Due to market pressures and disruptions, life sciences organizations need to reexamine their supply chain operations and how to more safely and efficiently bring products to market. However, deciding where to begin implementing change can be daunting. Here are some best practices for achieving streamlined end-to-end supply chain orchestration.

It’s 3 a.m., and your system just went down—again. That’s a nightmare scenario for any IT leader, whether due to a technical glitch or a security incident. This is exactly why incident case management matters. Think of it as air traffic control for your IT environment. Planes in the sky, tickets in the system—both must land safely. Incident case management is about tracking and managing those tickets—incidents—to ensure they get resolved swiftly.

As automation capabilities grow, so do the related use cases. It’s not surprising that 80% of executives think automation can be applied to any business decision, according to a Gartner survey. But as implementation becomes easier and automation adoption more prevalent, your organization can quickly lose control, with too many automation projects happening in silos.

If you think workflow orchestration sounds like tech jargon, check out this simple explanation and consider practical advice for how to apply it to your business processes. Let’s start with the basics: what is workflow orchestration? Put simply, workflow orchestration is the end-to-end management of people, digital workers, systems, and data in a process.

The magic of a data fabric architecture lies in its ability to unify data access and integration, enable real-time analytics, enhance governance and security, and boost operational efficiency. It’s not just a tool; it’s a game-changer. For IT leaders in this age of acceleration, understanding the top use cases of data fabric can mean the difference between winning and losing the race for customers and market share.

Integrating artificial intelligence (AI) into business operations is no longer optional—it’s necessary. Yet, too often, businesses fail to reap the full rewards. AI can’t produce the results that impress stakeholders and drive tangible results unless you take a strategic approach to its deployment.

In today's financial services landscape, the need for speed is paramount. Traditional financial processes are no longer sufficient to meet the demands of modern consumers and businesses. The synergy of three emerging technologies promises to expedite financial services processes: By implementing a data fabric, financial institutions can break down silos, enabling data to flow freely across the organization.

Generative AI has received the lion’s share of the press. With good reason—it’s revolutionizing the way we do work and do business. But it’s not the only game in town. Predictive AI also places a role across enterprise use cases like demand forecasting, maintenance, and customer experience. This blog will discuss these two types of AI: generative AI and predictive AI.

Trying to integrate data without knowing your starting point is like taking a road trip without a map—you’re bound to get lost. To navigate the challenges of data integration, IT leaders must first evaluate their current data setup. This means taking stock of all your data sources, understanding their quality, and identifying integration points. It’s like conducting a thorough inspection before renovating a house; you must know what you’re working with.

Data is the lifeblood of businesses. But the vast amount of data businesses accumulate makes it difficult to turn that data into actionable insights. Enterprise intelligence solutions offer a system for collecting, managing, analyzing, and monitoring your process and business data. A good enterprise intelligence solution empowers organizations to make informed, data-driven decisions, enhance operational efficiency, and maintain a competitive advantage.