Why AI-optimized workflows aren’t always better for business

Check all sessions on demand from Summit Smart Security here.


The cost of workflow and inefficiency of operations may reach 40% of the company’s annual revenue. In many cases, companies seek to solve this problem through implementation artificial intelligence (AI) scheduling algorithms. This is seen as a useful tool for business models that rely 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 employees across departments, the model is not yet perfect. Sometimes, it makes problems worse, not better.

AI lacks the human capacity to look beyond simply improving business efficiency. This means that it has no power over “human” variables such as workers’ preferences. AI scheduling limitations often lead to unbalanced shifts or unhappy employees, culminating in situations where the AI ​​”help” given to HR gets in the way of a smooth workflow.

When optimization goes wrong: The AI ​​can’t see the humans behind the data points

AI automatic scheduling has gained a lot of popularity in recent years. Between 2022 and 2027, the global Artificial Intelligence Scheduling System market is expected to witness a A compound annual growth rate of 13.5%And 77% of comp Already using AI or seeking to add AI tools to improve workflows and improve business processes.

It happened

The pinnacle of smart security on demand

Learn about the critical role of AI and machine learning in cybersecurity and industry-specific case studies. Watch sessions on demand today.

Watch here

However, it is important to note that AI cannot yet make schedules without human supervision. HR professionals still need to review and adjust automatically generated tables because there is still one big and obvious flaw in AI algorithms: the lack of “human parameters”.

AI is excellent at sorting through data and finding ways to increase efficiency in business processes. Workflow optimization via algorithms that use historical data is ideal for projecting things like order size 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 the AI’s inability to interpret “human parameters”, which it views as a decrease in efficiency rather than better work practices.

For example, if a company has observant Muslim employees, they need small breaks in their work days to observe prayer times. If the company is hiring new moms, it may also need built-in times for pumping the breastmilk. These are things that are currently beyond the AI’s capabilities to correctly interpret, because it cannot use human empathy and reasoning to see that these “inefficient schedules” are more efficient from the perspective of employee happiness in the long run.

Efficiency is not always the best policy; Is there any solution?

Currently, automatic scheduling tools can only pull data points from limited sources, such as timesheets and workflow logs, to evenly distribute work hours in what they consider to be the optimal method. AI scheduling tools need to help understand why it’s bad for the same employee to work the closing shift one day and then back for the opening shift the next. Nor can it yet account for individual worker preferences or miscellaneous availability.

One possible solution to this problem is to continue adding parameters to the algorithms, but this presents its own problems. First, every time you enter a new parameter, it makes the algorithm less likely to perform well. Secondly, algorithms only work with the data that is given. If AI tools are fed incomplete, incorrect, or inaccurate data, scheduling can hinder workflow efficiency and create more work for managers or HR staff. Adding more filters or restrictions will not help the algorithm to work better.

So what is the solution? Unfortunately, until we discover ways to infuse AI with empathic reasoning capabilities, there will likely always be a need for humans to have a hand in staff scheduling.

However, companies can work to create a more positive and synergistic relationship between AI scheduling tools and the humans who use them.

For example, delivery companies can feed historical data into AI tools to more effectively leverage initial schedule outputs. This reduces some of the burden on HR and scheduling managers. In contrast, the human scheduler now has an improved base schedule to work from, so they can spend less time fitting workers into their required time slots.

AI may be perfectly functional, but it still needs human assistance to keep employees happy

Humanity is still working hard The development of artificial intelligence who displaysGeneral Intelligence, a term applied to the visual intelligence of humans and animals. It combines problem-solving with passion and common sense, two things that have not been replicated in artificial intelligence.

When you need to automate repetitive tasks or analyze huge amounts of data to find inefficiencies and better working methods, AI outperforms humans almost every time. However, once you add nuance, emotion, or general intelligence, as with task scheduling, humans will still need to have the final say to balance improved workflow, employee satisfaction, and long-term company growth.

Vitaly Alexandrov He is a serial entrepreneur, founder and CEO of a company Food Rocketan express grocery delivery service in the United States.

Decision-makers

Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people who do data work, can share data-related insights and innovations.

If you want to read about cutting-edge ideas, updated information, best practices, and the future of data and data technology, join us at DataDecisionMakers.

You can even think Contribute an article Your own!

Read more from DataDecisionMakers

Leave a Comment