Why business-friendly AI-optimized workflows aren’t always the best option

Mismanagement of workflow and ineffective processes may waste up to 40% of a company’s yearly income. Many businesses have turned to AI scheduling algorithms as a means of addressing this problem. This is a useful tool for industries like delivery services and logistics that rely on swift and efficient operations.

Using AI has helped with the labor-intensive and erratic chores of scheduling employees across departments, but the model is far from flawless. Sometimes this approach actually makes matters worse.

Artificial intelligence is limited in its capacity to think creatively because of its narrow focus. This implies that “human” factors, such as the preferences of employees, are completely outside of its scope. When AI is used for scheduling, it may sometimes result in unfair shifts or dissatisfied employees, creating scenarios in which the “assistance” supplied to HR by AI is ultimately counterproductive.

Whenever optimization fails, AI is blind to the people behind the numbers

The use of artificial intelligence for auto-scheduling has skyrocketed in recent years. Seventy-seven percent of businesses are either currently using AI or looking to add AI tools to optimise workflows and enhance business processes, and the market for AI scheduling systems is predicted to grow at a CAGR of 13.5 percent between 2022 and 2027.

Though AI has come a long way, it still needs help from humans to make decisions like scheduling. Due to a major shortcoming in AI algorithms—a lack of “human parameters”—HR experts still need to examine and alter automatically created schedules.

AI excels at sifting through information and figuring out how to improve productivity in the workplace. Algorithms that optimise workflow based on past data are particularly useful for forecasting variables like order volume and the number of personnel needed in light of factors like marketing campaigns, weather patterns, time of day, estimated order volumes per hour, and typical customer wait times.

The issue arises from AI’s failure to take into account “human parameters,” which it interprets as decreases in efficiency rather than improved business practises.

Observant Muslims, for instance, need brief pauses throughout the workplace in order to pray. In the event that a company decides to hire new moms, it may be necessary to provide them with dedicated pumping breaks. Currently, AI is unable to take these into consideration since it lacks the capacity for empathy and human reasoning, making it blind to the fact that ostensibly “inefficient schedules” are really more efficient from the standpoint of employee pleasure mis the long run.

Is there a way to avoid the pitfalls that arise when efficiency is prioritised above other factors?

Auto-scheduling software can only use data from certain sources like timesheets and process records to allocate employees’ time effectively. Automation scheduling systems require human explanations of why it’s not a good idea to have the same person perform both the closing and opening shifts on consecutive days. They also can’t take into consideration the preferences and schedules of individual employees.

Adding more and more parameters to the algorithms is one approach, but it comes with its own issues. To begin, the algorithm’s chances of success are diminished with each additional parameter that is included. Secondly, the quality of the input data determines the quality of the results produced by an algorithm. Lack of comprehensive, accurate, or exact data supplied to AI technologies may lead to inefficient scheduling, which in turn adds extra work for managers and HR personnel. Adding extra constraints to the algorithm will not make it more efficient.

The question now is, what should we do about it? There will likely always be a need for humans to have a role in scheduling employees until we find methods to imbue AI with empathic reasoning skills.

But businesses can improve the interaction between AI scheduling tools and their human users by fostering a more collaborative environment.

For instance, logistics firms may use past data inputted into AI scheduling technologies to further refine future schedule outputs. Human resources and scheduling directors will like this change. Therefore, the human scheduler may spend less time trying to fit employees into available time slots since they have an optimum basis schedule to work with.

Even if AI were entirely efficient, it would still require human assistance to ensure the satisfaction of its workers.

The phrase “general intelligence,” used to describe the intellect shown by humans and other animals, is still in the works as a goal for AI development. Incorporating both rational and intuitive approaches to addressing problems, it is a step forward in the development of artificial intelligence.

AI is much superior to humans if there is a requirement to automate repetitive jobs or analyse large volumes of data in search of inefficiencies and improved work techniques. Humans will still need to have the last word to strike a balance between streamlined workflows and employee happiness and long-term corporate success as soon as you add subtlety, emotion, or general intelligence, as with scheduling assignments.