Optimizing DevOps with Machine Learning Technologies and BizOps

    Digital transformation efforts have accelerated rapidly in recent months. Yet, in many organizations, transformation is stalled by our decades-old predisposition to do hard things the hard way. With artificial intelligence (AI) and machine learning technologies at our disposal, it doesn’t have to be that way.

    With the adoption of DevOps, agile methodologies, microservices, and more, IT environments are becoming significantly more complex. New data streams and workflows continue to emerge. Teams need to contend with an ever-increasing demand for more, better, and faster application delivery. All these factors have created a sense of chaos for IT teams. At the same time, the needs of the business haven’t gone away. Customer experience is still paramount, requiring organizations to find new and better ways to use technology to align on business objectives.

    BizOps and AI: A Perfect Pairing

    BizOps is an emerging approach for addressing the challenge of connecting software development with the needs of the business. A data-driven framework, BizOps puts AI and machine learning front and center in its approach. These technologies are core to BizOps; they’re key to making teams more efficient, effective, and collaborative. BizOps empowers teams to make important decisions, react quickly to change, and continue to deliver the software and experience customers demand.

    The BizOps Manifesto, which I helped author, lays out 14 guiding principles to help define and advance the BizOps movement. I am particularly aligned with principle 7, which reads:

    Today’s organizations generate more data than humans can process, so informed decisions must be augmented by ML and/or AI.

    Increasingly, enterprises are turning to AI and machine learning technologies for help in making sense of the growing volume of data. They are also leveraging machine learning to automate time-intensive processes to create greater efficiencies and accelerate progress.

    In a recent study by Harvard Business Review, 85% of respondents said using AI more effectively would be a significant competitive advantage for their organization. The survey also found respondents are not happy with the impact that AI and machine learning technologies have had in their organizations. Often, this is because AI and machine learning were implemented in silos. As IT leaders look for ways to use technology to connect IT and business more effectively, they must consider the complete enterprise versus siloed functional teams or departments.

    One of the biggest challenges in aligning an organization’s development efforts with business needs is lack of data. In fact, 42% of business executives in the HBR study pointed to siloed or incomplete data as a top hindrance to digital transformation initiatives within their companies. Increasingly, enterprises are turning to AI and machine learning for help making sense of the growing volume of data.

    These technologies enable incredible leaps in productivity over human-powered workflows. For example, Appvance IQ’s patented machine learning technologies can create 6,000 unique scripts in under 10 minutes. This is 100,000 times faster than humans can write test scripts, no matter what language or test software they use. The production capability afforded through AI and machine learning offers a significant advantage to modern, software-oriented businesses.

    BizOps and AI: Keeping Customer Experience at the Core

    The first principle in the BizOps Manifesto states:

    Our highest priority is to wow customers and satisfy investors and stakeholders through continuous discovery and delivery of value-driven solutions.

    The performance of an app or service is paramount to customer satisfaction. In a recent survey by 451 Research, 70% of consumer respondents said they would switch brands or providers if the service they were using was slow or buggy. However, modern application stacks are complex and the resulting data is vast, making it difficult for operations and DevOps teams to identify the source and impact of performance problems.

    AI helps organizations address this challenge in several important ways. Leveraging AI, machine learning, and automation in development exponentially increases the ability of the development team to create and push secure, high quality code into production. At the same time, AI and machine learning-driven capabilities and automation can significantly reduce the time it takes to identify and solve problems that affect end users. Automated software testing, powered by AI, is a critical example of aligning IT investments with business goals.

    Given the pace of transformation organizations are undergoing today, the need to better align IT with business objectives is becoming mission critical. BizOps helps put business outcomes at the center of everything from development to IT operations. In our application-centric world, customer experience is a business priority. By applying AI and machine learning solutions to analyze IT operations data and automate critical processes, organizations can improve decision making, ensure delivery of high-quality applications, and keep the customer experience at the center of their work.

    Please join me in supporting the BizOps movement by signing the BizOps Manifesto at bizopsmanifesto.org.