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#31 Improving Employee Value with Catalytic
on Mon Dec 07 2020 16:00:00 GMT-0800 (Pacific Standard Time)
In part one of two episodes, Sean Chou, Catalytic CEO, and Neil Bahandur, Catalytic Head of Partnerships, join Darren to talk about Catalytic’s technology and how RPAs can help employees become more valuable through automation of back office repeatable processes.
RPA technology lineage comes from test automation and Excel macros, but the business lineage comes from outsourcing. Catalytic, however, views its lineage as software, with its system as the next evolution in creating custom software. Co-founder and CEO Sean Chou’s interest is in back office operations and efficiency. That narrow focus continues to allow Catalytic to employ AI in a full way and allows them to build something that is accessible in no-code.
Catalytic uses AI in a few different, tactical ways within the application to help reduce the need for expertise.
For example, one of the hardest parts of learning any platform is understanding its capabilities. One way to use AI to help offset this issue is by using natural language processing. People can just type in what they are trying to accomplish and the application can infer and say what action would be best help you accomplish this step. It’s basically a way to improve the product experience and decrease the level of expertise necessary.
Another example is that within the workflow, there are six different modules of different actions, each logically named for what it’s intended to solve, such as data processing, document assembly, etc. Within each module, there are different AI actions that people can use within their process, such as optical character recognition (OCR), sentiment analysis (is the person who wrote the email angry?) and other natural language processing actions.
There are other elements that are less AI and more utility actions such as providing information about a person based on their email address.
A lot of data flows through back office processes. Unfortunately, the system view of the data is often very fragmented. The Catalytic workflow can cut across systems, capturing everything. The data is stored in data tables, and it is easy to build machine learning models based on those tables, so you can predict future outcomes of other workflows.
Catalytic uses AI in these and other specific contexts to drive efficiency and provide insight and visibility.
For example, operational intelligence through AI is captured in the platform through a feature called Insights. For every workflow, this tab can tell you how long it takes for all the different steps, how often they break, and whether it’s a human error or integration failure. It gives you opportunities for improvement. At the same time, that machine learning algorithm can start learning to predict the outcomes of field values. So, machine learning can be used in multiple, related ways.
Most customers who have invested in multiple technologies over the years still feel like there is a gap between the investment and the business outcomes they want. That gap is usually filled by people who are doing tasks that software ought to be doing instead, such as copying and pasting something from one system to another. Sometimes adding software to the mix complicates things by adding extra steps and training. The Catalytic solution to these inefficiencies is a system that is reusable and extendable.
It’s impossible for any off-the-shelf-software to meet the individual needs of each business, and it’s inefficient and time consuming to see the ultimate value. So businesses must meet the software where it is or adapt the software to meet their business. The value in a WordPress-type story is apparent. If the power is actually in the hands of employees, you can have truly personalized software. Catalytic wants to put that power in place for any business process using RPAs combined with actions.