AI-Powered Automation is Enterprise Automation 2.0

Extending process automation for our new normal.

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Many companies are exploring advanced automation as an essential part of how we address our new normal. The COVID-19 pandemic has changed the dynamics of business and how we work. One example is YouTube, which serves over a billion hours of video daily for 2 billion logged-in users. In a recent blog post, the company noted that with fewer people in its offices around the world, automation software is doing more content moderation. “We have started relying more on technology to help with some of the work normally done by [content] reviewers,” the company said. 

Supercharging automation with AI

Our new normal is driving client-demand to automate processes that eliminate repetitive, monotonous tasks and augment humans to produce super-human results more rapidly. To meet this demand, we are turbocharging automation with artificial intelligence (AI) to enable enterprises to automate a broader set of tasks, as the following examples illustrate:

  • Task elimination targets simple, repetitive tasks across business and IT. Automating these tasks will free employees up to do more thoughtful work. For example, with closed offices keeping many of its workers away, PayPal has turned to chatbots, using them for a record 65% of message-based customer inquiries in recent weeks. “The resources we are able to deploy through AI are allowing us to be more flexible with our staff and prioritize their safety and well-being,” PayPal said in a statement.   
  • Task augmentation supports, speeds up, and increases employee efficiency. For example, with the increased use of online services during the coronavirus pandemic, AI-powered customer service agents can allow a single agent to help more users, decrease service queues, and increase customer advocacy. AI is used to gauge user intent and capture information and the nature of the problem the customer is asking the company to solve. An automation workflow can then examine possible resolutions without engaging a human. That being said, the most powerful form of task augmentation is when humans and AI systems work hand-in-hand in achieving the desired outcome.

To achieve these results, we are actively working to advance automation technology towards AI-powered automation, which we declare to be Automation 2.0. AI-powered automation is defined as a continuous closed-loop automation process where data patterns are discovered and analyzed, such that decisions on insights from the data can be translated into automated actions, with AI providing proactive optimizations during each stage of the process. AI-powered automation uses actionable intelligence to deliver IT and business operations with speed, lower cost, and improved user experience. The next section examines these four stages, illustrating how AI is transforming at each of these stages.

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Discover

Better understand and classify unstructured data and processes so you can lessen the burden of manually analyzing and orchestrating actions. 

Without AI, data discovery associated with automation is mostly limited to structured processes and structured data. Unstructured data is inherently noisy and usually slows down the automation process. With the use of machine learning (ML), models are produced to cut through, tease out, and detect patterns in the noisy data. For example, with a properly trained classifier model, documents can be classified as an invoice or insurance claim. Similarly, alerts from an IT system can be grouped and matched to a specific trouble ticket. With AI, the discovery process is no longer blocked by lack of structure; it uses AI intelligently to move from discovery to decision making.

For a deeper explanation of the nuances between different types of AI technologies, see "AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference?"

Decide

Combine the precision of IT automation with well-defined methodology of business automation to automate faster and with more accuracy in both IT and business.

AI-powered automation aims to comprehensively provide a converged business and IT automation system that operates across a broad range of labor types, including business workers, solution architects, software engineers, IT operations, SRE, security, and compliance engineers. By discovering data patterns across business and IT, decision making can now be more impactful versus systems that are siloed to specific parts of an enterprise. An example of this is to correlate activity across software development and IT operations. In this case, changes to source code and configuration during development can be matched against incidents happening in a running IT system to predict risk associated with future changes to that code or configuration. By applying AI to automation, we are greatly improving the speed with which an enterprise can react to new patterns discovered.

Act

Engage software bots more naturally and collaboratively so engagements become more self-service and productive.

The automation process is further differentiated in how automated actions are carried out. The gold standard in automating actions is robotic process automation (RPA) technology. With power from AI, we are evolving RPA from simple robotic scripts to a tech that is more like a digital employee in the workplace. This pairing of the virtual and physical worlds allows actions to be simulated in order to head off problems before they even occur, prevent downtime, and develop new opportunities. Furthermore, Automation 2.0 uses advanced natural language processing to produce a more collaborative relationship between AI and employees to produce a hybrid-workforce.

Optimize

Predict potential incidents earlier so systems can proactively resolve issues before they impact normal operations.

Optimizations are continuously applied during discover, decision, and action phases, capitalizing on new insights to autonomously enhance business and IT operations through closed-loop feedback. In Automation 2.0, optimizations move beyond reactive to predictive and proactive. With an end-to-end view of data across business and IT, AI-powered automation can anticipate fluctuations and help avoid overreacting. For example, by combining structured and unstructured properties of historical change and incident records from enterprise IT, linkages can be extracted between change-incidents to create empirical evidence as new inputs to a change risk model. As new changes are rolled out by IT, real-time proactive alerts can be issued based on predictions that illustrate why these changes are high-risk based on past evidence. Gartner Market Guide for AIOps platforms declares this proactive style of risk management as the most sophisticated stage of automation…Read about the importance of speed of automation in the complete blog here.

Interested to leverage AI-powered automation for reducing the amount of time spent on manual processes by 80%¹ and decrease customer wait times by half²? Read about IBM Cloud Pak for Business Automation here.

 


¹ IBM Client Story: Banco Popular 
² IBM Solution Brief: Brownells, Inc. 

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