Artificial intelligence (AI) is changing the way businesses operate, and more variations will occur. From storage warehousing, determining what and how much inventory to maintain based on an array of factors (including competition and seasonal items), to the use of AI in communications to employ underutilized portions of the electromagnetic spectrum without human intervention, the technology will continue to drive how organizations make decisions.
HR and the C-Suite Uses of AI
Human Resources departments utilizing AI today can complete a variety of tasks, including benefits enrollment, administration, employee hiring, and paid time-off (PTO) tracking. Furthermore, from an employee’s perspective, AI provides access and information to assist in decision-making processes.
In our current environment, we are obsessed with data—not only the ability to generate it but to answer the most basic of questions: What does the data tell us and what can we actually do with it?
Consider the following:
- Claims analytics: Healthcare spending can be reviewed in a variety of ways to better discern causes and impacts of specific areas. More importantly, it can point to areas within benefit plans that are not being fully utilized to permit better resource allocation to the most important areas. In addition, this data allow us to consider a variety of options to better control and contain costs.
- Ability to use data for total compensation: Data organized will be able to provide useful information about PTO, benefit choices, base pay, incentive pay, tuition reimbursement plans, etc. Imagine a world where we could use this data to create total compensation options tied to your employment population! One size clearly cannot fit all in today’s multiple-generational environment. We need to cultivate the ability to create total options that more closely parallel employee needs, while still being competitive, cost-effective, and compliant.
- Increased HR decision-making: At this point, HR has data and a lot of it, either current or historic on employees, their choices, their geographic area, and their demographics (family size, ages, gender). Using algorithms—just as Google or Amazon do—to direct market to consumers through enhancing data, using the available data with the addition of claims utilization, and external trends, including life expectancy and various healthy or non-healthy habits, will increase the ability of AI to guide HR decision-making.
This work often has involved use of external consultants or significant investment in employee time. As a result, businesses require what we’d call an “opportunity cost,” and this is often restrictive or prohibitive in the adoption of data analytics or business intelligence platforms. That’s where text analytics comes in.
With the advent of machine learning, text analytics has advanced to a level where it is capable of exploring large numbers of interrelated features, bringing structure and clarity to documents and data. Taking invoices as an example, companies such as Rossum.ai are able to remove the need for manual processing and extract the key information into a structured table. But consider applying similar techniques to contracts, spend data, and other usage data, and it becomes clear there could be a wealth of knowledge in analysing these datasets in combination; this is what VisionClerk does.
Performing the Analysis
When it comes to analytics, deep learning often is raised a potential solution to automatically extract meaningful patterns from large datasets for decision-making. However, the key here is truly defining and understanding the goals of your analysis. Pre-prescribed rules with specific logic and decisions are still invaluable in helping users uncover meaningful opportunities with a full understanding of where the information is coming from.
To make an informed decision, data is needed to back up your assumptions and understand the potential unintended consequences of taking actions.
- Creating total compensation options that include trading off benefit features: AI can be used to assist in attempting to quantify the operational impact of any benefit changes. Consider a total compensation package where employees can elect to reduce compensation or select alternate retirement plans or benefit plans in exchange for additional PTO. The impact on scheduling and resource allocations from an operational standpoint must be considered, including a matching of needed skill sets for certain operational activities. It may be a great idea to offer packages, but only if the business needs are fully considered and tested. Providing the support with data analysis will assist in making an informed decision.
- Testing enrollment patterns: If we are to move to offering these total compensation choices, using historical data and related patterns will be needed to more accurately reflect what may occur if these ideas are implemented. Absent this type of analysis, simply making assumptions regarding choices could prove dangerous. Of course, human nature may not be predictable—even with the use of algorithms.
- Using AI and data to become the employer of choice: Employers must act differently and stand out above the crowd in order to attract, retain, and motivate their employees. Even if incremental steps are taken to implement the concept of a total compensation offering, the data analytics surrounding the design will be instrumental in pointing to success or adjusting designs as current information is generated.
Total compensation is a view of all aspects of employer-provided benefits. The magic is how to analyze the total compensation in a meaningful way that meets the needs of employers and employees. C-suite executives can make a difference in the development of solutions to attract, retain, and motivate, employees today and for the future, and AI can assist in meeting the needed accomplishments.
Elliot Dinkin is president and CEO at Cowden Associates, Inc., specializing in helping corporate clients find the best solutions, both for the enterprise and its employees, with regard to compensation, healthcare benefits, retirement and pension issues, and Taft-Hartley fund consulting. Dinkin earned his MBA in Finance and Accounting from the University of Pittsburgh and a BA in Economics (Cum Laude) from Dickinson College.
