Cutting Cloud Complexity with AI Automation

In fact, IBM has been the persistent front runner in AI technology for decades. IBM arguably put AI on the map when its Deep Blue supercomputer publicly beat chess Grandmaster Kasparov in 1997, and again in 2011 when IBM’s Watson won Jeopardy!
The ultimate goal of the cloud is to provide simplicity and efficiency, yet getting there can be anything but simple or efficient. Enterprises and service providers are facing an increased level of cloud management complexity, all in the hopes of achieving higher performance and productivity. Given the momentum and the stakes, it’s essential to get cloud management right, and simplifying the complexity of cloud management will require a unique combination of automation and business-level intelligence.  Pipeline recently had the opportunity to discuss how to simplify the complexity of cloud management automation with Andrew Coward, General Manager of Software-Defined Networking at IBM. We talked about cloud resource management, AI-powered automation, and how IBM is uniquely poised to help enterprises and service providers capitalize on today’s cloud opportunities. 

Cloud management and AI automation

AI and machine learning (ML) are being layered across a wide range of industries to optimize automation and unlock efficiencies. According to IDC, by 2026, 85 percent of enterprises will combine human expertise with AI, Machine  Learning (ML), Natural Language Processing (NLP), and pattern recognition to drive outcomes like predictive maintenance, productivity, and efficiency. Yet many enterprises, including service providers, face gaps in maturity and technical AI talent that pose obstacles, such as ensuring the right data format or application of AI and ML technologies.

This is where IBM’s unique positioning, experience and capabilities can help. Coward explained, “IBM provides a unique understanding of applications, customers, industries, networks, and technology to create specific solutions that provide particular business outcomes.” He went on to add, “Our experience and technology enables unique insights, pattern-matching, and a holistic understanding of large, complex systems.”

Coward went on to describe how IBM is now focused on delivering the same level of automation and holistic knowledge on the networking side as it delivers on the computing side. This combines the strength of IBM’s internal, organic development with several strategic acquisitions to put the right array of technology and expertise in place. “IBM Research, for example, is able to bring forward an application for a new AI or ML mechanism to solve a specific business issue, and we then take that technology and apply it across our portfolio of products,” Coward added.

In fact, IBM has been the persistent front runner in AI technology for decades. IBM arguably put AI on the map when its Deep Blue supercomputer publicly beat chess Grandmaster Kasparov in 1997, and again in 2011 when IBM’s Watson won Jeopardy!, one of the world’s most popular trivia television game shows. Meanwhile, IBM’s Watson portfolio for business is helping enterprises better serve customers and cut costs across a variety of industries. Historically, Watson Health offered industry-leading data, analytics, and AI solutions to unlock health data to transform healthcare, and Watson was even being evaluated as a potential tool to transform the treatment of cancer. More recently Digital Iris, the AI airport concierge developed by IBM and Soul Machines, is being used to better serve travelers at Dallas-Fort Worth International Airport. Digital Iris uses IBM’s Watson for conversational AI and synthetic speech, with Soul Machines’ autonomous animation, to anticipate and answer travelers’ questions including gate updates, flight information, directions, restaurant information and more, all with human inflection, tone, and expression. However, IBM’s AI pedigree is just one piece of the automation puzzle.

The right cloud combination

In recent years, IBM has been strategically acquiring companies to add specific technologies to its AI and automation capabilities for network and cloud management. The acquisition of Accanto by IBM in 2020 strengthened its capabilities for orchestration in multi-vendor networks. In 2021, IBM acquired SevOne to bolster service assurance capabilities and manage applications over multi-vendor and multi-cloud infrastructures across end-to-end networks. Later in 2021, IBM acquired cloud-native virtual routing startup Volta Networks for distributed routing capabilities in cloud operations. This year, IBM announced the acquisition of NS1, which provides authoritative DNS for many of the world’s largest content providers, retailers, and banks. 

IBM also works with strategic partners worldwide on solutions for enterprise customers. Recent examples include IBM and Nokia’s plans to deliver private 5G managed services and a joint Juniper-IBM integrated telco-cloud solution for hosting


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