Luis Ibarra is chief technology officer at PingWind Inc.
In the era of increased automation and digitization, many work processes have been completely overhauled. Tasks that previously existed in analog format, or were done “by hand” or through a series of discrete steps, are now immediate, digitized, computerized and vastly more efficient.
There needs to be some clarity around workflow automation, particularly in the era of generative AI. While some automated tasks are performed via AI tools, not every automated workflow uses AI. Workflow automation refers to the design, execution, and automation of processes based on workflow rules. Various process tasks are passed from one workflow to another for action according to a set of procedural rules.
While AI can enhance workflow automation by adding capabilities like decision-making or prediction based on machine learning algorithms, many workflow automation systems are based on pre-defined rules and do not use any form of artificial intelligence. For example, basic workflow automation could be an email alert sent out when a new entry is made in a database. This basic workflow doesn’t require any AI—just a basic rule (when a new entry is added, send an email) that the system follows.
AI has been increasingly incorporated into workflow automation in recent years to create more sophisticated, adaptive, and efficient systems. Artificial intelligence can automate more complex tasks, make predictions, prioritize tasks, handle exceptions, and learn and improve over time. These are advanced features and unnecessary for basic workflow automation—but they are extremely important to understand as you move forward with more intelligence-based implementations.
For example, let’s take a hypothetical scenario where the Federal Emergency Management Agency (FEMA) responds to a natural disaster. Many people, resources, and supplies need to be dispersed, such as medical personnel arriving on the scene ready to assist people in need. These medics have an inventory of items they carry with them to accomplish tasks such as treating wounds or nascent infections. At a basic level, it is possible to build software to improve workflows for inventory management. Suppose you already know what every medical bag must have. In that case, you can make decisions regarding when inventory needs to be ordered to replenish each medical bag out in the field—a basic predefined rule of “inventory out, inventory in.”
Understanding, perfecting, and implementing a solid predefined rule provides the foundation for adding more complex and systematic interpretations to decision-making processes. In this scenario, FEMA would have complete visibility on the inventory of every medical bag out in the field and an automated process for replenishing those goods as medical bags get checked in or out during any natural disaster’s life cycle. Now, you can augment and improve the automated workflow by incorporating AI for more intelligent decision-making. Natural disasters are, by circumstance, chaotic. By adding geospatial mapping for medics to “pin” patients requiring assistance beyond inventory available to them in their medical bags, you can add detailed information on what is needed for the proper automated workflow decision to be executed. For instance, it could be as simple as low inventory for a given medic cueing the system to check current inventories on active medics and redirect the closest resource to help save a life. It could be as complex as injuries beyond the field medic’s capability triggering the system to redirect ambulatory services to the “pinned” location (with the details of the injury the responders are walking into).
Given the realm of possibilities that artificial intelligence can add to workflow automation for improving business processes across every industry, it’s not surprising that company heads are asking how they can be part of this trend that has been likened to a “gold rush,” with businesses prospecting for the promise of the digital gold that AI seems to offer. The allure of artificial intelligence in the business world is undeniable, with its promises of cost savings, improved operational efficiency, and the potential to open up entirely new markets or revenue streams. Moreover, a sense of urgency is driving businesses across various sectors to adopt AI hastily.
However, jumping blindly into the AI revolution without properly understanding its alignment with your business model can be counterproductive. As with any tool or technology, the value of AI is not inherently guaranteed—it is derived from the thoughtful application of the technology to suit specific, well-defined problems.
Digitizing or automating processes just for the sake of doing so can lead to inefficiencies, added complexities, and resource drain without adding substantial value. AI is a tool that thrives on specificity and needs to be applied where it can bring about the most significant impact. It requires an organizational culture shift towards data-driven decision-making.
When it comes to workflow automation, in order to avoid the pitfalls of an uninformed AI gold rush, businesses must first understand their basic predefined workflows. Ultimately, AI is a learning model—and having bad, ineffective, or inefficient baseline workflows means you will be teaching your AI implementation to be exactly those things. Understanding how AI fits into your workflow automation model involves awareness that not every workflow requires AI, as well as an understanding of the availability and quality of data your workflow uses, the existing technology stack, and the readiness of your organization to implement AI solutions.
While AI holds enormous potential for businesses, it is critical to temper the hype with strategic discernment. Only by understanding AI’s fit within your business model and leveraging its power towards specific, AI-driven goals can you unlock the true value of this technology. In doing so, you can ensure that the AI gold rush does not become your own internal, self-made AI bubble, leading to wasted investment and missed opportunities.
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