Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

How AI Can Turn Content into a True Information Asset

NuxeoWhile content represents more than 80% of all enterprise information, too many organizations struggle to manage it effectively. Part of this challenge is because of the complexity of content, and because humans have traditionally been required to interpret it and gauge its value.

But the rise of Artificial Intelligence (AI) means that extracting value and insight from content has become much easier. Previously, AI use cases within information management mostly focused on either simple classification of content as part of the capture or ingestion process, or as a more advanced, learning-based version of optical character recognition (OCR).

These work well but miss a huge opportunity, which is to use AI on the mass of content and data that already exists within the organization (in the ‘digital landfill’) by enriching it, making it easier to locate, and getting more business value from it.

Read More: Cryptocurrency Tax Returns and the IRS

Here are four ways in which AI can turn content into a true information asset.

More effective use of metadata

One of the most important and powerful types of information is metadata – or information about information. Back in the days of document management, each file had a set of metadata attributes (or tags) associated with it, but these were fixed and limited in number.

Changing these metadata schemas required a lot of development work along with mass updates to all content related to that metadata. However, metadata schemas in a modern Content Services Platform (CSP) are flexible and extensible. In addition, much more metadata is being stored and used than ever before – image resolutions, the language of a document, geophysical data, and more.

This increased capability and the ability to utilize metadata much more effectively is a distinct benefit of a modern CSP over old-school document management or ECM solution. But what about the content that many enterprises already have stored in those legacy solutions?

Another powerful feature one would expect from a leading CSP is that it can connect to content from other systems (modern or legacy, on-premises or cloud), leaving the content itself in-place but providing access to that content from the CSP. It also provides the ability to enrich legacy content with modern metadata schemas from the CSP – effectively allowing organizations to add metadata properties to legacy content without making any changes to the legacy system itself.

By using a CSP to pass content through an AI enrichment engine, users can potentially append additional metadata attributes to each and every one of the files currently stored. This immediately injects more context, intelligence, and insight into an information management ecosystem.

Read More: Smart Managers Know There’s More to Leading a Team Than Simply Being a Boss

Fast and accurate identification of the content

Related Posts
1 of 474

A key part of enriching metadata is that ability to ascertain ‘what is what.’ There are many uses for this, including being able to identify a file as a presentation, brochure, contract, invoice, etc. But beyond that, almost every industry has particular compliance regulations that require different types of documents and records to be kept for a specific period of time – retention policies or rules. There were typically two ways to do this in the past – manually, or not at all. The manual approach was tedious, error-prone, and time-consuming – which led to a lot of organizations adopting the ‘not at all’ approach.

But by using an AI-driven engine to classify content stored within legacy systems, this becomes much easier to do. Even simple AI tools can identify the difference between a contract and a resume, but advanced engines expand this principle to build AI models based on content specific to an organization. These will deliver much more detailed classifications than could ever be possible with generic classification.

Read More: AI in Cybersecurity: Applications in Various Fields

Taking out the rubbish

The ‘keep it all just in case’ approach not only exacerbated the digital landfill effect, but it also meant that a lot of information that could (and often should) have been destroyed, was not. Aside from the cost of having to store this content ad-infinitum, there are significant legal issues that arise from keeping information longer than necessary.

AI can be used to help mitigate this problem significantly. Part of the challenge of managing records, or even simply applying retention policies, is the sheer volume of content that needs to be managed. In the past, the only way to go through this was documented by the document.

By using AI-classification of content with a CSP, it is possible at a massive scale to quickly and easily determine what is not a record. According to numerous research studies, the significant majority of content stored is ROT (redundant, trivial or obsolete) – so by clearing out huge chunks of that ROT, the task of identifying relevant content to apply retention policies to become much, much easier. AI can then be used on the remaining content to identify the type of content in more detail, match that to the retention rules, and then make recommendations to the relevant staff members.

Read More: Top 5 Wearable Tech Trends in 2019

Fully trained, custom AI models…built with your own data

But what really makes a difference when using AI in this context is the ability to train and deploy your own custom AI models. When an organization works with its own data to train AI models, it means the AI engine can provide more accurate data about the document or asset and as a result, apply metadata that is completely tailored to the needs and nuances of their business.

Metadata attributes allow a user to help find and retrieve content, but automated entity extraction offers much more — by delivering more attributes, with greater accuracy, and at a faster pace. This drives applications such as automated image and content capture; the automated launch of workflows and related business processes; even associating new content or assets with pending tasks or work assignments.

To deploy machine-learning models and to train them using your own specific data sets is a powerful proposition indeed. It takes AI in information management to the next level and turns content into a true information asset.

Read More: Types of Medical Wearable Devices and 3 Best Apps for Them

Leave A Reply

Your email address will not be published.