The life sciences industry is rapidly embracing artificial intelligence (AI) to automate complex processes, enhance efficiency, and improve outcomes. From drug development to patient care, AI-driven automation is transforming the landscape. Here are some of the top artificial intelligence use cases for process automation in the pharmaceutical industry.

Think about the monotonous task of extracting data from sources like spreadsheets and emails and typing it into various systems. Repetitive tasks and manual workflows lurking inside business processes slow down teams, introduce human error, and erode employee engagement. Slow, ineffective processes can also kill an organization’s time to market and customer satisfaction.

Medical affairs has grown to become a core pillar of the pharmaceutical industry, linking the work of research and development and commercial operations. However, for leaders in the industry, navigating the process and data complexities and communication silos involved can prove challenging.

AI can offer transformative business value. But you need the right combination of capabilities. Appian is continuing its history of providing practical value to enterprises across industries with the new AI capabilities in our 24.2 release—from 11 new generative AI skills that help you optimize mission-critical processes to the release of our Enterprise Copilot that gives users instant answers to their questions. This post will cover the latest enhancements to our AI offerings.

Local governments aim to serve the needs of their communities by fostering a safe, prosperous, and vibrant environment where residents and businesses can thrive. But too often, inefficient processes and manual, paper-based tasks consume valuable time and resources. This leads to delays, errors, and inconsistencies in the delivery of essential services, eroding citizen satisfaction and trust.

3. Elevate risk governance to the next level. NatWest is the UK's leading business bank and second-largest retail mortgage provider. Lengthy change implementation processes hindered risk governance at the bank. Initiating change projects or launching products and applications meant navigating layers of policy checks and approvals, which could take up to 73 days.

Customer experiences matter. And many businesses struggle to improve them. More often than not, a bad process is to blame: manual tasks, data spread across too many systems, or even just old ad hoc workflows that linger on because that’s how it’s always been done. But the cost of ignoring these issues is high.

The regulatory and business demands of financial services require teams to collaborate with consistency and accuracy—a difficult task when it comes to enterprise risk management (ERM). Business units often complete their risk management strategic objectives independently, and since each may have its own taxonomies and processes, human errors may go unnoticed, increasing potential risk.

IT leaders who want to scale operations to meet future business needs face an epic challenge—improving operational efficiency while managing risk, ever-growing IT backlogs, and stakeholder expectations. That's where process orchestration comes into play. Process orchestration is not just about efficiency and scale. It's a transformative approach that revolutionizes how work gets done. By automating routine tasks, it frees up your workforce to focus on strategic initiatives.

As generative AI grows in popularity and enterprises scramble to embrace new technology in a scalable, compliant way, it can be difficult to know the right path forward. Your enterprise has already invested in automating processes to free up resources and improve organizational efficiency, but is it enough? This is where intelligent process automation comes into play.