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.

AI is emerging as an important tool for meeting the mission at federal government agencies. It can add efficiency and aid decision-making by: Many government agencies are already using AI processes. Below are some important use cases for AI in federal government: And that’s just a small sample of the ways federal agencies are using AI. Thousands more use cases have been identified related to national security, healthcare, transportation, and more.

The insurance industry is undergoing a profound digital shift. To keep up, insurers need a connected insurance platform that not only meets the demands of today but anticipates the needs of tomorrow. For many leading insurers, Appian is that platform. This blog walks you through how Appian equips our partners with the insights and tools they need to amplify their success in the Insurance industry.

In the rapidly emerging artificial intelligence economy, organizations will split into two groups: those who are good at AI and those who are bad at business. Most experts agree that AI won’t replace humans, but instead augment us in a world of mixed autonomy. You’ll need new structures to harness AI’s transformative potential while managing its very real risks—the biggest of which is data privacy. So how can leaders handle AI and privacy risks?

The human brain is a prediction machine. It sees patterns, then makes predictions from previous experiences. This part of human intelligence has been critical to our survival. For example, many years ago, a forager might have eaten a particular berry, gotten sick, and thus learned the clues that indicate that a berry is poisonous. This would happen automatically—we’d get nauseous when seeing the berry again, which would make us steer clear.

Ready or not, AI workflow automation is poised to transform business operations from the shop floor to the C-suite in the AI economy. The situation is a byproduct of the generative AI boom. And yet, so many companies have hardly scratched the surface of AI automation’s full potential in their business operations. The more these companies get AI workflow automation to work for them, the more agility, efficiency, productivity, and economies of scale they’ll get back for themselves.

For business and IT leaders, digital transformation in the financial services industry continues to pose a challenge but is necessary to keep pace with changing regulatory requirements, consumer demand, and budget constraints. It's also important to prevent operational areas from growing their expenses unintentionally. In the typical boom and bust cycle, banks can feel pressure to roll out new financial products, services, and business lines.

Casework is an intricate process that involves the assembly and management of a myriad of files, tasks, and personnel. At its core, casework aims to build a comprehensive narrative or profile, drawing together disparate pieces of information to construct a cohesive whole. The complexity of case management arises from the diverse data sources and numerous stakeholders involved in the process.