In today's hyperconnected world, where negative reviews on social media can wreak havoc on a company’s reputation, delivering an exceptional customer experience isn't just a luxury—it's a business imperative. Companies are locked in a fierce battle for customers that is primarily based on their ability to deliver outstanding customer experiences (CX). According to research by The Conference Board, 65% of CEOs globally prioritize investing in strategies to improve CX.

Artificial intelligence (AI) was a major topic at Appian Government 2024, the premier event for public sector digital transformation leaders and mission owners. Most AI products are created on the West Coast, with commercial customers in mind. But commercial enterprises and government organizations differ in many ways. Important issues that affect the federal government’s approach to AI include: For 25 years, Appian has focused on serving public sector organizations.

Digital evidence plays a pivotal role in roughly 80% of criminal investigations. From text messages and emails to social media posts and GPS data, digital evidence offers law enforcement insights that majorly impact police investigations. Smartphones, mobile devices, computers, surveillance cameras, and other digital devices have become ubiquitous in daily life. Digital footprints are now critical to establishing timelines, tracking movements, and identifying suspects.

If you're worried about the potential for heightened regulatory scrutiny in financial services, you're not alone. Business operations teams everywhere are focused on the end-to-end, client lifecycle management (CLM) process as they cope with ever-changing regulations governing how, when, and where client data can be stored and accessed. It's hard to stay compliant when customer data is spread across multiple operational silos.

The process of preparing and submitting labeling to regulatory authorities can be time-consuming and complex, requiring specialists’ input and careful coordination across organizations and functions to ensure the quality and safety of products going to market. This often causes delays that ripple across the supply chain, impacting profitability and patient care.

Organizations must constantly seek ways to improve their operations and drive efficiency. Two concepts gaining traction in this quest are process intelligence and process mining. While these terms may sound similar, they serve distinct purposes and can provide different insights into your processes. Understanding the differences between them is crucial for organizations aiming to enhance productivity and streamline workflows.

In the insurance industry, the claims process can often feel like a complex maze of manual processes for both customers and agents. However, new and advanced technologies are paving the way for smoother, more efficient claims handling. Claims processing automation at insurance companies can streamline routine tasks, reduce risk of errors, and enhance customer satisfaction. In this blog, we’ll explore three key areas to focus on for effective automation.

ERP systems are critical in government agency operations for integrating internal and external management information. They are intended to optimize processes, improve collaboration, and connect with customers, vendors, and partners. Government ERP systems typically include modules related to financial management, human resources, sales, procurement, inventory, and operations like contract and project management.

When AI first started to gain widespread adoption, it sparked a wave of fear. While much of that fear was overblown, we still need to remain cautious about any new technological innovation. Given AI’s potential to drive change on a massive scale, applying ethical principles to AI is not just important, but urgent. Every company must prioritize responsible AI—not only as an ethical responsibility but as a practical, strategic choice.

Many vendors have started calling their platforms “low-code” or “no-code.” Competitors often pit the terms against each other. But what do low-code and no-code really mean? And what's the difference between the two? Both platforms offer improvements over traditional high-code approaches. The biggest differences are the target user groups.