Workforce planning tools are next in line to benefit from AI and machine learning — and it all starts with staff scheduling.
Artificial intelligence (AI) and machine learning have both generated significant capital. VentureBeat notes that in 2016, AI businesses raised more than $1.5 billion to develop new technologies and bring new products to market. For finance leaders, it's easy to see AI and machine learning as useful solutions for other departments, but increasingly sophisticated offerings suggest the rise of workforce planning tools ready to tackle HR's toughest challenges — such as scheduling — and help improve financial decision-making.
While subtle differences exist between AI and machine learning, for the purpose of financial decision-making and staff scheduling they're effectively synonymous. They leverage intelligent, autonomous processes to help identify the best course of action using available data rather than human input. There's a growing market for these tools. The Wall Street Journal (WSJ) notes that the workforce management software market is now worth more than $11 billion.
There's more to the story than cost savings and market hype. WSJ notes that "computers may be better suited to some managerial tasks than people are" in part because humans are subject to confirmation bias and lack the sheer processing power to make decisions based on massive data sets. VentureBeat confirms that half of executives don't believe their current workforce performance management approach is effective in "driving employee engagement or high performance." Machine learning can help remove unconscious biases, improve recruiting efforts and reduce the amount of time spent managing complex staff schedules.
The AI Advantage
Despite a growing market for workforce planning tools empowered by AI, eLearningInstitute reports that 57 percent of managers say they knew "very little about AI/ML and how it could change the way they do business." It makes sense — development of artificial intelligence often focuses on proof-of-concept over functional use cases, making it difficult to visualize downstream impacts.
Consider the case for AI-driven scheduling. As noted by Iot For All, organizations are now leveraging AI tools for inbound logistics planning, using data-driven machine learning models to ensure the right supplies are in the right place at the right time. HR scheduling operates on the same principle — the right number of staff in the right place at the right time. But scheduling staff can often be more time-consuming and complicated than managing material supply and demand because human employees have changing needs, health concerns and modifiers to their availability, which includes amount of paid time off already used, corporate seniority and previous request history.
According to Harvard Business Review, the most tiresome and onerous task required of staff (which also fell outside primary work duties) was scheduling meetings. Simple requests quickly devolved into complex negotiations taking up far more time and effort than the meeting itself. But using AI tools, HBR reports the team was able to create a virtual assistant that could schedule meetings based on existing data and by reaching out to staff directly via email.
AI in Workforce Planning Tools
If it works for meeting schedules, there's no reason AI can't do the same for staff paid time off (PTO) and other leave requests. Cloud-based tools are now building in the capability to leverage AI for this purpose; the end goal allows automated systems to analyze all incoming PTO requests and then generate a "bundle" of potential actions based on factors such as availability, seniority and skill sets — and then implement decisions with the click of a button by management. For finance managers, this means less time wasted with back-and-forth emails about attendance records and preferences, which translates to reduced spend on staff and C-suite busywork.
It's not hard to extrapolate the next step in AI evolution: Improved financial decision-making based on historical data, current information and future predictions. From hiring new staff to trimming current workforce levels, intelligent processes could both reduce the potential for managerial missteps while bolstering long-term budgets. AI is coming to workforce planning tools. Finance leaders should consider clearing their schedules.
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