Cutting Cloud Complexity with AI Automation

Business-level logic reduces risk...
both virtualized and containerized services. Strategic partners also include AWS and Microsoft, among others. As Coward pointed out, “Through our partner network and global system integrators, we are making it easy to integrate our software with partner products and tie together complete, end-to-end automation around it.” 

Cloud automation starts with process mining. It is essential to identifying the priority processes to automate, based on the jobs and tasks that teams, such as Development Operations (DevOps), Network Operations (NetOps), Information Technology (IT) and others, perform on a daily basis—and to understand where the bottlenecks are actually occuring in order to improve efficiencies by modeling scenarios across an organization. This helps create an understanding that provides visibility into data silos across applications, systems and processes, leveraging insights to connect clouds—securely—and informs a definitive list of what talks to what, for what purpose, and what control. This creates holistic cloud and infrastructure visibility that provides workers with quick access to the information they need—to keep the applications, network, and services up and running—without flooding them with irrelevant information they don’t need or understand.

“In a typical telco environment, changing a single firewall parameter can be a six-week process—and take even longer in a more regulated enterprise, like a bank,” Coward explained. “It’s about having a holistic understanding to develop a customer-specific transformation strategy, informed by cost projections, modeling, and technology expertise to help solve key problems in the cloud-automation journey for our customers,” Coward said.  

The logic of business-level automation

Today, AI and automation should be directly tied to business outcomes and success—and it’s exactly this approach that IBM is embracing with intelligent automation at the business-logic level. Business-level logic reduces risk, creates efficiency where and when it’s needed, and can provide repeatable benefits.

Consider the pitfalls of automation by script, a method that is commonly used to combat network outages. When the network goes down between New York and Chicago, NetOps creates a script that runs and fixes it, and can be reused again should that specific network outage occur again. But over time, there may be a different script for each city, potentially leading to thousands of scripts that must be managed. This creates a paradox, as the scripting that was put in place to create efficiency and reduce risk has created more complexity, and added extra risk. IBM’s approach is different and looks at both the problem and solution from the business-logic level.

“Instead of looking at the problem from a connection, instance, or device level, we look at the business logic and say, if the connection between two cities in the US breaks, find an alternate path and increments of bandwidth, and provision them across the infrastructure,” Coward stated. “That business rule doesn’t care what vendors or cities are involved, but the technology drives the logic across the environment to make it happen.” Abstraction driven by business logic reduces risk and unlocks reusable efficiency that can be applied across the organization.

Successful cloud management and transformation can also be derived by enabling proactive AI Operations (AIOps). Think about network operations and fault management. When the network goes down, NetOps scrambles to sift through what changed immediately prior—but this is a job perfectly suited for AIOps’ predictive capabilities and proactive resolution. AI’s predictive analytics capabilities can identify the alert when it comes on Saturday night, before peak traffic is going to send the network down and cause a massive problem on Monday morning.

Cutting cloud complexity

To fully realize the benefits that the cloud has to offer, service providers and enterprises must effectively tame its complexity. This is no easy task. It requires a unique understanding across businesses, processes, and industry applications. It needs advanced automation technologies, such as AI and ML. It must be supported by a portfolio of products and an ecosystem of partners, all focused on key business outcomes and delivering a specific, tailored strategy designed for individual customer success. 

Our conversation with IBM was enlightening. IBM appears to be uniquely positioned to help cut the complexity of cloud for its customers. Its pedigree as pioneer of both cloud and AI technologies, portfolio growth through strategic acquisitions, and broad industry experience—helping enterprises transform for over 100 years—may just be what the industry now needs.


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