Artificial Intelligence in Content Services | Documation Lecture Summary

Artificial Intelligence in Content Services | Documation Lecture Summary

I was recently giving a lecture at Documation, an exhibition held in France Last week. The following is a blog post summarizing the main points with regards to AI and Content Services.

From Enterprise Content Management to Content Services. What is this change all about? Does this affect the organizations associated with a shift to ECM?

As Gartner stated in their report during December 2016, Reinventing ECM: Introducing Content Services Platforms and Applications, content services are “a set of services and micro services, embodied either as an integrated product suite or as separate applications that share common APIs and repositories, to exploit diverse content types and to serve multiple constituencies and numerous use cases across an organization.”

While ECM relied on one centralized platform to achieve a vast range of operational goals, content services relies on multiple tools and strategies to get the job done. It is no longer about simply “storing” the content for organizations, but it’s more about making use of his content to gain the needed insights. The changes can be simplified with the following features:

  • Multi user editing
  • Discover and Predictive
  • Metadata Driven
  • Predictive Analytics

Artificial Intelligence

The stimulation of human intelligence by machines in order to recognize, learn and correct, with the ability to distinguish voice and vision. Examples of technologies that enable AI to solve business problems are robotics and autonomous vehicles, computervision, language, virtual agents, and machine learning.


Machine Learning…

Most recent advances in AI have been achieved by applying machine learning to very large data sets. Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time.

7 steps of Machine Learning:

  1. Gathering Data
  2. Preparing that Data
  3. Choosing a Model
  4. Training
  5. Evaluation
  6. Hyper parameter Tuning
  7. Prediction

With deep learning – type of machine learning – a wider range of data resources can be processed. Requires less data preprocessing by humans, and can often produce more accurate results than traditional machine-learning approaches. The network can then make a determination about the data, learn if its determination is correct, and use what it has learned to make determinations about new data. For example, once it learns what an object looks like, it can recognize the object in a new image.

What is driving this interest in AI research and Technologies? 

  1. Explosion of data in all its forms has consumers and businesses scrambling to find cost-effective solutions that enable them to intelligently find the critical data they need to make smart decisions.
  2. Recent tough economic times have taught organizations to run leaner and to seek out process improvements and innovative technologies..
  3. The computational power needed for AI algorithms is significant, and computers have only recently become capable of processing these intense AI calculations

Advantage of Artificial Intelligence and Content Services


You can gain value and insight from your data stores, and leverage the benefits of AI-powered analytics for faster decision making and task automation.

You can leverage machine learning to unlock the value ECM– for example, understand and analyze customers, trading partners, employees, orders, invoices, cases, documents and other data managed by your CS solutions.

Metadata = Context = Meaning = Value

Content cleanup that is professionally done and invisible—driven by AI. AI is able to improve the situation and, at the very least, prevent this syndrome from getting any worse.

It is also possible with tools that migration and federation vendors deploy to analyze your existing content stores, identifying what is old, duplicate, unused, or just plain junk

Ex: a typical business process that consolidates invoices with statements, purchase orders, and delivery notes. Even in small organizations, such an exercise can produce dozens of duplicate copies via email exchanges. Using an AI-driven robotic process automation (RPA) tool, organizations can automate the collection and pairing of multiple related documents. This is a process improvement approach, but a side benefit is a dramatic reduction in duplication and sprawl.

Artificial Intelligence and Machine Learning have been used for a long time in enterprise applications but their usage has really been for really complicated scenarios such as enterprise search (e.g., for proximity, sounds etc) or sentiment analysis of social media content. But it has never been easy to use machine learning for relatively simpler use cases. Additionally, no vendor provided any SDKs or APIs using which you could use machine learning on your own for your specific use cases.

Things are changing Specifically for ECM, you apply machine learning algorithms to find related documents, classify them, do content analysis and analyze patterns.

You can also course create your own machine learning programs using Python, R or Scala.

Check here our content services platform.