Source – cio.com
In recent years, enterprise machine learning has revolved around crunching numbers: analyzing datasets or tracking customer behavior. But what organizations will soon realize is that applying machine learning to content—physical documents, images, presentations and even conversational UIs—removes the cap on who machine learning impacts, and how far its value extends across the enterprise.
Tracking down lost documents and images, or drafting abstracts and case studies only to realize they’ve already been written are just a few of the daily frustrations that we typically consider unavoidable. But as it turns out, it’s these issues specifically—content discovery, tagging and classification—where machine learning is in a strategic position to make a substantial impact.
Numbers-driven algorithms have informed strategy for years now, but applying machine learning to content will likely have a similar, if not greater, impact on the enterprise. As companies generate written, verbal and visual content that’s measured and impactful, they’ll start to realize that numbers aren’t the only insights driving business forward.
Solving the problems you never knew existed
As words and phrases start to fuel machine learning algorithms, each and every member of an organization can reap the benefits. More often than not, these benefits will manifest in ways we never thought possible.
The inability to access, manipulate and leverage the right content at the right time is the hidden speed bump in your daily workflow. Too often, marketers, content creators, IT professionals or project managers are inundated with digital assets—blogs, websites, Google Docs, etc.—each serving separate business goals with different audiences and categories. Hours are wasted writing, editing, organizing and analyzing content that already exists, sparking frustrations and decreasing new output.
However, machine learning techniques can now classify content by category, provide answers to questions while you type, or offer suggestions that add depth to written material. Capabilities like these offer solutions to the problems you never thought could be fixed. By building on progress that’s already been made, companies can finally combine creativity with productivity to make future content even more compelling.
Internal content is only the half of it. Inbound materials like customer service inquiries, emails and requests may seem manageable at first, but can pile up over time. Questions arise around how to utilize this content effectively, and ultimately improve business communication as a whole. Applying machine learning in this context welcomes tools like FAQ generators that quickly consolidate inquiries, identify common questions, and generate FAQ documents accordingly. Simplifiers like these put time back into your schedule, and bring momentum to the enterprise from the inside out.
Discovering machine learning’s value
As organizations generate better (and smarter) content, the entire company will move down the path of least resistance. With content becoming more intelligent, materials are rolled out more quickly, inbound requests are analyzed and turned actionable automatically, and IT isn’t summoned to help with unnecessary tasks.
Even better, with content turning conversational, the potential for machine learning gets even stronger. Right now, virtual assistants like Alexa or Google Assistant lack context, essentially making the term “conversational UI” a misnomer. They are far more instructional than they are conversational, but applying content-driven machine learning to these systems will transform them into discovery mechanisms for the enterprise. Pretty soon, you’ll be able to ask Alexa what image you used in a blog post in June 2015, with the correct image appearing on your screen within seconds. As we inch closer to truly conversational content, we’ll reach a level of efficiency unlike ever before.
There’s no telling what type of content will flow through the enterprise next. But as content of all shapes and sizes becomes the playground for machine learning, productivity hacks that crunch words, rather than numbers, will start to prove their value.