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New AI products are constantly coming to market with promises to revolutionize some aspects of your business and save you time and, ultimately, money. It’s an exciting time, full of promise, but it’s important to sift through the hype and take a hard look at whether the benefits justify the costs.
Take workforce data analytics. Employee dissatisfaction and disengagement, especially among younger workers, have been a hot topic since the pandemic. It’s a critical issue, but many business owners are unaware of just how costly employee turnover can be. A median-size S&P 500 company can lose between $228 million and $355 million a year in lost productivity from employee disengagement and attrition, according to McKinsey research.
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Even when companies acknowledge they have a problem, they often create interventions to address the issue with little more than guesswork. AI gives businesses the opportunity to analyze their workforce issues more affordably than hiring a pricey consulting firm. AI data analytics tools can now predict the precise cost of employee turnover, identify the causes and offer data-driven solutions to prevent it.
Just because the technology exists, however, doesn’t mean your company will automatically benefit. You should vet decisions on whether to deploy AI solutions using the same rigorous cost-benefit analysis you use in every other aspect of your business.
Below are six questions to ask yourself before you commit:
- How many employees do I have? AI workforce analytics typically only starts to pay off once your company has more than 50 employees. That’s because it takes resources to collect and structure the data, and it’s at the larger numbers that analytics become complex enough to justify the costs.
- What kind of data am I already collecting? For predictive workforce AI analytics to work, your company needs to be collecting a lot of data already, preferably using employee management software. Useful data include employee schedule adherence and variability, employee utilization, sentiment around feedback reviews, employee skill sets, overtime hours and overtime pay.
- What’s my free cash flow budget to apply to R&D? Even if you’re collecting a lot of data, you still need a robust pipeline to structure the data, and that can mean high upfront costs. Simple descriptive AI tools won’t require as much investment but also won’t deliver the same predictive insights. Be sure you know precisely what your AI tool is offering and what you will need to spend to make those insights pay off for you in the long run.
- What outside data does my AI tool crunch? A strong predictive AI tool will combine your internal company data with external data affecting employee satisfaction — right down to traffic patterns on workers’ commutes. Ask questions at the start. What data does my AI tool bring to the table that I can’t access on my own?
- Are my current workforce retention strategies working? If you’ve already tried to tackle an employee retention problem, do you have data to back up the effectiveness of interventions? Or are you flying blind? A good workforce data analytics firm can use causal analysis to determine whether you’re wasting money on solutions that don’t get to the root of the problem.
- What’s my ROI? You need to calculate the cost of employee attrition at your company, the cost savings from implementing changes to help you retain top talent, minus the expense of implementing AI data analytics. How does it compare to the expense of a consulting firm? A good workforce data analytics company can help you determine whether it’s worth the investment, and an honest one will tell you when it’s not.
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AI workforce analytics tools have incredible potential. They can identify which employees are planning to leave your company — before they even know. New tools give small and mid-size businesses access to information and insights that were impossible to come by in the past. Still, it’s wise to be cautious and to make sure the investment will pay off for your business in the long run.
