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.

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