HELLO 2019: 5 Predictions on RPA and Business Line AI Use Cases

Here’s a look into the new year with five predictions for 2019 from Tom Wilde, CEO and Founder of Indico, on how next year could be the year that AI finds real business use cases.

#1: AI/Data Science Meets the Line of Business

One of AI’s biggest obstacles has been the disconnect between data science teams and subject matter experts (SMEs) in the business. SMEs play a critical role but the complexity of the underlying tech typically requires a lot of data science expertise. Enterprises will put increasing pressure on their teams to close this gap so that they can get more value from their AI initiatives.

#2: The Rise of Explainable AI

As AI becomes embedded in more and more processes, there is an increasing need for transparency in how it works and makes decisions on our behalf. Users will demand real-world, plain English examples and explanations to for full transparency. This will also make it easier for data science and SMEs to collaborate on improving AI’s contribution to the business.

#3: More Focus on Mid- and Back Office Applications/Use Cases

A lot of the attention in AI to date has been on the front office applications – those involving customer service interactions via bots. As companies look for ways to drive more profitable growth, they are looking at more opportunities to use AI and machine learning in their back-office operations – especially those manual, document-based workflows that drive many of their core business processes.

#4: AI is No Longer “What.” It’s “How.”

Companies are looking for business solutions – aimed at improving the customer experience, accelerating cycle time, increasing business efficiency, and expanding capacity and productivity. Expect to see fewer AI-only solutions coming to market, and fewer pure-AI startups being funded.

#5: Filling the Gap Between RPA and AI (IPA)

RPA has been one of the hottest areas of tech in the last two years – because of its simple, easy-to-understand value prop – process automation, efficiency; freeing resources up to focus on higher value activities, etc. But It has fundamental limits – it’s only effective with rote, repetitive processes and it cannot impact workflows involving unstructured content which makes up over 80% of data in most enterprises.

At the same time, AI and machine learning are seen as too esoteric; requiring too much data science expertise, too much hand-holding, too much uncertainty and risk about ROI. Companies will look to bridge the gap in 2019 – between the horsepower of RPA and the intellect of AI/machine learning through what many experts are calling “intelligent process automation,” or IPA.

Editor’s Note: Where did the year go? 2019 is almost upon us, and the brightest minds in the real-time analytics space are looking forward to the new year and taking their guess at where they think this space is going. Help us expand the discussion and build out the future of real-time by joining the RTInsights Brain Trust)