AI-Injected Automation is the New DevOps

Summary Bullets:

Charlotte Dunlap – Principal Analyst, Application Platforms

• Modernizing workflow processes has become a complex endeavor as a result of increased data sources and volumes

• Automation, when applied to IT, provides capabilities that bring insights and diagnostics into various backend systems

AI-injected automation has become the power source for digitizing companies in a pandemic era, ensuring resilience, agility, and efficiency for modern, distributed apps. Modernizing workflow processes has become a complex endeavor as a result of increased data sources and volumes, heightening the need for comprehensive data management, integration, and security strategies. Automation, when applied to IT however, provides capabilities that bring insights and diagnostics into various backend systems, including pre-determined automation of problem remediation and policy controls.

Enterprises are increasingly looking to automation to provide the capacity to move traditional on-premises business processes or workflows into distributed, and sometimes multi-cloud, deployments. Automation helps resolve the fact that operations teams can’t keep up with the time-consuming demands of manually configuring repetitive operational processes within IT systems. New intelligent automation solutions also improve the UX through newer integrations with popular collaboration tools so more people can participate in the process of moving workflows to the cloud, naturally reducing operational risk and lowering costs. Another element of AI-powered automation is the ability to perform predictive maintenance by using analytics to identify anomalies and potential trouble spots and to take action before outages occur, reducing equipment downtime.

For many companies struggling with how to get started in meaningful DevOps-related business transformations, intelligent workflow tools provide a solid starting point in IT automation. As companies move to digitize workloads and automate various processes as part of digital transformation initiatives, advanced analytics and observability innovations (such as process mining and task mining) are beginning to play an important role in identifying areas where inefficiencies may exist and improving business workflows within a CICD model. Creating modern workflow processes has become complex due to increased volumes and sources of data, which must be appropriately connected and managed. Process and task mining technology is aimed at abstracting traditional operational provisioning associated with the costly and complex lifecycle of digitizing workloads. This is achieved via insights and diagnostics into various backend systems, including pre-determined (and eventually self-learning) automation of problem remediation and policy controls. Solutions include standardization with leading systems and platforms such as SAP, Oracle, Workday, and Salesforce for end-to-end digital workflows.

The cloud integration value chain is represented by the merging of transaction, workload, and data integration – a concept that’s been significantly enhanced over the past year through AI and automation to advance continuous integration under an evolving DevOps model. Modern apps are designed to flow through sophisticated pipelines involving an application lifecycle the likes of which have never before been in operation. Those pipes will be further enhanced in coming months through evolving observability technologies, built on advanced concepts such as Prometheus and Grafana. The innovations address reliability issues well beyond traditional APM solutions not equipped to support the true cloud-native scalability stemming from high-speed data integrations. Observability in particular appears poised to help bring together enterprise teams (applications, database, and network/infrastructure) often at odds with one another due to competing agendas: keeping the network up and running at all costs versus establishing flexible platforms that speed innovation.

The competitive landscape includes a variety of players approaching the technology from different angles, including low-code leaders such as Appian and OutSystems; cloud giants Google, AWS, and Microsoft; traditional middleware/app platforms providers IBM, Red Hat, and SAP; and RPA vendors, including Automation Anywhere, UiPath, BluePrism, and others.

For a deeper dive into this topic please see AI-Injected Automation Has Become the Power Source to Digitization, June 24, 2021)

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