Businesses struggle with complex IT ecosystems. Facing increasing market pressures and constant change, they often add new solutions to solve problems. But over time, the complex systems used to automate and optimize business processes lead to significant technical debt (and management nightmares). IT orchestration helps businesses manage their IT seamlessly. It simplifies business processes, optimizes resource use, extends existing IT investments, and enhances efficiency.

In business, speed is key. When facing rapid change, adapting slowly eats into an organization’s revenue, opportunities, and market share. This has led organizations to deploy business process automation technologies to streamline processes, roll out products faster, and deliver services of a higher quality. However, organizations often stitch together disparate automation tools, leading to disjointed IT ecosystems and bloated licensing costs.

Developing, distributing, and monitoring a drug or medical device is an extensive process. It involves the coordination of people, systems, and substantial amounts of data. Production slowdowns negatively impact the product lifecycle, adding unnecessary costs and keeping valuable products from the patients who need them. The need for automation is evident.

Think about a time when insufficient information or inaccessible data slowed you down. Or maybe a time when your work required manual data entry that resulted in human error and rework, taking time away from higher value tasks. These moments of process frustration and inefficiency are exactly what workflow optimization helps address. Workflow optimization describes the strategies and measures taken to improve the flow of daily tasks and make processes more efficient.

In 24.2, we introduced our enterprise copilot. Enterprise copilot allows you to upload business documents and collect them in knowledge sets. From there, you can ask questions about information in these documents and receive answers quickly. For instance, an organization with a heavy regulatory burden could upload legislative and operational documents. Then, these employees could get insights from Appian AI Copilot to ensure they adhere to compliance requirements.

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.

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.

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.

Data fabric technology can help in a significant way, which is why it’s becoming a go-to method of choice for data management. Data fabric technology integrates information scattered across disparate sources and creates a unified data model through a virtual data layer. Data fabrics let you leave your existing data architecture intact, pulling directly from your current data sources and updating them in real time. So why is a data fabric approach the solution to your complex data challenges?