Ready or not, AI workflow automation is poised to transform business operations from the shop floor to the C-suite in the AI economy. As organizations embrace digital-first initiatives, IT teams will be able to do much more with less. 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 modern data-driven approach comes with a host of benefits. A few major ones include better insights, more informed decision-making, and less reliance on guesswork. However, some undesirable scenarios can occur in the process of generating, accumulating, and analyzing data. One such scenario involves organizational data scattered across multiple storage locations. In such instances, each department’s data often ends up siloed and largely unusable by other teams.

With organizations prioritizing data-driven decision-making, the amount of collected and stored data is reaching historic highs. Meanwhile, organizations are democratizing access across all functions to convert this data into actionable insights. Since more users will work with sensitive data, ensuring secure access is more important than ever. Organizations must regulate and maintain the relationship between their data assets and users. Why?

Process orchestration, also called business process orchestration, is an approach to manage and organize multiple automated tasks or workflows within a business process, like ETL process orchestration. It involves coordinating data extraction, transformation, and loading tasks across various systems to create a unified data source.

Data modernization transforms an organization’s data into more usable and accessible formats, making it usable by modern applications and systems, such as cloud-native applications, IoT devices, and mobile applications. Data modernization also includes extracting, cleaning, and migrating the data into advanced platforms.

In the fast-paced world of government software development, efficiency and effectiveness are paramount. One way government organizations enhance agility, foster innovation, and optimize resources in the public sector is through citizen development—the practice of allowing non-technical employees to create their own software applications using low-code or no-code platforms.

Do you know proactive fraud detection and prevention could save organizations up to a staggering $5.9 billion annually? However, gathering relevant data for this purpose is no easy task. Financial data integration plays a crucial role in the fight against fraud, enabling organizations to merge data from various sources and formats into a unified, consistent view. However, gathering relevant data for this purpose is no easy task.

In the pharmaceutical industry, an effective supply chain is critical, not only for operations but also for ensuring the safety and quality of products going out to market. The complex nature of pharmaceutical supply chains, combined with their stringent regulatory requirements, makes risk management a top priority.

Automation has unleashed unprecedented productivity and profits for organizations. By automating formerly manual tasks, businesses see efficiency gains, reduced human error, cost savings, and improved innovation. Automation frees employees for creative work and work that requires human intervention. To facilitate this, many organizations have stitched together a wide range of automation tools. This includes robotic process automation (RPA), generative AI, predictive AI, and business process management.

Business analytics is analyzing data to find insights that inform business decisions. Fundamentally, it involves applying data analytics tools and techniques to a business setting to simplify decision-making and improve business outcomes. Often shortened to BA, business analytics includes: With the right tools and techniques, enterprises can turn raw data into actionable insights, driving strategic decision-making and sustainable growth.