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Inefficient workflows and processes can cause damage up to 40% annual revenue of a company. In many cases, companies seek to solve this problem by implementing Artificial Intelligence (AI) scheduling algorithms. It is considered a useful 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 involved in scheduling employees between departments, the model is far from perfect. Sometimes, it makes matters worse and not better.
AI lacks the human ability to see beyond optimizing business performance. That means it’s not possible for “human” variables like worker preferences. The limitations of AI scheduling can often lead to unbalanced shifts or unsatisfactory workers, culminating in situations where AI “help” is available to the department. HR really hinders a smooth workflow.
When optimization crashes: AI can’t see humans behind data points
Automated scheduling AI has gained popularity in recent years. From 2022 to 2027, the global AI scheduling system market is expected to witness a boom CAGR is 13.5%and 77% of companies are using AI or looking to add AI tools to optimize workflows and improve business processes.
However, it is important to note that AI cannot yet schedule without human supervision. HR professionals still need to review and adjust auto-generated schedules because there is still a big, glaring flaw in the AI algorithm: Lack of “people parameters.”
AI is excellent at classifying data and finding ways to maximize efficiency in business processes. Workflow optimization through algorithms that use historical data is ideal for predicting things like order volume and required number of workers, based on information like marketing promotions, weather patterns, time of day, hourly order estimates, and average customer wait times.
The problem stems from AI’s inability to take into account “human parameters,” which it deems to reduce efficiency rather than better business practices.
For example, if a company has Muslim employees, they need to take small breaks during the workday to adhere to prayer times. If a business employs new mothers, they may also need to have time available to express breast milk. These are things that are currently beyond AI’s ability to properly explain, because it can’t use human empathy and reasoning to see that this “inefficient schedule” is much more effective from the outside. employee long-term happiness.
Efficiency is not always the best policy; have a solution?
Currently, automated schedulers can only pull data points from limited sources, like timesheets and workflow history, to evenly distribute work hours in a way that the tool considered optimal. AI schedulers need help understanding why it’s so bad to have the same employee work the last shift one day and then return to the open shift the next day. Nor have they been able to account for individual worker preferences or different availability situations.
One possible solution to this problem is to keep adding parameters to the algorithm, but that introduces its own problems. First, every time you introduce a new parameter, it reduces the likelihood that the algorithm will perform well. Second, algorithms only work well with the data they are fed. If AI tools are fed incomplete, inaccurate, or inaccurate data, scheduling can hinder workflow efficiency and create more work for managers or employees. personnel staff. Adding more filters or limits to the algorithm will not help the algorithm perform better.
So what’s the solution? Unfortunately, until we discover ways to infuse AI with empathic reasoning, it’s likely that humans will always need to be involved in employee scheduling.
However, companies can work towards creating a more positive, synergistic relationship between AI schedulers and the people who use them.
For instance, delivery companies can feed historical data into AI tools to increase the efficiency of their originally scheduled outputs. This relieves some of the burden on HR managers and planners. In turn, human schedulers now have baseline schedules optimized for work, so they can spend less time adjusting employees to needed intervals.
AI can be completely effective, but it still needs human help to keep employees happy
Humanity is still working hard AI development display “general intelligence,” is a term that applies to the intelligence seen in humans and animals. It combines problem-solving with emotion and common sense, two things not yet replicated in AI.
When you need to automate repetitive tasks or analyze huge amounts of data to find inefficiencies and better work methods, AI almost always outperforms humans. However, even when you add nuance, emotion, or general intelligence, and schedule tasks, humans will still need the final say to balance optimized workflows with employee satisfaction and the long-term growth of the company.
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