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In December 2019, Automation Hero was introduced to Swiss insurance group Baloise at an Ideation Workshop in Europe, hosted by global tech accelerator Plug and Play. 

Over the next few months, the two companies worked together to solve a long-running problem for Baloise: the company’s asset management team was doing too much manual work and spending too many staff hours extracting data and running analyses. 

Automation Hero built a solution that streamlined data gathering, aggregation, and data entry work for the Baloise staff, ultimately saving the company some 700 hours per year. 

Plug and Play’s case study tells the story of how Automation Hero ultimately saved the Swiss insurer Baloise 700 hours of manual work.

Read the full case study here.

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With the number of natural disasters continuing to increase thanks to climate change, mitigating portfolio risk is no longer enough for P&C insurers. Adjusters need a faster way to process disaster claims.

AI-powered automation platforms are the solution to dramatically accelerating claims approvals. In fact, Automation Hero’s deep-learning AI has helped insurance companies reduce claim processing times by 80%.

In our new e-book, “Why P&C Insurers Need Automation Tools for the Rising Tide of Catastrophe Claims,” you’ll learn:

• Why manual processes and legacy RPAs no longer cut it
• How automated data extraction can eliminate tedious document processes
• Why claims adjusters need AI to better serve customers — and reduce risk
• How Automation Hero is a key tool in your strategy to alleviate exposure to climate change

Many customers interact with businesses solely through their call centers and contact centers, so it is extremely critical for businesses to improve that customer experience. Thankfully, this can be improved easily with call center automation.

Most call centers see productivity problems, resulting in long wait times, high call abandonment rates and poor average handle times. When things go awry with a call center employee, customers aren’t afraid to take their business elsewhere. Some 76% of customers report that it’s easier than ever to switch service providers based on their experience. According to a NewVoice Media report, bad customer service costs U.S. businesses more than $75 billion a year, all from loss of customers. So how can automation help?

Perhaps you’ve implemented RPA in your company. You may or may not have seen improvements and hit the goals or metrics you were hoping to achieve. Whether you’ve seen success or not, there’s still room for improvement by adding AI. 

In this piece, we’ll walk you through the differences between RPA (robotic process automation) technology and IPA (intelligent process automation), and spell out the advantages of each. 

For example: even after implementing automation, many employees still spend hours of time manually handling processes that are too complex for RPA. These include data extraction, data normalization, and turning unstructured content (e.g. text and images) into a structured, usable format.

Businesses have leaned on robotic process automation (RPA) to help identify, organize, and create tasks based on information in documents. Some organizations have attempted to push the limits of their legacy RPA tools to make their document processing even more effective. Unfortunately, these tools fail to provide larger-scale features of intelligent document processing (IDP) because they:

• Can’t understand unstructured information
• Fail to scale processes along with business growth
• Don’t operate as an end-to-end solution

Developers can use better designed IDP systems to make their document processing more efficient than ever before. In our e-book, The Evolution of Intelligent Document Processing, you’ll learn about:

• How IDP technology has evolved over the last few decades
• The difference between legacy RPA tools and modern intelligent automation systems
• Automation Hero’s future-proof and flexible approach to IDP

Inefficient processes cost organizations as much as 20-30% of their annual revenue, according to IDC research. The average employee spends between 4 to 10 hours a week on repetitive computer tasks (up to 350 hours per year). Often these computer tasks aren’t directly related to their primary job function.

For your company, this could mean thousands or even millions of dollars are wasted on ineffective systems and processes. Intelligent process automation can help optimize organizational effectiveness by reducing the time and money that’s wasted on broken processes.

Automation has transformed businesses for more than two decades, streamlining processes so more can get done with less work. But if you’re still using a legacy RPA, you’re probably struggling with challenges like:

• High implementation costs
• Incredibly long lead times for initial setup
• Difficulty scaling to different departments or business processes
• Downtime caused by frequent maintenance
• Human error from data entry or incorrect routing

It’s time to take advantage of the time and cost savings associated with end-to-end intelligent process automation. In our new e-book, Beyond RPA: Upgrading to Enterprise AI for the 2020s, you’ll learn about:

• The new generation of deep learning AI-powered automation
• How IDP can process virtually every single type of document
• Automation Hero’s template-free approach that lets you build advanced AI models without IT support
• Leveraging our platform as an end-to-end solution or filling gaps in your current system

Enterprises have long used OCR tools to extract data from documents containing structured and semi-structured data. However, the limitations of the OCR tools are hard to ignore. OCR tools, lacking intelligence of their own, can hardly deliver accuracy greater than 60% in data extraction.

Given that the remainder of the document processing must be done manually, the amount of human involvement and effort required makes using OCR tools for bulk document processing a cumbersome exercise.

So, what’s next? The availability of artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and deep learning capabilities are all making IDP solutions the next logical solution – specifically around challenges involving handwriting and complex documents.

In the webinar, GigaOm Lead Analyst, Saurabh Sharma, will join Automation Hero CEO, Stefan Groschupf, for a discussion about the IDP landscape and what enterprises should consider when looking into a solution.

The value from enterprise AI that reads, understands, and processes documents like a human

Generative AI tools like ChatGPT have recently gained mainstream attention. The technologies contain large language models—AI trained to read and respond to language—to answer the user’s questions in a “chat” format. It seems that everyone is asking: “how can I use this technology?”

For enterprise tech leaders, the pressing question is how large language models and GPTs can move the needle and solve real-world business problems. At Automation Hero, we have seen firsthand the challenges that enterprises are facing, and we have developed award-winning AI and an end-to-end automation platform to provide real value in processing documents.

On June 15, Automation Hero executives discussed the promise, misconceptions, and most valuable applications of big language models, particularly in the context of documents.

During the webinar we discussed:

• The definition of an enterprise-ready GPT—from capabilities to data security
• How large language models can be used to read and understand documents
• Examples of Automation Hero’s GPT technology in action