Everyone wants the same thing: To work less and profit more. But not everybody knows how to achieve those things. At Astera, we’re pretty sure we’ve cracked the code, and you might be surprised to learn that it starts with how you process your documents.

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.

Remember when you had to stand in long lines at the store, waiting for a cashier to check out your items? Or think back to when factories relied entirely on human hands to assemble products, one piece at a time. Fast forward to today, and self-checkout machines have made waiting in line a thing of the past, while robots on assembly lines are handling repetitive tasks with speed and precision.

For the past ten years, Astera has been serving enterprise customers to extract, integrate, and store data. Our solution has been trusted to handle large volumes of data with complicated needs. So what is it exactly that makes Astera the right choice for these enterprises?

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.

It’s 2025, and manual finance workflows are a thing of the past—or at least they should be. Enterprises and organizations in the healthcare, financial services, logistics, and retail sectors deal with thousands of invoices daily. Considering the high volume, automation is the obvious solution to ensure efficient and streamlined operations.

Logistics industry has to deal with data from multiple sources including bills of lading, customs declaration forms, proofs of delivery, and others. Then they have to ensure the data is prepped, extracted, parsed, converted to the right format and then analyzed. Given how important logistics is in today’s market, it is no wonder that McKinsey Global Supply Chain Leader Survey 2024 reported 74% of respondents were interested in advanced digital and AI-based tools for planning and scheduling.

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?