Many companies are exploring advanced automation (link resides outside ibm.com) 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 (link resides outside ibm.com), 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.
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:
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.
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?“
Combine the precision of IT automation with well-defined methodology of business automation so you can 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.
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 becoming 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.
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 being 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 (link resides outside ibm.com) declares this proactive style of risk management as the most sophisticated stage of automation.
The automation process of discover, decide, act, and optimize might lead one to think that automation is a sequential and time-consuming process. While it is absolutely true that perfecting the automation process can take weeks or months, there are fast paths forward. For example, the use of RPA and low-code development are all designed to speed up the automation of “bite-size” activities or processes so customers can get immediate ROI without having to wait until the entire end-to-end process is automated. The fast turnaround time also allows business and IT to fail fast by iterating quickly and responding in real-time to external forces.
AI-powered automation does not require everyone involved to be a data scientist. On the contrary, the use of AI enables automation technology to reach the general business user population, in addition to the IT developer, highly skilled knowledge workers, and, of course, the data scientist. Users across the enterprise benefit from pre-trained models that were prepared in advance by experts — allowing for immediate use — without requiring deep AI skills. Delivering AI-powered automations using natural language and chatbots creates an environment where the automation system meets users where they work, the way they work. This provides a more natural interaction, enabling more workers across an enterprise to both contribute and benefit from automation.
IBM’s approach to AI-powered automation takes the form of a converged business and IT automation system with an ability to continually optimize by discovering, deciding, and taking action as a means to automate processes across an enterprise. With this end-to-end view of automation, we are taking a bold step towards creating a hybrid workforce where your employees, collaborating with their digital twins, can gain deeper efficiencies across their business and free up time and money to focus on new business opportunities.
With this post, we have put AI-powered automation on the map — but we’re really just getting started. My next post will further define and examine the process behind AI-powered automation, expanding the view to include architecture and capabilities.
Also, please check out my new podcast called “The Art of Automation,” (link resides outside ibm.com) where I will invite subject matter experts to share their examples of how automation is changing everyday life for the better.
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