Imagine your business is an ocean. Under the surface, a rich ecosystem of processes, people, and technology needs to work together to create a healthy environment. Above the surface, constant waves can make or break your business, either propelling you to new opportunities or sinking you under pressure.

Data fabric might seem like yet another data management technological innovation you have to learn about—but this one is different from the rest. While technology continues to evolve, the data problems that enterprises face are in many cases getting worse over time: data, data, and more data is permeating the business.

Artificial intelligence (AI) promises outsized gains in business efficiency. And since organizations live or die by their processes, integrating AI into the picture through AI process optimization can help boost ROI with end-to-end process automation and enhancements. This blog will explore five ways AI can be used to improve and optimize process efficiency. But first, let’s explain what AI process optimization means.

I’m a firm believer that banks rarely invest to drive revenue and elevate customer service. But I recently hosted a roundtable discussion in London with Appian, a process automation platform company, where leaders from mainstream and challenger banks in the UK gathered to discuss the challenges facing financial institutions when adopting new technology.

Data management in the modern enterprise requires skill and stamina. With the mountainous rise in data volume and the complexity of managing disparate data sources across a wide variety of legacy systems and hybrid cloud environments, it can seem virtually impossible to successfully climb past enterprise data obstacles to success. However, data fabric technology eliminates some of those obstacles. Is it the answer for your data strategy?

Process automation tools automate manual and repetitive tasks inside a business process. But what distinguishes process automation tools, which may also be called business process automation (BPA) tools or hyperautomation tools, from other automation solutions is that they automate an entire business process, end to end. Some process automation examples can be found in banking customer onboarding processes and automated insurance underwriting.

Automation has been a linchpin for businesses for decades. Automation tools pave the way for greater efficiency, enhanced productivity, improved customer experience, and many other benefits. Yet, in some cases, businesses have only seen small gains from their automation efforts. When this happens, it could be because organizations were focused only on automation rather than the wider concept of hyperautomation.

How can you find time to drive efficiency across your organization when there is so much digital work to be done? This is one area where hyperautomation and digital transformation initiatives intersect. Yet, just like other technology trends, our understanding of hyperautomation and its value has become convoluted. Let’s demystify some of the gray areas surrounding what hyperautomation technologies and tools can help you accomplish as you shape automation strategy and select tools.

The importance of an organization’s data strategy cannot be understated in today’s world. With the speed at which data is created across the digital landscape, organizations need to be able to harness information from a wide variety of disparate sources at a rapid pace to drive business intelligence and competitive advantage.

If you’re evaluating enterprise business process automation technologies, it’s important to understand what complete process automation technology can do compared to today’s many automation point solutions. Process automation technologies automate repetitive and manual tasks inside a business process.

The banking and financial services industry is evolving rapidly with the advent of new technologies and digital platforms. To stay competitive, banks need to focus on providing superior customer experience to their institutional and retail customers. This should start with the initial Know Your Customer (KYC) process and continue through the entire customer lifecycle. Here are five keys to transforming practices for heightened customer experience in banking and finance.

Artificial intelligence (AI) has become increasingly important as organizations use it to drive innovation, enhance efficiency, improve decision making, and boost digital transformation efforts. But ensuring success requires significant, ongoing efforts. Consider setting up an AI center of excellence (CoE) to boost the success of your AI initiatives.

Do you know how many steps it takes you to complete a simple task like adding data to a record? What about how long it takes to send a follow-up email? For most of us, efficiency in our work is something we strive toward but not something we track. That’s where task mining and process mining can help. These tools enable you to see how work can be improved and where to optimize the larger process in which it’s involved. The more complex the process, the more the benefits add up.

Process discovery is a method for creating visual representations of business processes using data. It takes the guesswork out of business process improvement projects and makes it easier for business and IT leaders to spot inefficiencies. But to get there, you need to select the right set of process discovery tools.

Enterprise data. Whether you’re ready or not, it’s growing more and more important to organizational success every day. Business decisions, business insights, and business intelligence all depend on having the right data at the right time.

Data is the heart of business operations. And much of that data is sensitive or regulated. Cyber threat actors know they can benefit financially from your data by holding it for ransom or selling it on the dark web. The most recent Cost of a Data Breach Report 2022 from Ponemon Institute and IBM clocks the average data breach cost at $4.35 million USD (that’s $164 USD per data record). The stakes are high. Your security must be too.

2023 is looking likely to be a breakout year for artificial intelligence (AI) and machine learning (ML). Some industry-watchers predict that recent breakthroughs in AI might lead to a new revolution in society akin to the industrial revolution, the invention of the internet, or the advent of the smartphone. Yet, 2023 doesn’t mark the invention of AI—just the year it went viral thanks to OpenAI’s ChatGPT technology.

So, did you invest in RPA? Did you buy into the dream of automation? How's it going? Are your bots running at 100% reliability and delivering amazing ROI? Or are you fighting bot breakdowns and struggling to justify the expense of costly RPA bots with poor reliability?

When was the last time you had all the data you needed to make a business decision? Hopefully, it was today. But if it wasn’t, you’re not alone. It’s increasingly difficult for people inside enterprise organizations to harness the power of their data. Even though good decisions are nearly impossible without good data, getting data into the right hands at the right time is easier said than done.

In today's fast-paced and increasingly unpredictable business environment, it's more important than ever for you to deliver value with fewer resources. Modern enterprise process automation platforms are the key to connecting your people, processes, and data and unlocking operational efficiencies.

With the new year well underway, you might be feeling like all the new initiatives, lists of priorities, and looming project deadlines are endless—and you’re probably wondering how you’ll get it all done. Appian is here to help.

The philosophy behind continuous improvement is simple: incremental and ongoing changes to your organization result in more efficient business processes, better products, and superior customer experiences. The pursuit of excellence is familiar to many of us in our daily work, and your organization may already have a culture, goal, or process management method designed to improve performance and facilitate operational excellence.

Today, data is the lifeblood of every organization. Without it, you’re left without key insights into business processes, consumers, employees, and the overall health of your entire organization. And here’s the scariest part: most organizations are on life support due to their inability to properly manage their data and turn it into actionable insights. Data fabric technology can help in a significant way, which is why it’s been getting so much buzz recently.

Data fabrics are getting a lot of attention lately, and for good reason. But, for any topic with a lot of hype, there also tends to be a lot of confusion. If you are still trying to fully grasp where the concept of a data fabric architecture fits amongst all of the warehouses, lakes, lakehouses, and meshes of the data engineering world, let's set the record straight. What is a data fabric? A data fabric is a toolset that connects data across disparate sources to create a unified data model.

Artificial intelligence (AI) and machine learning (ML) bring significant efficiency gains to organizations and should play critical roles in any digital transformation efforts. One major use case for these technologies is AI document processing, also known as intelligent document processing (IDP). Organizations amass a large number of business documents—from invoices in billing departments to medical coding documents at hospitals.

Why do experiences matter? In turbulent economic times, organizations that can't offer high-quality digital products and services with excellent experiences cannot attract and retain customers and employees. Customers today are seeking more than just products or services. They are looking for memorable experiences that create a lasting impact and emotional connection with the organizations they interact with. And they are more than willing to pay more to have those experiences.