cc 1  IDP is built on an AI-native platform

Critical Capability 1: IDP is built on an AI-native platform

by Mark Clark, on April 9, 2021 9:30:00 AM PDT

AI can solve big problems in the document intake process, but it needs the right foundation to succeed. That’s why knowing if the options you’re looking at are rooted in an AI-native platform is so important. To successfully implement and operate AI, you need a platform built for these technologies. Layering a few AI technologies onto a legacy OCR platform creates operational risks you don’t want.

Older OCR systems are typically not AI-native. As a result, they lack the capabilities to make full use of the AI technologies that you need to overcome the most persistent document processing problems.

Critical Capability 1: AI-Native Platform

An AI native platform allows an IDP to achieve AI’s full business value

Business Benefits -- improves accuracy, performance, agility, flexibility.

 

AI-native platforms harness AI’s power

Choosing an AI-native platform lets you harness AI’s power. Instead of wrestling with a fragile legacy system like OCR, AI-native platforms ensure that the solution works in an operational setting and can be supported throughout its lifecycle. These platforms are designed to get work done using AI.

An IDP solution built on an AI-native platform will be able to leverage the broadest set of available AI technologies to automate the most challenging document processing jobs. 

An AI-native platform, or an “AI orchestration platform” allows you to get the full business value from your AI technologies. Since it is purpose-built for AI, the platform allows you to successfully use your AI technologies to deploy, maintain, and scale intelligent systems like IDP.


Making the technology work for you

Leading IDP solutions integrate and orchestrate multiple AI technologies, sometimes running 15 or more AI models including machine learning, deep learning, natural language processing, and computer vision, to provide the highest levels of performance and accuracy. AI-native platforms are built to support this kind of AI orchestration.

It’s not just about the best technology; it's about making that technology work for you. Operating AI is not the same as running a software solution like OCR. AI-native platforms are designed to deploy, operate, scale and maintain AI technologies in business operations. 

Questions to ask

We recommend that you dig deep on proposed vendors and solutions to understand their architectures and operational environments, as well as how the AI technologies used will be orchestrated and supported.

Here are 5 good questions to ask a solution provider:

5 Questions to Ask A Vendor


  1. Is the IDP solution built on a platform using an AI-native architecture?
  2. Does the platform integrate multiple AI techniques––such as machine learning, deep learning, natural language processing, and computer vision––to accurately extract data from unstructured documents?
  3. How many AI models will be running when your solution is in operations? What type of algorithms will be used?
  4. How will the solution be operated and maintained to ensure the AI components are optimized?
  5. What are you doing to support AI’s specific needs?


Get the business benefit

An AI-native platform will deliver the highest extraction accuracy across a broader set of documents. It also helps ensure the AI technology works and can be maintained, as well as optimized, in an enterprise operational environment. You’ll automate more, have higher process performance, and reduce processing costs.

Example: Empire Company

Empire company deployed an intelligent document processing solution with the goal of automating data extraction from invoices that had nested tables. This level of document complexity required the solution to use multiple AI technologies. 

Unfortunately, Empire’s IDP solution had been built on a platform that did not fully support the AI technologies it used. While it was deployed into production with acceptable accuracy, it was never able to learn and improve its performance over time. 

That meant Empire was unable to automate its invoice process, creating a poor customer experience and driving processing costs through the roof.   Eventually, the solution was decommissioned, and the process reverted to manual data extraction. 


What does an AI-native architecture look like?

Problems occur when you try to layer AI into systems built for traditional software engineering approaches (e.g., OCR systems). Traditional systems carry technical debt that will create implementation and infrastructure complexity from trying to deploy an AI-based solution. 

Traditional software systems can have slow development cycles, brittle processes, monolithic platforms, rigid point-to-point interface dependencies, and duplicated data efforts.

Instead, you want an IDP solution with AI-native architecture that is a foundational element in the framework and operating model.

What will an AI-native IDP solution do differently?

Compared to a solution built for software or a solution that layers on AI as an adjacent component, an AI-native solution will unique capability.

Unique Capabilities of an AI-Native IDP Solution

Composite AI. It will use a composite AI approach, integrating different AI techniques to solve a problem with a single IDP solution.

Scalable. It will integrate digital technologies, compute infrastructure, and process workflows that support scalability.

High performance. It will leverage compute-accelerated AI platforms such as GPUs to deliver state-of-the-art AI capabilities and performance.

Adaptive and resilient.  It will continuously learn and adapt to changes in the business environment and process. 

Learn and improve. It will support AI learning and continuous improvement.


Why is an AI-native platform a critical capability?

Operationalizing AI is a challenge for any AI system, not just IDP.  

IDP isn’t worth the time, money, and effort if your organization can’t put it to good use. 

Yet many enterprises struggle with AI.  Gartner estimates-- based on inquiries and surveys-- that nearly 50% of AI projects don’t make it into production.

An AI-native platform helps make AI work. 

That is why we recommend you consider an AI-native platform as a critical capability. 

Key takeaways 

It is not enough for an IDP solution to provide high accuracy. You need a system that delivers business value and state-of-the-art AI capabilities. You need an AI-native platform that is built to:

Takeaways

Scale AI into production, for not just a few models, but a broad set of models an IDP system uses.

Provide operational resiliency and efficiency of production workflows, technology, and service delivery.

Be resilient and accept frequent changes in document, data, and model contexts.

Help minimize risk and technology complexity while maximizing team productivity.


An AI-native platform can help get the full value of your AI technology, so you can deploy it, operate it in a scalable, manageable way, and generate real value from AI.  It makes your process transformation possible.

This blog 2 of an 11-part series on critical capabilities for intelligent document processing solutions.

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Topics:Intelligent AutomationAI ReadinessHow ToIntelligent Document Processing

About this blog

AI can be a game-changer, but only if you know how to play the game. This blog is a practical guide to turning AI into real business value. Learn how to:

  • Make sense of complex documents and images.
  • Extract the data you need to drive intelligent process automation.
  • Apply AI to gain insights and knowledge from your business documents.

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