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AI vs Automation: 6 Ways To Spot Fake AI

Stephanie Overby, The Enterprisers Project
26.3.2020

Is that really artificial intelligence - or just automation, being described as AI? Let's explore the difference - and six possible signs of AI washing

Seems like everybody’s an artificial intelligence (AI) provider these days. The global enterprise AI market, which garnered $4.68 billion in revenues in 2018, is expected to generate $53.06 billion by 2026, according to a report by Allied Market Research. So it’s little wonder that seemingly every enterprise technology vendor wants to grab a piece of the AI pie.

But not all AI solutions are what they seem. “AI washing” – the practice of touting a technology solution as AI when it may be no more than simple automation or a new marketing spin for an existing application – is a real phenomenon, industry analysts say. “Very few, in my opinion, are using strong AI,” says Wayne Butterfield, director of cognitive automation and innovation at ISG. “What we need to be mindful of is that AI covers over 200 different disciplines, so it’s not uncommon to be using a branch of AI in a tool. Some advanced analytics may now be classed as AI, even a small amount of machine learning. This means that you can be creative as a vendor.”

The explosive growth in the velocity, volume, and variety of data being produced by today’s enterprise, along with increasing computing power and the accessibility of new tools, means more IT organizations are considering or deploying AI solutions to solve business problems. However, as always, it’s important to push back on hype and dig into what a new technology actually has to offer before signing on the dotted line.

What’s the difference between AI and automation?

There is a clear difference between AI (in its various forms, like machine learning, deep learning, or natural language processing) and non-AI automation - both in how they work and what types of outcomes they can produce.

“In general, AI relies on models and algorithms to autonomously find patterns in the data (‘inputs’) to provide insights, predictions, and prescriptions (‘outputs’) that could have significant business impact,” says JP Baritugo, director at business transformation and outsourcing consultancy Pace Harmon.

“In contrast, automation is traditionally used for processes where the input data is structured, the rules are defined with manageable exceptions, and interactions with multiple systems are required. Automation use cases are predominantly task-oriented versus a true end-to-end process view.”

An automation solution, for example, might be used to transfer data from emails into an ERP system, execute bulk data uploads and changes, or provision IT assets and resources for a new employee.

AI, by contrast, has broader applications in the business. It can cluster like data to drive business insights, classify data to determine if a customer is a churn risk, or provide predictions or recommendations such as the “next best action” based on a customer’s profile or behavior.

“Generally speaking, automation is applied to rote processes, and AI is ‘smart’ because the applications are trained to improve,” Baritugo says.

Telling the difference can still be hard. “The reality is that it is somewhat difficult to identify even for the relatively well-trained eye, since many of the systems present as black boxes that take in a certain input and present an output, regardless of whether AI is involved or not,” says Anil Vijayan, vice president at Everest Group. Making the environment even more confusing, some of today’s robotic process automation (RPA) providers now integrate cognitive capabilities into their offerings.

Read the full story on The Enterprisers Project

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