Dynamic Automation: The Next Phase in Automation’s Evolution

By: Larisa Shwartz|Co-written by: Pritpal Arora - Leave a comment

Automation is to dynamic automation as cruise control is to autonomous vehicles. If you drive, you’ve probably used cruise control to manually bring your vehicle up to a certain speed and then set it to keep it there. You still need to keep your hands on the wheel to guide the car and pay attention to traffic, your route and anything on the road that requires you to change the speed. Human attention and intervention are necessary for everything outside of speed control.

An autonomous vehicle is a different story. Once you enter the car, you set the destination and the car holds the data to map out the right path. The vehicle maintains all the systems — including acceleration, braking, turns and speed — without requiring any intervention from the passenger. If a car brakes in front of you, the autonomous vehicle knows how to respond. If the vehicle gathers traffic data, it can reroute to avoid congestion. With these features, passengers can spend their commute working on things far more interesting than getting from point A to point B.

The Evolution of Automation

IBM Dynamic Automation marks the next stage in automation’s evolution. It moves beyond tasks and processes to focus instead on outcomes.

For most of us, the automation we’re familiar with is script-based. Humans determine what can be automated and then write scripts that enable it. This process removes the monotony of repetitive tasks — and the opportunity for human error. This level of automation works well for repetitive problems that never vary. It has transformed the daily lives of IT teams all over the globe. However, it still requires human interaction. If an event exceeds the bounds of the automated task, a human must intervene.

How many businesses experience problems that occur in the same way, without variation, every time? Very few. Businesses routinely face simultaneously occurring problems and unexpected events. By harnessing the power of IBM’s Watson and extensive learning network, you can dynamically respond to and resolve the unexpected.

How Dynamic Automation Works

IBM Dynamic Automation finds problems in IT systems and then fixes them before they become critical. To accomplish this, it uses predictive analytics and virtual engineers and relies on an extensive library of defined events and actions. This library has been built over decades of global deployment experience. Each automation event contributes to the library, creating one of the most robust and customized automation models available. Analytic capabilities built into this system learn and proactively identify events that can be automated and the actions that those events should prompt.

This library of actions and events then powers virtual engineers that continually monitor systems for irregularities. These software components use algorithms to assess and address problems spotted in a system — all without human intervention. When the virtual engineer finds an irregularity, it acts like a human engineer. It goes through a series of programmed actions to ameliorate the problem.

It is also designed to collect data about issues that it does not know how to fix. Once collected, the data is analyzed by predictive analytics and then shared with the team that manages the system. From there, the team gains new insights and adds more events and fixes to the library that the virtual engineer can use in the future.

Increasingly complex IT environments demand an analytical approach to problem resolution. By combining predictive analytics and dynamic automation, you can ultimately reduce operational fixes from months to minutes. Your IT team can then spend that time channeling its creativity into innovation and better customer experiences.

About The Co-author

Pritpal Arora

Pritpal Singh Arora

Enterprise Architect, TI&A-Enterprise/Analytics Consulting; IBM Expert Cert. IT Specialist, IBM Sr. Inventor

Pritpal Singh Arora is a leading enterprise architect with 21 years of IT experience, 7 years in IBM GTS with distinctive proficiency in middle-ware, analytics and database technologies, driving major transformation programs, architectural blueprints, health-check engagements, service improvement initiatives for high profile clients across the globe for banking, insurance and retail domains. As an integration architect, He has been instrumental in major transitions for deployments of Workspace Support Services with Watson offerings for multiple clients across the globe. He is a part of the core team to build up a Technical Health-check as a service that provides a continual, automated, near real-time indication of overall architectural health of an enterprise with a Cognitive and Health processing analytic engine. He is an IBM Sr. Inventor on plateau 3 with more than 15 patents filed around the Enterprise/Hybrid Cloud and Operational Analytics space.

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About The Author

Larisa Shwartz

STSM, IT Service Management and Analytics

Larisa (Laura) Shwartz is a senior technical researcher (STSM) at IBM T. J. Watson Research Center with research experience in mathematics and computer science. She received her Ph.D. degree in mathematics from UNISA University. Dr. Shwartz is focusing on analytics for IT service management, cloud systems and automation. She has over 50 publications and more than 80 patents and patent applications.