Composable and contextual AI will drive enterprise content automation to the next level – and the possibilities are endless, says Dr John Bates, CEO of SER Group.
Business process automation is moving well beyond simply automating routine back office processes. Traditional robotic process automation (RPA), based heavily on screen scraping and human mirroring, is being replaced by intelligent content and process automation technologies. As artificial intelligence (AI) becomes much more embedded in the organization and its processes, the focus will shift towards automating more complex processes, which are heavily content oriented, with more complex content understanding, systems integration and contextualization needs.
Content and process automation have begun to converge. Intelligent pattern recognition technology can detect when documents are invoices, for example. Based on algorithm/score-based rules the technology can identify elements of the document such as the company name or tax information. Having extracted the relevant data, intelligent process automation technology can then transfer the data into a database or trigger the next step in a workflow.
However, until recently, each time the system encountered a new document, it did not have the capability to apply any learning from how it had processed similar documents in the past. It was not able to match new orders to existing customer records or invoices to purchase orders. In other words, it wasn’t contributing to building a single view of the customer journey.
Now, the increasing integration of artificial intelligence and pattern recognition technology is taking process transformation to the next level, where it is possible to process documents automatically in a much broader context. And every time a document is processed, this contributes to a growing body of knowledge, building up re-usable information about customers and suppliers. This can be termed “contextual AI”.
A new dimension of AI
The combination of pattern recognition and contextual AI drives the potential for business process automation into another dimension. Not only can intelligent software recognize what a document is and extract relevant data, it can also understand where the documents sits in the wider business context. It understands which actions need to be taken in response to the document and data.
For example, the intelligent software could approve payment for an invoice from a recognized supplier, resulting in savings in managers’ time and faster payment for suppliers. Customer and employee data is another source of potential for contextual AI. Repeat orders might be rewarded with discounts. High numbers of sick days might flag up the need to step in and ensure an employee’s needs are being met, avoiding the expense of replacing a valuable employee.
Of course, customer relationship management (CRM) and enterprise resource planning (ERP) solutions promise many of these benefits. But these systems are only as good as the data put into them. And the failure to join the dots between data often means that the organization is not getting the whole story. For example, a regular customer may not be a good sales target currently if they owe you money, are in a legal dispute with you or have support issues with your IT team.
Open AI architecture
When AI is limited to a single application, and limited to a specific framework, not only is its full potential restricted, but the tech itself will likely be outdated soon. Software companies that promote single-application AI – as opposed to enterprise AI which bridges with ERP or CRM solutions – have often thrown in their lot with a particular AI/machine-learning framework such as Google TensorFlow, Microsoft Azure Cognitive Services, a Python-based framework, or specific capabilities for pattern matching/image recognition or natural language processing (BERT, ERNIE, etc). This restricts the potential for contextual intelligence.
What’s needed is AI-enabled content management that transcends a single application or department, based on an open architecture which supports AI across the enterprise. The goal is to be able to apply AI to new integrations and business challenges for years to come.
The key to achieving that goal is to design an open architecture, which will be able to apply AI in different ways as the technology continues to evolve. As intelligent automation replaces basic RPA-based automations, cross-enterprise integration and contextualization will become increasingly important to exploit the potential of AI for content automation.
Human and digital workforce
Organizations are having to adapt quickly to new ways of working and rise to meet challenges ranging from recruitment to economic shocks. The combination of human workforce issues and intelligent software evolution is creating ideal conditions for AI to work hand-in-hand with humans to build and carry out enhanced, contextual and automated business processes.
Artificial intelligence, intelligent document processing and robotic process automation are fast converging. Layering contextual intelligence on business process automation is the next step for organizations. Integrating intelligent processes that learn as they go into a wider enterprise context is an evolution which will deliver the promised business benefits of content and process automation.
About the Author
Dr. John Bates is SER Group’s big-hitting new CEO, a tech visionary, automation expert and experienced CxO with a PhD in computer engineering from Cambridge University. SER Group is a leader in Intelligent Information Management, headquartered in Bonn, Germany.