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RPA and Artificial Intelligence - Five Questions
[March 17, 2020]

RPA and Artificial Intelligence - Five Questions


BOSTON, 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?
RPA continues to be one of the hottest areas of enterprise tech. And it will continue to grow rapidly as it gains new users in small and mid-size enterprises. According to Gartner, most of the inquiry calls it received from users are from new users just getting started. That suggests there is still huge potential for continued growth. 

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?
According to Gartner, only 15-20% of enterprise AI projects are ever designated for production use, and only about half of those projects actually make it into production. The reasons vary. Projects drift and get pushed aside due to changing priorities and solutions fail to deliver, but the most common issue is that enterprise users underestimate the ecosystem in which AI has to run. There are a lot of things to  consider: infrastructure, process, people, organizational structure, business sponsorship – that impact how AI will run (or no), but the reality is that too many AI-projects start as data science projects—entirely disconnected from the business, with no clear business outcome or KPIs in mind.



Experimentation is important, but without a defined business outcome, projects are doomed. Users must ask themselves, why they are using and what business outcome will it achieve.

3.  Is Explainability Becoming a New Standard for AI-Powered Initiatives?
Everyone agrees that RPA, AI, Natural Language Processing (NLP) and Intelligent Process Automation (IPA) technologies can all add tremendous value by automating mundane tasks and allowing people to focus on more important things. Unfortunately, there have been problems realizing those benefits in practice: 


  • Many have experienced AI that is so complex, and NLP capabilities that are so cutting edge, that non-technical people will never be able to understand them.
  • Most solutions have been integrated behind the scenes so that you don't have to worry about how it works. But if you want to change anything, you’re out of luck.

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?
As more and more enterprises look beyond the hype of digital transformation and focus on putting it into practice via business process automation, we’re seeing the emergence of Automation Centers of Excellence—teams tasked with identifying the best opportunities for automation in an organization and finding the right solution to accomplish that goal. Sometimes that may be RPA, but in others it may be AI or IPA. 

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?
One of the big goals of process automation is the ability to speed accurate decision-making to accelerate cycle time. To date though, business process management (BPM) and RPA efforts have stalled in this area – due to the limitations of hard-coded rules-engines and taxonomies. If the decision is not binary one, defined by pre-existing conditions, these technologies cannot make one.

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
Indico is the leading provider of Intelligent Process Automation (IPA) solutions. We help organizations turn process into profit by enabling them to automate manual, labor-intensive, document-based workflows. Our breakthrough in solving these challenges is an approach known as transfer learning, which allows users to train machine learning models with orders of magnitude less data than required by traditional rule-based techniques. With Indico, enterprises are now able to deploy AI to unstructured content challenges more effectively while eliminating many of the common barriers to adoption. For more information, visit https://indico.io/.

Media Contact:
Jeremy Stinson
Indico
508.277.7837
jeremy@indico.io

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