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How AI is Changing the Way Organizations Manage Content

By Special Guest
Uri Kogan, VP, Product Marketing at Nuxeo
November 26, 2019

For too long, we have drawn an artificial line between content and data, as well as between unstructured and structured information. This approach has the unfortunate effect of relegating content to a secondary status – a business area that represents a complex problem to solve (or live with) rather than an opportunity to exploit.

Think about it. By most estimates, content – or, if you will, unstructured information – represents more than 80 percent of all information. More importantly, almost all human-generated information is content.

Content literally provides the basis for how the modern enterprise conducts its work. Content is how we communicate, and collaborate with one another. It informs most of our business decisions. It allows us to analyze large amounts of data. And, with the explosion of rich media, it’s also how we engage with and delight our customers. Simply put, content is indispensable to the functioning of the modern enterprise. Despite its importance, however, most organizations fail to leverage their content strategically. They are only concerned with how to create it, how to store it, how to deliver it or even how to “manage” it.

The reality is, very few organizations look at content as a true information asset. Why? For one thing, content is complex and difficult to manage. It also usually takes a human to interpret content and its relative value. But this is changing – fast.

Indeed, the advent of artificial intelligence (AI) and machine learning (ML) is crumbling the walls between content and data. We now have automated tools that let us better understand unstructured information, and extract valuable data, provide insight into its substance and import, and interpret the sentiment of its author. In short, we now have the unprecedented ability to enrich content. This new power, of course, makes it easier to quickly find, to readily reuse, to accurately deliver and even to intelligently mine information, providing valuable new sources of competitive advantage and support for business transformation for today’s enterprises.

AI in the Enterprise
AI is now creeping into the enterprise is various ways.  A few of the more obvious examples include robots, Echo, and chatbots. But what exactly does AI look like when applied to content management systems?

Some people mistake robotic process automation (RPA) for AI, but with RPA, every automated process must be explicitly programmed by a human. AI isn’t just “artificial automation” – it also must be intelligent.

That’s the difference between RPA and an AI-driven content services platform (CSP). Modern CSP systems are true AI because they actually can reason on their own. The systems are designed to learn and continually improve business  processes.

Taking it one step further, the leading AI frameworks also let you plug into custom business knowledge and train AI models to come back with analytics based on those specifics. The result: the data collected, and the insights provided, are more specific to your particular business and business process.

An Intelligent Update
AI frameworks can already enable broad integration with, and support for, various third-party AI engines like Google Vision, Amazon Rekognition, and Amazon Comprehend. Several enterprise content management (ECM) and digital asset management (DAM) vendors have made use of these tools to provide general tagging for images, auto-classification for content, and sentiment analysis for documents and communications. Often there is even support for tagging video content – an added bonus in our increasingly video-focused world.

The issue with most generic AI engines (like Google Vision) is… they’re generic. These tools can tell you what is in an image, and they excel at enriching content, but how much of the information is truly valuable to a specific business? Tags or labels are great for helping search for and retrieve content or assets, but you can’t really use tags to launch workflows or kick off specific business activities.

Contextual or business-specific AI, on the other hand, take analytics and content management to the next level. The key difference is that users can employ their own data to train AI models that are tailored to the unique needs of their business.

Why is this important? Imagine you show a picture of a truck to one of the generic AI engines. The system recognizes that the image is a truck; it’s got four wheels, it’s blue, and it’s a Ford that is parked by a building. The AI will do a reasonable job of categorizing and classifying that – interesting, but not all that useful.

If you’re Ford, you want to know more Ford-centric specifics. For example: what model of truck is it? What exact type of alloy wheels are on that truck? What specific paint code is that blue? This is the type of information needed for truly domain- and business-specific intelligence and automation.

The leading AI frameworks let you plug into that business knowledge and train the AI model to come back with those specifics. The result is that the data collected and the insights provided are more specific to your particular business and business process.

The Future is Now
Across many industries, AI is making groundbreaking changes to the way companies do business and interact with customers. Now, AI is becoming integral to the way we manage information and digital assets as well. But the future of AI in content management is more than just automating repetitive tasks. It involves enhancing metadata, delivering contextual/business specific insights and learning from business processes to make workflows more effective and efficient than ever. And in this era of big content, the knowledge we stand to gain by using the technology seems to have no bounds.

About the author: Uri Kogan leads go-to-market strategy and execution for Nuxeo. Before joining Nuxeo, Uri spent 8 years at HP in marketing leadership roles for digital experience technologies, launching new and transforming legacy businesses, and improving supply chain performance. Earlier in his career, Uri was an economic consultant to utility industries and the US Bureau of Labor Statistics.




Edited by Ken Briodagh


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