AI Use Case 1: Step 2 Future State of HR Hiring


So to summarize from the last post.  The first primary step in designing for AI projects is to align to the business strategy. As discussed earlier a specific use case is needed. As well as understanding current state processes, capabilities and outcomes.  Now that we understand the state of the organization and the intended high level outcome. We get into to aligning AI capabilities that could best address the desired outcome.

A US National law firm in leveraging prediction ML models could apply it to these processes:

The hiring process. By improving candidate identification which would then reduce hiring costs and time. HR knows all the variables of good candidates; but cannot do it by hand today in the time needed. Having an ML Model improve candidate identification would help the HR team.

Predicting attrition factors of a hired partner would be another process.  Early intervention would reduce risk and cost associated with losing a law partner.   The ML model could analyze more data points for each partner faster than HR.

Skills management would be another process to automate. Identifying skill gaps by combining performance reviews and other sources of data would benefit each lawyer’s assessment.   Since more data points would provide better insight into where a lawyer would need training. Which would support the market strategy of industry practices and expertise.

Improved HR performance reviews would be another area that would enjoy ML models. Analyzing more factors of a lawyer’s activity; versus semi-annual reviews, would give better insight. Documentaton: time, court time, legal preparation time as well as HR data provide insight into a lawyers performance.

Setting a vision or desired future

The cost to hire, train and keep a skilled lawyer can be high. The efficincies gained by greater insight into lawyer HR data will reduce overhead costs. ML models would predict candidates potential of attrition and skill level matching in a more automated fashion.

Machine Learning will only improve HR assessment and feedback. As well as; identifying early intervention on job dissatisfaction. The law firm would be able to improve it’s work place ratings on a national basis. Thereby; making a more desirable location to work and providing differentiation in the market place (another Porter strategy dynamic).

When the firm is able to hire the best resources at a lower cost. The market focus that they provide will only improve and provide clear competitive differentiation. Which is in alignment to their market strategy of industry focused teams. 

Current state is manual based. And on based on human experience only.  With the ability to increase data points and measurement the combination of ML and HR resources would create an increased collective intelligence. This is in line with the three market factors of Porter: Cost, Differentiation and Focus for the company.

Technical and leadership and managerial requirements

In order for Machine Learning to provide the most benefit requires first. Subject matter experts to assist in the development of the AI project. Thus, Practice Leads interviewing staff, HR leads, existing BI, and tech resources need to take part.   All these roles will need to engage with the development of the model and the testing and training of the model.  There has to be executive sponsorship in order for this to succeed. This sponsorship’s role is to communicate and ensure the project stays on track and schedule but also be seen as involved. 

Technical requirements would ensure that the sources of data such as: HR and Case Management data could be accessed and offloaded to “parameterize” the data sets. The “parameterizing” of the Machine Learning model is critical the success of AI.   Natural Language Processing will extract data from CV and other free-form text sources of information.

Next instalment will look more closely at the NLP aspect of this use case. As well we will look at the need of a “domain” or corpus of knowledge that is needed for the AI to be of any value.

Additional technical requirement will be.  What does the organization have currently.  Does the tech staff have the appropriate training for; BI tools, Predictive Analytics, Data Integration tools.  Another consideration are; can this be done in the cloud or does it need to be done on premise due the sensitive nature of data sets.  What are the security and privacy capabilities of the ML technology? Has an ethical review been done on types of information and sourcing to minimize risk ?  These questions need to be addressed by all of the team members.

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.

%d bloggers like this: