You Asked, We Answered: Top 5 Questions on Combining RPA and AI
Last week’s webinar with top Everest Group analyst Sarah Burnett about the right way to combine RPA and AI was one of the biggest blockbusters WorkFusion has ever hosted. We were especially thrilled to see so many smart and relevant questions asked by our audience. Here are the best ones:
If I want to choose multiple automation solutions to meet my business needs, would that increase cost and complexity?
The short answer is yes. The long answer goes something like this: A lot of customers are looking for both RPA and AI to solve for different use cases, and if you already have one RPA solution, it may seem logical to tack AI on top of that. However, if you have multiple solutions, you’ll have to pay for multiple licenses, multiple hardware, and you will have two different systems to support. In addition, when you have two systems doing your automation (for example, if you would use Blue Prism and Watson) you’ll have two separate analytics tools. In order to stitch them together, you’d need another custom tool.
WorkFusion offers a single solution that encompasses RPA, AI, BPM, OCR and analytics. Besides offering simplicity and efficiency, a one-stop solution handles complexity better. It is, for example, a lot better at dealing with exceptions. Another advantage is that scaling with us over time is much more affordable as our license and infrastructure costs are vastly lower than our competitors’.
We’re in the financial industry and we have a huge pile of data that we have to manage, store and process. While it’s structured, it’s difficult to tag properly as it comes from various sources. How can WorkFusion help?
Glad you asked! As you’re no doubt aware, going forward, all companies will have ever-increasing data volumes that they will have to tackle. It’s an inevitable problem, and there’s no way around it. WorkFusion is taking a data-first approach using AI to manage and process any data you throw at it. But to go back to your specific industry, our client Six Financial Information is one of the world’s top providers of financial information and they faced similar issues that you do. Here’s how we provided a solution for them.
Is it recommended that existing processes are reviewed and broken down into manageable use cases before automating using RPA and AI?
Yes, it’s best practice to list the use cases that you have. WorkFusion can help you select which ones are the best candidates for automation. When identifying RPA use cases you should look for high-repetition, highly manual use cases with minimal process changes. If you want to automate complex use cases that require exception handling you’ll also need AI capabilities. A good way to distinguish these kinds of use cases to use hand work vs. head work model that you can see in our handy image above. And if you want to dive a little deeper, you should check out this video of an automated claims process that uses both AI & RPA.
Are data scientists needed for data-driven ‘cognitive’ automation?
Not if you opt to work with WorkFusion. We have a highly revered team of data scientists that work on optimizing our models and developing novel techniques. They leveraged their unique skillsets to create our Virtual Data Scientist, a model that teaches robots to train robots. This out-of-the-box tool enables engineers to produce high-quality machine learning outputs. To summarize, our brilliant data scientists help us create products that enable and empower your existing team so you don’t need to hire data scientists.
In sectors where there are regulatory and compliance standards around information and customer data, how do you stay in compliance using RPA and AI?
All enterprise customers need to apply best practices for their customer data. It entails how it’s stored, how it’s secured, who has access to it and how to stay in compliance when processing it.
The good news is that RPA and AI are helpful tools for this. For example, RPA’s capability to pull and aggregate data from multiple sources enhances the efficiency of regulatory, non-financial, and risk reporting as it can help eliminate or reduce the time-consuming processes of collecting, compiling, and cleansing, and summarizing large amounts of information. Monitoring and testing provide a powerful example of both RPA and AI’s potential to transform compliance operations. For example, financial service institutions execute a portfolio of individual tests to determine if their various operations are compliant with specific laws, rules and regulations.
We also help many enterprises in financial services and insurance stay in compliance by deploying WorkFusion on-premise in their own datacenter on their own hardware so they can store the customer data locally.
If you have any questions about RPA and cognitive automation, please get in touch with us.