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Workflow and process inefficiencies can cost up to 40% of a company’s annual revenue. In many instances, companies seek to resolve this issue by implementing Artificial Intelligence (AI) scheduling algorithms. This is seen as a beneficial tool for business models that depend on speed and efficiency, such as delivery services and the logistics sector.
While AI has certainly helped with some of the time-consuming and often unpredictable tasks associated with scheduling workers across departments, the model is not yet perfect. Sometimes, it makes the problems worse and not better.
AI lacks the human ability to look beyond simply optimizing for business efficiency. That means it has no capacity for “human” variables like workers’ preferences. The limitations of AI scheduling can often lead to unbalanced shifts or unhappy workers, culminating in situations where the AI “help” given to HR actually gets in the way of smooth workflows.
When optimization goes wrong: AI can’t see humans behind the data points
Auto-scheduling AI has gained a lot of popularity in recent years. Between 2022 and 2027, the global AI scheduling system market is expected to see a CAGR of 13.5%, and 77% of companies are either already using AI or seeking to add AI tools to optimize workflows and improve business processes.
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However, it’s important to note that AI cannot yet make schedules without human oversight. HR professionals still need to review and adjust automatically generated schedules because there is still a huge, glaring flaw in the AI algorithms: A lack of “human parameters.”
AI is excellent at sorting through data and finding ways to maximize efficiency in business processes. Workflow optimization via algorithms that use historical data is ideal for projecting things like order volume and the required number of workers, based on information such as marketing promotions, weather patterns, time of day, hourly order estimates and average customer wait times.
The problem stems from AI’s inability to account for “human parameters,” which it perceives as drops in efficiency rather than better business practices.
For example, if a company has observant Muslim employees, they need small breaks in their workdays to observe prayer times. If a business employs new mothers, they may also need built-in times to pump breastmilk. These are things that are currently beyond AI’s capabilities to properly account for, because it cannot use empathy and human reasoning to see that these “inefficient schedules” are much more efficient from a long-term employee happiness perspective.
Efficiency isn’t always the best policy; is there a solution?
Currently, auto-scheduling tools can only pull data points from limited sources, like timesheets and workflow histories, to evenly distribute work hours in what it deems is the optimal way. AI scheduling tools need help understanding why it’s bad to have the same employee work the closing shift one day and then return for the opening shift the next day. They also can’t yet account for individual worker preferences or varied availabilities.
One possible solution to this problem is to keep adding parameters to the algorithms, but that presents its own problems. First, every time you introduce a new parameter, it decreases the likelihood that the algorithm will perform well. Second, algorithms only work as well as the data they are given. If AI tools are provided with incomplete, incorrect or imprecise data, the scheduling can hinder workflow efficiency and create more work for managers or HR employees. Adding more filters or limitations to the algorithm won’t help it work better.
So what is the solution? Unfortunately, until we discover ways to infuse AI with empathetic reasoning capabilities, there will likely always be a need for humans to have a hand in scheduling workers.
Nonetheless, companies can work toward creating a more positive, synergistic relationship between AI scheduling tools and the humans who use them.
For instance, delivery companies can feed historical data into AI tools to increase the effectiveness of their initial schedule outputs. This reduces some of the burden for HR and scheduling managers. In turn, the human scheduler now has an optimized base schedule to work from, so they can spend less time fitting workers into the needed time slots.
AI might be perfectly efficient, but it still needs human help to make employees happy
Humanity is still working hard on developing AI that exhibits “general intelligence,” which is a term applied to the intelligence seen in humans and animals. It combines problem-solving with emotion and common sense, two things yet to be replicated in AI.
When you need to automate repetitive tasks or analyze massive amounts of data to find inefficiencies and better work methods, AI outshines humans nearly every time. However, as soon as you add nuance, emotion or general intelligence, as with scheduling tasks, humans will still need to have the final say to balance optimized workflows with employee satisfaction and long-term company growth.
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