Artificial intelligence (AI) and machine learning (ML) are increasingly being integrated into workflow automation systems to improve decision-making and automate complex tasks. This integration can lead to significant benefits for businesses, including increased efficiency and productivity, improved decision-making, and reduced costs.

One of the key benefits of integrating AI and ML into workflow automation systems is improved decision-making. AI and ML algorithms can analyze large amounts of data, identify patterns and trends, and make predictions about future events. This can help businesses make more informed decisions and improve their overall performance.

Another benefit of integrating AI and ML into workflow automation systems is the ability to automate complex tasks. AI and ML algorithms can perform tasks that would otherwise be too time-consuming or difficult for humans to complete. This can lead to increased efficiency and productivity, as well as reduced costs for businesses.

How AI and ML enhance Workflow

One example of how AI and ML are being integrated into workflow automation systems is in the field of customer service. AI-powered chatbots can handle customer inquiries and complaints, freeing up human customer service representatives to focus on more complex tasks. This can lead to improved customer service and increased efficiency.

Another example is in finance, where AI and ML are being used to automate tasks such as fraud detection and financial analysis. This can lead to improved accuracy and faster decision-making, as well as reduced costs for businesses.

The integration of AI and ML into workflow automation systems can lead to significant benefits for businesses. It can improve decision-making, automate complex tasks, and increase efficiency and productivity. As these technologies continue to evolve, we can expect to see even more ways in which they are being used to improve workflow automation.

The current version of FlowWright already has many integrations with ML/AI services from Microsoft Azure and Google. For example, FlowWright uses Machine learning based translation to translate UI labels, forms, dashboard content to 60+ different languages. FlowWright also has AI/ML based services that can identify objects in a picture, describe images, recognize faces, or even recognize specific data from a specific region of a picture.

With FlowWright v10, We are integrating in more and more ML/AI based functionality into the core product. Here are some of the areas of FlowWright where innovation is happening:

• To be able to predict what step comes next – based on customer’s process data, FlowWright will be able to predict the next step or steps that should be used. As FlowWright is used more and more at Enterprise level, there are various departments using FlowWright to build and deploy automated processes. As these departments are disjointed from one another, its difficult to see what other departments are automating or how they are automating. In the next version of FlowWright, FlowWright will use all process data in a Machine learning trained model to predict what step should come next.
• Engine auto scaling – As FlowWright has always used worker processes to process workflows, it was always a set number. With the use of ML/AI functionality, FlowWright engines will auto scale based on load.

Above are some of the areas where ML/AI will be used, but there are many other areas within FlowWright ML/AI will be deployed, innovation is ongoing. If ML/AI is done right, it’s a great asset to our customers, our innovation is focused around adding functionality to our product that will ultimately add great value to our customers. 

Keep reading our blog for our latest tips to improve your business processes, and check out our case studies to see how our AI performs for other enterprises!