candidate.  Then; setting up interviews of the top candidates with the view of hiring a certain number of summer students each year.

On the initial assessment of current state it was felt that targeting the internal strategy of lower cost would be the best area to focus on.  This strategy of lowering cost to gain the best legal resources is where AI can assist. 

The HR and hiring teams have a clear understanding who makes an ideal candidate and having an AI system that can read resumes and other sources of information on each candidate would speed up the candidate identification process and reduce the risk and cost of attrition over time.  This then supports their external strategy of having the best industry focused legal teams for the clients they serve.

Certain questions and concerns around data use and data privacy will need to be discussed as well as ethical considerations.  The HR team that participated in this workshop highlighted the fact that they have a pretty good ‘sense’ on how to identify strong candidates.   However; they were also hoping that as the AI technology learns from assessing candidates that the HR team would learn of candidate dynamics that they may not have considered in the past.  As an example a student applying from overseas may not have the standard experience and educational background that typically is reviewed.  

One other observation in running this workshop which will be echoed across all of the use cases I describe is that having line of business as well as IT in the room at the same time is extremely beneficial.

Next posting will be on future state and where AI can assist.

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