AI has the potential to transform modern business, and change just about every aspect of the way we do work. One area the technology is expected to have a significant impact is content management. In fact, as Nuxeo’s Uri Kogan explains, it is already.Â
Artificial Intelligence (AI) has so many applications within business and the wider world, it can be easy to let your focus be drawn to some of the more exciting use cases. While AI-powered robots undoubtedly sound impressive, and in many cases have huge potential, AI is arguably delivering more value to enterprises in other ways.
In the case of content-centric business applications, business-specific metadata is the foundation that drives effective search, workflow, and other value-creation activities. Until now, the challenge has been the manual effort and investment required to properly and accurately identify content and link it to related materials, so bringing AI to bear on this problem makes a lot of sense. Unfortunately, until recently, AI wasn’t quite up to the task. Traditional content enrichment AI services take one of two approaches. Some services are easy to deploy and provide generic metadata not based on business-specific content. Others allow custom model development, but require scarce data science expertise to use.
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But as AI services evolve and grow more sophisticated, businesses no longer need to make this trade-off. Modern Content Service Platforms (CSPs) enable more organizations to access contextual AI and use the technology to achieve meaningful transformation.
As a result, content management is a key area where AI is beginning to have a significant impact on business. By completing repetitive, mundane tasks more quickly, more efficiently and more accurately than ever, an AI-powered content management systemOpens a new window promises untold value for today’s enterprises. Let’s look at a few examples.
Metadata Enrichment
Metadata – the “information about information†– is a transformational area for content management. No longer is the plain-old document the star of the show, as in the days of document management or enterprise content management (ECM). In those earlier regimes, each stored document became the focal point for invoice processing, claims management, and other enterprise processes. Every one of those stored documents contained a small set of metadata attributes, or tags, typically limited to information such as filename, date created, author, and type of content. For most systems, once the metadata “schemas were defined, it usually remained untouched because changing metadata schemas required tedious development work and mass updates to all content related to that metadata.
In a modern Content Services Platform (CSP), on the other hand, metadata schemas are both flexible and extensible. This means you can readily add a new metadata field. In addition, much more metadata is being stored and used than ever before – image resolutions, language of a document, geophysical data, sentiment, and more. CSPs provide increased capability and the ability to utilize metadata much more effectively.
This is massively powerful when combined with AI, which can dramatically accelerate the creation and classification of metadata attributes. For example, let’s say you have an existing ECM repository containing customer agreements. These contracts are poorly managed, and the only relevant metadata attributes associated with these documents are customer reference numbers. By using an AI-infused CSP, an enterprise can identify and extract critical attributes at scale, such as project, customer contact, term period, etc. With this type of automation, additional security controls and provisions per privacy policies or regulations can also be enforced more expeditiously.
Classifying Content within Legacy Systems
Publicly available AI tools have already proven valuable in identifying basic and generic content attributes, such as the difference between a contract and a resume. However, AI models based on data and content specific to an organization can be immensely more valuable. So, for example, if your business needs to know the difference between a personal life insurance document and a life annuity document and automatically apply the right contract language from your legal team, this can be incorporated into a specifically trained AI model, which in turn will deliver a much more detailed classification than could ever be possible with generic AI.
Modern CSPs with such AI capabilities enable organizations apply this to the mass of content stored in any connected systems or repositories. By using AI-classification of content with a CSP, it is possible to quickly and accurately identify and organize different types of content, even at the scale of billions of pieces of content.
An AI Framework that Performs in Layers
Research indicates that 80% of the content commonly held in information repositories is “R.O.T†— redundant, obsolete, or trivial. AI can identify which 80% is R.O.T. and either get rid of it altogether or apply low-touch retention policies and practices to better manage that information. AI will quickly and effectively analyze documents to accurately determine which content should be kept, which should be archived or removed from systems of record entirely.
Amazon, Google and other companies have built advanced AI engines, but these tools are based on publicly available data sets, which means they can’t deliver results specific to a business.
Modern CSPs enable enterprises to leverage create custom AI models based on their own data sets, which deliver attributes that are much more specific to the business.
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 is the exact type of alloy wheels are on that truck? What is the specific paint code of 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. And then the result is that the data collected, and the insights provided are more specific to your particular business and business process.
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AI’s Future Potential for Content Management
We see that infusing content platforms with contextual AI makes it easier to find content by automating the creation of high-quality metadata. Moreover, pairing a CSP with AI-powered content classification lets us manage the exploding volume of metadata that most enterprises are struggling to organize and store. This powerful combination of capabilities – the modern CSP and AI – enhances how users can identify and organize different types of content – and does so at a scale never before possible. The effect is a radically increased utilization of metadata within the enterprise – an enhanced capability of incredible value to today’s modern enterprise. Basically, the modern CSP is poised to remedy legacy content management frustrations that have plagued business for years.
So, while self-driving cars and flying robots may be years off, the future of AI for content management is already here. It may be much more practical, but the possibilities the technology creates for business will create new opportunities we have yet to imagine.