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RPA and Artificial Intelligence - Five QuestionsBOSTON, March 17, 2020 (GLOBE NEWSWIRE) -- Indico CEO Tom Wilde sees a shifting landscape ahead for AI and RPA users as enterprises look to move their process automation efforts forward. Some of the major themes he’s watching include: RPA’s adoption problem; the poor viability of AI-only projects; explainability as a new standard for AI; the opportunity in scalable decisioning; and the emergence of Automation Centers of Excellence. 1. Is RPA at an Inflection Point? However, those users trying to expand their use of RPA are discovering real limits – both in the technology and within their organizations. According to Gartner, only a small percentage of RPA users have expanded their implementations across the enterprise. For one, it requires organizations to re-engineer business processes. That takes time. Additionally, RPA has some built-in technological limitations. While it’s great with repetitive, deterministic businesses processes involving structured data -- where there is no judgment involved. However, it does not work when it is required to make judgements about information or learn and improve with experience. These type of workflows require some level of cognitive ability. They also make up the majority for many enterprises today; e.g., contract analytics, audit planning and reporting, RFP analysis and composition, sales opportunity workflow automation, customer support analysis and automation, appraisal and claims analysis, etc. 2. Can AI-Only Projects Succeed? 3. Is Explainability Becoming a New Standard for AI-Powered Initiatives?
When your AI algorithm makes a decision to do something or to take some action, business users (and in some cases, regulatory bodies) need to know how it reached that conclusion. It’s less about how the algorithm works and more about why it makes decisions the way it does. They want an audit trail. In our experience, about 80% of AI errors can be tied back to bad training data. The problem is finding it. It is really important to tie every prediction that your algorithm makes back to training data so you can figure out why it made the decision it did. The other reason is to prevent unintended bias. Most AI models are trained on some historical behavior or reference. The ability to understand how an algorithm is making decisions can surface potential problems. 4. Will Automation Centers of Excellence Become Best Practice in Large Enterprises? These groups are responsible for defining clear KPIs and business outcomes for each use case and connecting data science and technology with lines of business so best practices can be applied more quickly across an organization. Today, we see this most commonly in financial services, but we expect to see more Automation CoEs in manufacturing and healthcare in 2020. 5. Is Scalable Decisioning A New Power Use Case for AI? New applications of AI and deep learning are changing this though. By adding the cognitive ability of deep learning to process automation, users have the ability to automate a much larger percentage of business process decisions and minimize the manual intervention required in those processes. We are already seeing these capabilities being applied to a number of enterprise use cases such as contract analytics, regulatory compliance, customer support analysis, insurance claims analysis, and more. In legal departments, for example, contracts that are out of compliance, can easily be automatically flagged for manual review, vs. having a highly trained legal resource comb through volumes of pages to determine which contracts are in and out of compliance. About Indico
Media Contact: Jeremy Stinson Indico 508.277.7837 [email protected] |