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January 27 2022

3 min read

What’s Next for Intelligent Document Processing

2021 was a big year for the automation industry and the Intelligent Document Processing (IDP) category. As a product marketer with 8 years in the intelligent automation space, it is incredibly exciting to be part of one of the fastest growing software markets in history – and 2022 doesn’t show any signs of slowing down.

From Cloud giants to tiny startups, to legacy providers and big consultancy firms, to new AI-led solutions, Gartner’s Competitive Landscape Report on IDP Vendors now includes over 40 solutions.

The new breed of AI-driven solutions in particular continue to disrupt the space, while taking automation and accuracy rates to the next level.

Here are my top 3 predictions for how the industry will evolve in 2022:

  1. From Intelligent Document Processing to Intelligent Content Processing

As automation initiatives mature across organizations, they will continue evolving from a single, basic task automation into a more ambitious approach, where the goal is to automate entire processes from start to finish. In this case, parts of a process may depend on data contained and extracted from a complex document (like a legal contract), or the intent of an email, or even key insights buried in hours of recordings.

Intelligent Document Processing (IDP) providers will continue expanding their capabilities beyond structured and semi-structured documents to allow enterprises to extract data insights from fully unstructured documents to support the automation of increasingly complex processes.

According to Gartner, IDP will evolve into broader content processing over the next two years. In this next stage, solutions will grow beyond document-heavy processes to process data contained in audio clips, videos, and more.

  1. The rise of responsible AI

Traditionally, digital ethics has always been an afterthought. But the new wave of AI-led software providers are destined to guide organizations and society on how to regulate and navigate AI and related technologies as the benefits become accessible to everyone.

It is not enough to deliver only the technological capabilities of AI – software vendors have an important responsibility to ensure that AI is human centered and socially beneficial, fair, safe and inclusive for all. Successful organizations will establish ethics advisory boards and committees that will work with stakeholders to reach agreements about relevant and actionable AI ethics guidelines.

The ethical AI guidelines that most technology vendors, governments, and industry groups include in their publications today are:

  • Be human centered and socially beneficial. AI solutions should be designed to support human goals and objectives, with human and society needs in mind.
  • Be fair and accountable. Fairness should be a principle in every interaction; be impartial, avoid any type of discrimination, and eliminate undesirable bias.
  • Offer explainability and transparency. Technology vendors need to ensure AI decisions are explainable and customers control their own data models
  • Be secure and safe. AI solutions should be implemented in a way that the data used is secure to protect people’s privacy.
  1. Alignment of automation and data strategies

The next key product and service opportunity for automation software will be combining an organization’s data strategies with its process and task automation initiatives.

When you think about IDP, the intelligent part shouldn’t just be extracting the data; it is also the ability to model the data flexibly and the ability to put it into context with the rest of the data in the organization – like humans do. This allows the data to be applied to, and to better inform, a variety of use cases across an organization.

Organizations looking to unlock the potential hidden in data extracted from documents and emails will explore the combined use of IDP with technologies like a Knowledge Graph. These technologies provide an entity-centric view that supports knowledge management. As a result, subject matter experts in different domains can become citizen data scientists and influence the way that the data is extracted, modeled, and visualized – with interfaces or workspaces that adapt to them, and not the other way around. This will create the opportunity to involve more employees across the enterprise in key digital transformation initiatives.

Over the next twelve months, as the automation landscape continues accelerating towards these three predictions, the companies who prioritize and expand their automation journeys will be the ones who remain resilient in the face of changing market conditions or a future crisis. The most successful companies of the future are the ones enabling greater human and machine collaboration across their organization to transform operations – to the benefit of customers and employees alike.

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