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IBM Think puts AI to use in IT operations

Its annual conference may be virtual, but IBM has remained focused on the key themes of hybrid IT, cloud and artificial intelligence

The trend towards joining up disconnected processes, sometimes across different organisations, is driving digitisation initiatives. However, there is a growing realisation across the IT industry that these increasingly interconnected systems are proving impossible for IT administrators to keep healthy.

Arvind Krishna, CEO at IBM, said the pace of artificial intelligence (AI) and cloud adoption is increasing. “Businesses are questioning and transforming their operating models,” he said. “Transformation journeys that were going to last a few years are now being compacted into months.”

This transformation is having immediate and long-term consequences, added Krishna.

At its virtual Think event, IBM discussed its approach to AI for IT operations (AIOps) powered by its Watson deep learning system. The company unveiled a new offering called IBM Watson AIOps, which it said uses AI to automate how enterprises self-detect, diagnose and respond to IT anomalies in real time. 

According to research firm Aberdeen, unforeseen IT incidents and outages can cost businesses about $260,000 an hour. 

IBM said Watson AIOps enables organisations to start automating their IT infrastructure intelligently. The system has been designed to help CIOs better predict and shape future outcomes, focus resources on higher-value work and build more responsive and intelligent networks that can stay up and running longer, it said.

Coronavirus has driven the need for IT automation and use of AIOps. “Our industry was hit hard by the pandemic,” said Roland Schuetz, executive vice-president and chief information officer of the Lufthansa Group. “Our work in AI over the past several years will help us to mitigate some of the future challenges. Working with IBM to apply its Watson AI technologies has helped us accelerate how we modernise our data science tool landscape.

“We use AI to automate processes that result in benefits such as highly responsive customer care and operational topics. In this way, we are making an important contribution to a solid start after the crisis.”

Rob Thomas, senior vice-president, cloud and data platform at IBM, said: “The Covid-19 crisis and increased demand for remote work capabilities are driving the need for AI automation at an unprecedented rate and pace. With automation, we are empowering next-generation CIOs and their teams to prioritise the crucial work of today’s digital enterprises – managing and mining data to apply predictive insights that help lead to more impactful business results and lower cost.”

Read more about AIOps

  • Reducing effort, reacting faster, preventing problems and improving understanding of the IT environment are the key benefits of AIOps.
  • AIOps means artificial intelligence to aid in IT operations. But it could mean much more than that, bringing in development, business, security and other application stakeholders.

Discussing the keynote, Nick McQuire, senior vice-president and head of enterprise research at CCS Insight, said: “Understandably, AI was a focus throughout the keynote, as it was last year, but this year IBM is doubling down on automation as a key theme.”

McQuire said he expected Watson AIOps to address the need for IT departments to solve problems remotely and drive more automation into inefficient  processes.

“For me, it’s the right solution at the right time, especially as we enter a potentially severe global economic recession and customers will need to look at rationalisation as a strategy in order to survive. In this respect, a new pragmatism is now characterising approaches to AI, which IBM is certainly tapping into.”

As part of the announcement, IBM also introduced its Accelerator for Application Modernisation with AI service, aimed at tackling the high cost of application modernisation. It said the service provides a series of tools designed to optimise the end-to-end modernisation journey, accelerating the analysis and recommendations for various architectural and microservices options.

The accelerator leverages continuous learning and interpretable AI models to adapt to the client’s preferred software engineering practices and stays up to date with the evolution of technology and platforms.

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