December 03 - 06, 2019
Hyatt Regency New Orleans

Five AI-based Tech Advancements are Enhancing Intelligent Automation

By: Katherine Byrne

As you probably already know, intelligent automation (IA) combines artificial intelligence (AI), machine learning (ML) and automation innovations. Intelligent automation can be connected to increasingly complex procedures, which provides an overwhelming amount of insight (as opposed to just utilizing ‘normal’ scripted or rules-based automation). IA enables additional speed and precision and is also equipped for extricating data and determining learnings that can be fed into downstream procedures.

Other intelligent automation innovations are evolving at a fast pace too, including a big group of key capabilities that are helping companies to accomplish key business objectives and boost ROIs.

Let’s take a look at five AI-based tech advancements that are driving intelligent automation to be even more intelligent:

1. Content processing
Very soon, automation solutions will be able to process content in increasingly advanced ways. Content will be able to be processed in the form of videos, images, speech, or text. Theoretically, computer vision and deep learning (DL) algorithms will be able to extract, analyze, and understand more useful information from digital images, speech, and videos through the use of advanced image recognition and processing capabilities. This will truly be very interesting to see, as text and voice recognition has already gained the ability to understand sentiment and utterances. Further advancements will allow enterprises to extract and classify new information from semi-structured or unstructured data sources, such as emails and letters.

2. Online marketplace for pre-built automations
Solution providers have already begun to create online marketplaces that allow for partners and customers to trade vendor-validated pre-built reusable automations. This includes robots for horizontal and industry-specific processes, (such as accounts payable and claims or mortgage processing), and integrations with cognitive skills (like NLP and computer vision). This online marketplace for pre-built automations is helping to develop an ecosystem where there is easy access to a large and crowing repository of reusable automation components – thus reducing development time for enterprises.

3. Automating the automation
“Automating the automation” allows for some RPA platforms to use artificial intelligence to not only find new automation prospects within business process, but also allows for the optimal process variation to be identified. There have been some developments that have taken this further by robotically creating and adding respective automation workflows precisely in the automation design studio to be further enhanced and coded by users. This step forward has enabled a higher ease of use, in addition to allowing for enterprises to scale up quicker.

4. AI-based next-best action
Increasing artificial intelligence based next-best-action recommendations (to complement Robotic Desktop Automation) refers to the use of machine learning and AI to locate patterns based on past customer behavior/interactions, followed by recommendations for the next-best-action to help employees provide better customer service. For example, this could include suggesting specific up-sell/cross-selling opportunities to the customer service agent based on the customers past interactions. This could also include suggesting relevant questions for the customer service agent to ask in order to achieve a faster resolution of customer grievances.

5. Intelligent workload balancing
Intelligent workload balancing can best be defined as the ability of a platform to use embedded artificial intelligence to identify work distribution patterns and learn to distribute the workload autonomously over time. A series of load balancing algorithms can then be employed by the platform to identify and assign critical tasks to the available robots in case of an expected resource crunch.

In conclusion, intelligent automation is indeed becoming mainstream as more and more organizations implement these technologies into their business operations. The five artificial intelligence-based tech advancements mentioned above are driving intelligent automation to become even more intelligent, although that does not mean that every organization has fully embarked on an IA journey yet, or has plans to do so soon.

With this in mind, the trend towards IA implementation does signify a cultural movement whereas digital transformation is top of mind for many organizations. That said, how are companies preparing for the future, and in turn, how is the vendor landscape evolving to meet their growing needs? Is the IA landscape balanced between supply and demand, or will there come a time that supply will dry up, or vice versa? Additionally, how should your organization plan ahead so to be in the IA driver’s seat?

Join us next week at Intelligent Automation Chicago, taking place August 5-8th at the Sheraton Grand Chicago to uncover the answers to the questions above!

Return to Blog