Intelligent Automation Information Breakdown
Intelligent Automation Information Breakdown
Intelligent Automation can be broken down into three classes: Robotic Process Automation (RPA), Learning Cognitive Automation and Artificial Intelligence.
RPA is a quick hit, entry-level approach that includes tools such as rules engines and screen-scraping. RPA is sometime referred to as “macro-based” automation, and is valuable for automating mundane tasks – such as cutting and pasting data from one system to another. These tools work with existing IT systems, making them a great way to get started with intelligent automation.
Learning Cognitive Automation encompasses capabilities beyond RPA, such as natural language processing – which chatbots fit in. Chatbots continue to grow because they can deliver not only cost savings but also a better constituent experience. Chatbots can free the agent from repetitive tasks as constituents engage with the agency through cognitive-powered text or voice chat. With learning cognitive automation agents can devote more time to developing customer-centric skills. Los Angeles and Kansas City are among many governments finding ways to use chatbots to handle a variety of tasks.
Artificial Intelligence’s approach requires a substantial investment of time and resources but can address an agency’s most complicated problems. Cognitive software mimics human activities such as perceiving, inferring and gathering evidence and hypothesizing and reasoning. The power with artificial intelligence comes in its ability to ingest huge amounts of data quickly and arrive at possible solutions.
Recent advancements of AI have put it on a path to drive the future of our economy in an era of big data. AI can understand, monitor, reason, predict, interact, learn and improve – which makes it a hot topic for implementation across both private and public sectors. Accenture estimates that AI has the potential to double economic rates by 2035.
There is an excitement around many modern tools, but most government offices are still trying to reach more basic modern operating standards. Governments can start thinking about implementing AI by learning from previous government transformation efforts and AI implementations in the private sector.
While AI is not a solution to government problems, it is one powerful tool to increase government efficiency. Implementation of and use of AI in citizen services may also become an indicator of how the public sector can leverage other emerging digital tools.
Types of government problems appropriate for AI applications:
· Resource allocation
o Administrative support is needed to speed up task completion
o Inquire response times are long due to insufficient support
· Large datasets
o Dataset is too large for employees to work with efficiently
o Internal and external datasets can be combines to enhance outputs and insights
o Data is highly structured with years of history
· Experts shortage
o Basic questions can be answered, freeing up time for experts
o Niche issues can be learned to support experts in research
Learn the latest information on Process Automation in Government at our event held in Washington D.C. on May 29th. http://bit.ly/2SZHAnB