Contextual AI holds the key to its business value

Today, content management systems or services that claim to use AI usually have the memory of a goldfish. That is, they apply image or shape recognition very well, but beyond that, the value of applying AI is very limited. As each document is scanned and its content is “read” and entered into the target system (for example details of invoices in the relevant accounting system), this knowledge is treated as new: there is often little or no contextualisation. The documents and their contents are not intelligently linked to those residing in or extracted from other documents. For example, a purchase order is not automatically linked to a customer record in the CRM system, to provide new buyer information.

There is no doubt that automating processes at this basic level offers decent value in terms of time and cost savings, as well as improved customer services. But the next step in digital business transformation, one that will provide true competitive advantage, is the smart integration of content management more intrinsically within the broader business ecosystem. When intelligent connections are made between documents, data and systems across different functions, it enables more efficient decision-making and can help streamline more complex processes.

Beyond Image and Shape Recognition

AI-enabled single-process use cases, such as intelligent order or invoice automation solutions, are now ripe for integration with other enterprise systems. It’s about combining intelligent learning software with advanced content management to build synthesized institutional knowledge – so-called “contextual AI”. Here, each document and the data it contains can contribute to an intelligent knowledge bank that provides a holistic view of the customer, supplier, product development process – or any other strategic objective.

Next-generation content management platforms and services apply AI in new and smarter ways. Once the technology identifies the type of document, it is able to process that information by entering data into a database or triggering the next step in a workflow.

Take the ability to increase and transform the impact of customer relationship management (CRM) systems, driving more focused sales efforts and better customer service. CRM systems have long promised a “360 degree” account view (much like supplier management systems do in supply chain management). Yet these potentially very powerful systems are only as effective as the information fed into them, which until now has relied on the respective teams building this picture of everything they know about a given account. That is, additional knowledge from accounts, etc., is not fed into or made available to these systems automatically, through direct, intelligent sharing of information across the enterprise. And this failure to join the dots leads to inefficiency. For example, a sales rep won’t automatically be able to see that a customer has asked the support team for new features, which could be addressed in a new sale. They are thus missing out on an income opportunity by living in their local information silo.

With contextualised, enterprise-wide, AI-powered content automation, the scope for business process transformation and new efficiencies increases dramatically, for example through bypassing protracted processes based on trusted information on new accounts. An invoice from a recognized supplier that is identified as matching a purchase order on the system, for example, could now trigger a payment without needing manager approval, saving time and effort. money while giving loyal suppliers a better experience.


There’s another important consideration in all of this, and it’s building from the perspective of continuing to add more value over time as AI technology develops.

Adopting single-app AI is less likely to provide this path. A software application with “Built-in AI” will be locked into a specific AI framework such as Google TensorFlow, Microsoft Azure Cognitive Services, a Python-based framework, or tied to specific capabilities for pattern matching/image recognition or for natural language processing (BERT, ERNIE, etc.).

Cabling in a single technology approach is a risky decision given how quickly technology evolves and changes. A better approach would be to adopt an open content architecture, which supports any combination of current and future AI options, on a “composable” basis. In this scenario, companies will over time be able to continue to connect intelligent systems in different ways that do not depend on the specific embedded AI of individual applications.

None of this is to say that the role and value added by Human teams has diminished in any way. But given the extent to which the Great Resignation and hybrid work have increased pressure on roles and skill retention, it follows that smarter automation delivers significant value by empowering each team member to truly excel, supported by richer information that has been automatically gathered. and in a timely manner, by AI-enabled, business-to-business content services.

About the Author

Dr. John Bates is the new CEO of SER Group, a technology visionary, automation expert and experienced CxO with a PhD in Computer Engineering from the University of Cambridge. RES Group is a leader in intelligent information management, headquartered in Bonn, Germany.

Norma A. Roth