Inefficient operational processes often lead to bottlenecks, delays, and increased costs, hindering a company's ability to respond swiftly to changing market demands and optimize its supply chain management. Enter Business Process Modeling and Notation (BPMN), a powerful visual language that helps businesses enhance operational efficiency and streamline complex processes.

Artificial intelligence has been instrumental in helping businesses streamline and speed up business operations by automating repetitive tasks. It also frees up workers to focus on higher-level tasks that require human intervention. And when paired with other intelligent automation tools, such as robotic process automation, it can improve business processes and provide a competitive advantage.

Generating files for data extracts, reporting and even data migration is a crucial part of any back-end team. A typical data team will have to generate many files to be sent to various clients or consumers inside or outside of the organisation. As with all things, the question is how can the generation of those files be made simpler, and can it be automated to such a degree that files are dynamically generated? The short answer is yes, it can be done dynamically.

Project management across multiple teams, especially under the triple-constraints of time, cost, and scope, is an intensive task. Project managers have to cover tons of ground to keep things on track. For example, they might manage allocating resources based on availability, assigning tasks, controlling execution, tracking and reporting progress, and forecasting future trends.

Effective quality management in the life sciences industry is essential for ensuring patient safety, regulatory compliance, and the delivery of high-quality, efficacious treatments and medical devices. And good quality management is about more than just checking a regulatory box. Ensuring the safety and efficacy of your products can prove challenging, but the right technology can help.

Automating a process can be complex, especially when it’s a long-running process with many steps and complex business rules, cross-cutting multiple departments and systems in an enterprise. To automate a process like this from end to end, you’ll need a process automation strategy and software. Assuming you’ve ticked both of those boxes, the next step is putting those things to work, which is the focus of this post.

In a world drowning in documents, intelligent document processing (IDP) is a lifeboat. Imagine freeing yourself and your teams from the frustration of manual data entry (and the human error that always comes with it). This is the promise of IDP—a transformative force for any kind of document processing work.

We've all been involved in automated processes in companies, governments, or schools that might have made sense in theory—but fell apart when they were rolled out. That’s because automating a process is different from automating a process well. Developing a process automation strategy helps you automate effectively—but only if it includes these three often-overlooked elements.

AI automation involves leveraging artificial intelligence (AI) to automate repetitive tasks within operational workflows, thereby enhancing efficiency and productivity. This not only streamlines routine tasks within core business processes but also enables companies to scale growth efficiently by integrating a broad range of digital tools such as robotic process automation, APIs, and software robots.

For the banking and financial services industry, artificial intelligence (AI) isn't just a new tech trend. It's a powerful tool that will have a wide range of impacts, from risk management to operational efficiency and customer experience. According to Deloitte, the world’s top 14 investment banks could potentially boost their front-office productivity by 27% to 35% by leveraging generative AI.