In the last few years, IT operations vendors, as tech vendors in many sectors, began incorporating AI and machine learning capabilities into their solutions. Coined "AIOps" by Gartner, these tools are now commonplace, with dozens of startups launching every year.
2020 will see AIOps adoption going mainstream as use cases crystallize for improving IT efficiencies and supporting faster decision-making. Expect AI-enhanced automation to become smarter and more contextual, move towards the edge, and used increasingly for customer and user experience analysis. Yet there are significant challenges and cautions, which will shape AI's development in not only IT but across business and society.
AI will be questioned
This may seem like a negative way to look at AI in the new year, but one must go no further than understanding what's already occurred: the viral spread of fake news that may have affected important election outcomes, how children erroneously receive obscene content, and how terrorism spreads through pervasive social media algorithms. Could AI systems one day kill people, in an algorithm gone wrong?
Stuart Russell, a prominent professor and AI researcher from UC Berkeley opines in his new book that it's time to institute better control over the algorithms in AI applications to prevent dangerous outcomes: "If all goes well, it would herald a golden age for humanity, but we have to face the fact that we are planning to make entities that are far more powerful than humans. How do we ensure that they never, ever have power over us?" The impact on AI vendors and users will be a deeper investigation over how to design systems that are smart, safe and as Russell says, "explainable."
Governments will invest more in research
Foreign governments, such as China, are investing heavily in artificial intelligence. Offshore cyber-criminal groups are likely doing the same.
These pressures will incentivize US government agencies to spend more on R&D in artificial intelligence and machine learning, to support their own programs for criminal and terrorist surveillance and to deliver insights into designing useful, practical AI technologies.
AIOps will support new automation frameworks
As complexity grows in IT organizations, from multi-cloud and software-defined infrastructure to growing digital business initiatives, automation needs are changing. The next evolution of automation is smarter, more aware, and more contextual. AI and machine-learning technologies will discover hidden resources and threats, uncover patterns, filter the noise, aid decision-making and automatically fix routine issues. AI tools will incorporate self-learning algorithms so that IT operators can find root causes faster and get advice on how to optimize IT performance as conditions change.
AI tools will be increasingly applied to user/customer experience
The criticality of improving customer experience to gain a marketplace advantage is on every CXO's mind and AI will play a growing role here by more comprehensively analyzing customer interactions and data use. This could deliver insights on how to reduce customer churn through optimizing digital experiences or predicting how new application features will improve workforce productivity.
On a broader note, IT Operations leaders know they must track their efforts closer to business goals and needs, and AI will help them get there. In a recent survey conducted by OpsRamp, 64% of IT operations leaders believe their job is to deliver agile, responsive, and resilient infrastructure that can support fast-moving business requirements. AI will play a role here by forecasting the business service impact through analyzing infrastructure metrics and tying it back to business key performance indicators.
AIOps will be widely used on the edge
AIOps solutions typically run from the cloud. Yet this is getting more expensive and sluggish, as data volumes and use cases grow. As a result, companies will begin to deploy AI tools on the edge of the network, where it's faster and often cheaper. This will enable near real-time AI-enhanced monitoring, eliminating the travel time from the data center to cloud service and back. That time savings will bring a noticeable difference in the case of a critical incident resolution.
Implementing AI on the edge won't require new expertise to deploy; it will happen seamlessly behind the scenes through the cloud. Intelligent edge technology combined with the smart cloud will solidify the benefits of AI to IT operations teams.
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