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These days there’s a lot of hyperbole and hyperventilation over “the rise of machines.” Even as Siri and Alexa slip quietly into the flow of our lives and we watch IBM’s Watson play Jeopardy! on TV, artificial intelligence and machine learning still draw comparisons to SkyNet and the movie I, Robot. Marketers play on this to draw attention, declaring in intentionally stark blog posts that “AI is better than humans” and it “really is going to take people’s jobs.”

We can speculate, we can worry, we can ponder the defeat of humanity in the face of AI, but that focuses on provocative possibilities, not the reality. To understand the potential for machine learning, we must stop obsessing about the forest and sharpen our focus on the trees.

On a macro level, there’s no doubt that machine learning will reduce the need for manual labor across many industries and roles, including the maintenance aspects of IT. But as basic tasks are taken over by machines, it opens up tremendous opportunity for human roles focused not on rote tasks, but on innovation.

This rebalancing of human/machine roles is already happening. Hospitals are using AI to manage scheduling and diagnoses so doctors and nurses can focus more on personal interactions with patients. Amazon’s Alexa is streamlining the grocery shopping process by proactively learning buying patterns. IT teams are using machine learning for monitoring and troubleshooting, keeping engineering and development teams focused on product and service delivery improvements.

AI might be better named as IA: intelligence augmentation. It’s not just the reality of machine learning today; it’s the reality of how humans and technology interact and intersect. One does not supplant the other. We work in concert. It’s happening everywhere — and it’s not just for consumers, either. Humans and organizations use IA technologies to augment and improve their lives and workflows in everything from trip planning to IT optimization. But there’s one common factor that separates the most successful AI from the rest, and it isn’t algorithms or even heuristics. It’s high-quality data.

Take, for example, Google Maps, which is transforming the commuter experience with not only real-time traffic information but predictive modeling on traffic patterns days and weeks in the future. Google has something no other company has: millions of Android phones that it’s turned into a network of real-time traffic sensors. Through the application of machine learning, Google can create these real-time and predictive models. But it’s not just the AI that matters here. It’s the quality of its data source. High-quality data describes the world around us, and machine learning analyzes that data to spot patterns, learn trends, and anticipate the future based on those trends. Google’s massive, high-quality traffic data is the perfect data source to use machine learning to augment Google Maps.

The same logic applies to IT. Technology — from Google Maps to Alexa to cybersecurity solutions — is only as smart as the data set it’s using. And thanks to advances in analytics, IT organizations are also gaining access to new high-fidelity data sources ripe for machining learning.

While IT organizations are already applying rudimentary machine learning to data sets like log files and code data, they are now turning to a new source of data that encompasses all digital interactions: the enterprise network — a data source so vast that tapping it has only recently become possible. Flowing over the network is a wealth of data about every aspect of the digital business, from application performance, to security issues like denial-of-service attacks and ransomware threats, to end-user experience. It’s real time, it’s accurate, and it’s complete. When we apply machine learning to this data set, it becomes the eyes and ears for IT, alerting them to application performance issues and potential security threats before they can affect business operations or customer experience.

Today, the world centers on digital experience, and it must be seamless, secure, and ubiquitous. People want smart homes and smart cars and smartphones. They want to ask Siri and Alexa, and they want an answer immediately. Machine learning and IA technologies are making that possible. In enterprise IT, which is tasked with supporting a seamless digital experience for everything from online shopping to patient care to shipping logistics, the promise of IA is even more powerful. The “Alexa for IT” will be always on, always listening, always learning. Not a replacement for the architects and engineers and operators, but the go-to first line for any question. A source of insight that “keeps the lights on” so that humans can focus on the work of continuous improvement and innovation.

Arif Kareem is the CEO of ExtraHop Networks.

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