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Point of View


THE NEXT AUTOMATION FRONTIER IS UNSTRUCTURED DATA

December 1, 2022
 * 
 * 
   
 * 

The automation market is changing quickly. Yet, what is the context for all
those automation discussions? Through which lens should we look at automation?
Is it robotic process automation (RPA)? Is it an extension or evolution of RPA?
Or do we have to look through a fundamentally different lens at all those
discussions? HFS has repeatedly stated that automation is not your strategy. It
is the necessary discipline to ensure your processes provide the data—at
speed—to achieve your business outcomes. Against this background, it was
refreshing to discuss these issues with a provider with a fundamentally
different lens. Automation Hero puts artificial intelligence (AI) and
unstructured data at the heart of its strategy with the potential helping
enterprises finally make broad automation progress.

FOCUSING ITS AUTOMATION ON AI AND UNSTRUCTURED DATA DIFFERENTIATES IT FROM RPA

Automation Hero, headquartered in San Francisco, flips the automation
discussions on its head. In its view, the intent is not to expand automation
technologies such as RPA or runbooks with a plethora of adjacent technologies
but to automate as many process steps with AI as possible, from data entry to
reaching out to customers at the perfect time. It developed its Hero Platform_
as a new business operating system combining fast data, AI, and business process
management capabilities to form an intelligent business automation platform
optimized for workloads with an AI-centric mindset. Automation Hero’s approach
is about ingesting data and using AI to enhance its value.

Much of the RPA-centric automation discussions focus on automating structured
data, such as invoices, where AI modules help to identify known items, such as
addresses or billing information. Unstructured data is information that either
does not have a pre-defined data model or is not organized in a pre-defined
manner. That includes obvious examples such as email. But again, the
differentiation of Automation Hero does not lie so much in IDP use cases, but
broader data capabilities. Real-time data monitoring and meta data scanning
stand out in that regard. Therefore, it can be challenging to store that data in
databases. Here, Automation Hero’s AI capabilities come to the fore.

TRAINING DEEP LEARNING MODELS IS AT THE HEART OF AUTOMATION HERO’S APPROACH

Automation Hero’s approach focuses on a set of AI capabilities. Most are
pre-trained models that can be either configured (no training) or advanced
(train additional neural network layers with domain knowledge). Hero Platform_
tightly integrates with Google TensorFlow to train and execute a wide range of
deep learning models. Existing models can be installed and used as a function in
any data or business process workflow and take advantage of the enterprise
readiness of the platform. The Hero Platform_ can train pre-defined (e.g., in
Python) models, or its own AI design studio can create deep learning models. The
platform contains three advanced deep learning-based engines:

 * Context Aware OCR: Advanced OCR for structured, semi-structured, and
   unstructured documents with high accuracy.
 * Recommendation Engine: Recommendation engine for cross and upsell, churn
   prediction, and best next-step suggestions.
 * Classification and Intent-detection Engine: Identifies the intent of written
   communication, including emails, text messages, or online forms and posts.
   This engine can also classify documents or emails and route them or apply
   specific extraction strategies.
 * Dark Data Extraction Engine: Can be trained to extract structured data from
   unstructured data. It can extract names, job titles, phone numbers, and
   address or product data from text conversations. It also can be extended to
   first convert (e.g., image data to text) and then extract meaningful
   structured data. The latest version of the extraction engine does not need to
   be trained but can be configured with natural language questions, which gives
   Automation Hero a strong differentiation.

IDP IS A LANDING PATCH THAT IS ACCESSIBLE BY THE BROADER MARKET

Refreshingly, Automation Hero is largely staying clear of trying to bandy around
monikers such as RPA and intelligent document processing (IDP) to gain
visibility or traction. However, it uses IDP as a pragmatic way to achieve two
things. First, it is a landing patch with clients to demonstrate its
capabilities in a “low-level” use case. Second, once clients have trained their
staff and captured value from their investment, upselling to much more complex
scenarios becomes easier. All too often, AI approaches that go beyond just
machine learning provide a barrier to entry through perception issues, a lack of
talent, or insufficient training data.

Typical use cases are claims processing with strong traction within insurance.
The other cluster of use cases is around supply chain optimization. For some
clients, the platform gets trained to find POs even if they are not in an
invoice but in other relevant documents. Furthermore, the platform can extract
billing data from non-standardized orders, even for highly specific items. The
ability of the platform to understand the context and receive that information
in a knowledge graph is crucial. This ability allows for a non-linear flow of
information that is conducive to the vast amount of unstructured data and the
complexity of cloud-native operations. Automation Hero creates a knowledge graph
by connecting a data source with historical data, for instance, ERP or CRM.
Then, it internally builds a graph to improve OCR accuracy, classification, or
other AI models.

THE GOAL IS AN OPERATING MODEL FOR BUSINESS PROCESSES

Automation Hero’s goal of evolving toward an operating model for business
processes is ambitious. Yet, as Exhibit 1 outlines, it is a compelling example
of the evolution of automation where process orchestration and automation
enhance workflows, and process intelligence drives the execution of data and
insights. Thus, Automation Hero’s competitors are not just IDP providers but the
new cool kids on the block, like ServiceNow, Workato, Soroco, and Turbotic. The
differentiation will come by demonstrating capturing value and progressing
toward end-to-end automation. The battle lines are drawn here.

EXHIBIT 1: AUTOMATION HERO AIMS TO EVOLVE INTO AN OPERATING SYSTEM FOR BUSINESS
PROCESSES



Source: HFS Research, 2022

THE BOTTOM LINE: THE EFFECTIVE INGESTION OF UNSTRUCTURED DATA WILL DRIVE MORE
VALUE IN AUTOMATION DEPLOYMENTS.

Markets are won by sales and marketing, not capabilities. Thus, Automation Hero
must invest in marketing to highlight its differentiated capabilities. Part of
that is developing a partner model that focuses on sales and customer success in
equal measure. Enterprise executives should evaluate the platform’s
effectiveness while comparing it to IDP and much broader data ingestion. The
value proposition is to provide actions on insights. The next battleground is
unstructured data, not just the digitization of structured information.

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