How Digital Transformation Starts with Automating the Mundane

11Dec - by aiuniverse - 1 - In Artificial Intelligence Machine Learning

Source –

Digital transformation, the notion of harnessing advances in data science to create value, is being reimagined through applications of artificial intelligence (AI). Focusing AI on the field of intelligent content analytics, the world’s largest banks, technology companies and life sciences organizations are taking it out of the lab and putting it into the hands of business to reduce risk, maintain compliance, increase profitability and optimize faster.

While AI is not—at least yet—a precise augur for predicting the unknown, companies now use advanced AI techniques such as machine learning to cogently search, codify, reveal and extract the hidden economic and operational data that is contained within hundreds of thousands of documents, especially contracts. A system can even be designed to teach itself once it has enough examples.

This use of AI to automate traditionally mundane, manual work and reveal future, actionable insights is where it is making perhaps its most remarkable impact. In one survey of professionals from 14 different sectors, conducted by employment specialist Emolument, jobs in the legal profession were voted the dullest in the world with 8 of 10 legal pros indicating that they are are bored. Similarly, 60 to 70 percent of experts in financial services, banking, sales and operations indicated that their jobs are tedious and unexciting.

What they all have in common is the dull repetitiveness of document review such as researching cases and rulings, reading contracts, and analyzing impenetrable blocks of text. So, let’s get those paralegals, operational experts and procurement specialists riveted to their work by helping them eliminate monotony and reveal the future using AI.

At the fundamental level, for example, data from sell-side contracts can be exposed to highlight which customer contracts have volume pricing language, which are coming up for renewal, which have license limitations and so on. On a more enhanced level, combining contractual data with data from other enterprise applications can reveal significant opportunities for cross-sell and up-sell.

Doing this with thousands of contracts has been well-nigh impossible in the past, and secondary to ensuring compliance and risk mitigation. But the latest capabilities of intelligent content analytics (ICA) technology, driven by AI, is giving birth to real digital transformation of the enterprise.

So, what exactly is intelligent content analytics? According to Jim Lundy, lead analyst of Silicon Valley-based Aragon Research, ICA refers to the use of analytics to derive insights from content “where the text or a higher-level abstraction of meaning—called a concept—has been organized in a model that can be mechanically processed.” He sees this as a ‘third era’ in unstructured content, where the focus has shifted from storing and tracking data to extracting and analyzing it.

This shift evolves from what has traditionally been called enterprise content management (ECM), which has been around for nearly two decades but is no longer sufficient. Web content management, document management and digital asset management morphed into singular enterprise-wide platforms to handle nearly all types of unstructured content. Mostly they had a bias, with some focused on the regulatory nature of critical content with capabilities like auditing, workflow and security as their strengths, while others focused on managing website content and the creation, workflow and approval processes around it.

ECM has universally relied on metadata to categorize content and understand the relationships between unstructured data in documents, and to describe the main features of the content objects themselves. However, ECM systems provided extremely limited intelligence into what these content objects contain, and virtually no level of analysis was being applied to them. That is, what the words in the documents said, what the sentiment might be, and what those words meant for the owner of those objects.

With the development of AI and machine learning models, and the massive recent improvements in computer processing power, it is now feasible to apply analytics to hundreds of thousands of pieces of content in parallel, and to extract the cognizant information hidden inside.

And, crucially—and this is key—it is now possible to derive insight from them which leads to better decision-making, risk mitigation and opportunity-taking. This is something ECM systems were never designed to do, and not unsurprisingly, documents such as contracts have emerged as the obvious place to start for this level of deep analysis.

There is a lot at stake. For instance, contract documents very often contain legally enforceable clauses that may pose both risk and opportunity for one or more contracting parties. They could contain revenue-generating opportunities, such as negotiated pricing agreements, or risk in terms of obligations associated with a data breach.

This is why gaining insight into contracts is proving to be the perfect foundation for the even bigger market of content analytics where, with technologies like SAP HANA and Hadoop, ICA providers will emerge to do the same thing for unstructured data that business intelligence tools have done for structured data at a massive scale.

We are now poised to give key unstructured content objects, such as insurance claim forms, medical records, marketing content, and financial documents, the same attention with deep analysis that contracts have enjoyed over the last five or six years.

And much to the relief of those employed in the dullest professions in the world, automating the mundane using advanced techniques in AI is not only useful for revealing the future, but it also makes their jobs a lot more interesting.

Facebook Comments

Comments are closed.