In government agencies around the world, large enterprise legacy systems are what stand in the way of desperately needed modernization programs. Such legacy systems can be large enterprise resource planning (ERP) implementations or custom-built applications using complex codebases. Simply put, they are not supportable, upgradable, and do not provide the rich user experience customers have become accustomed to.

This post was originally published in June 2023 and has been updated for comprehensiveness. Steve Jobs famously remarked that the way to boost programmer productivity is to create apps with less code. The market has finally caught up with his vision.

When we talk about technology, we should try to do it in the simplest possible terms. But while we hear many CEOs and other tech executives preach this advice, it’s rarely followed—and the workflow software space is no exception. Popular workflow lingo like hyperautomation, digital process automation, and intelligent automation all mean essentially the same thing: to digitize processes for efficiency, consistency, and compliance.

Today’s policyholders expect fast and reliable insurance service tailored to their needs. Claims organizations need to accurately and efficiently manage claims while balancing the growing demand for speed and reliability. However, operational bottlenecks and lack of business agility continue to delay claims resolutions and increase costs. Leading insurance companies are turning to process automation to improve customer satisfaction and employee experience.

What are some common AI/ML use cases? What use cases should business and IT leaders track as they shape a holistic process automation strategy? Let’s find out. Artificial intelligence (AI) refers to computer systems that mimic human thought and decision-making. Within AI, deep learning and machine learning play crucial roles. Deep learning uses neural networks to process large amounts of data and uncover patterns.

The reason to adopt a continuous improvement process methodology is simple: incremental and ongoing changes to business processes result in better business outcomes. Many organizations think they need to make big, broad changes to have a meaningful impact. But this kind of change is time consuming and difficult to measure. Continuous improvement efforts are about doing the opposite—small, gradual changes that lead to a sweeping sea change over time.

For the pharmaceutical industry, effective regulatory intelligence management is no longer optional—it’s a strategic imperative. But a lack of transparency and siloed data hinders compliance efforts, slowing down clinical trials and ultimately the entire product lifecycle. Key hurdles include: Pharma companies need a better way to navigate complex regulatory requirements.

To improve processes, businesses need both qualitative and quantitative data. Too many companies rely on qualitative data alone, using anecdotal evidence and intuition. But without seeing the numbers, how can you know if you’re meeting key performance indicators (KPIs)? How can you know for sure that your real-world operational processes are setting you up to meet your goals?

In 2025, the financial services industry will undergo a seismic shift. We’ll see: Imagine near-perfect AI-driven document data extraction. Agentic AI that brings together automation technologies and human intelligence. Flexible data fabric networks that make faster data-driven insights possible without the burden of costly consolidation. For financial leaders, this will be a year of bold moves and transformative strategies.