Process Automation for Unstructured Content

Process Automation for Unstructured Content

2 | Copyright © 2019 Indico

• Boston-based Enterprise AI

solution for process automation

founded 2013

• Unique deep-learning based

technology solving key barriers to

enterprise AI adoption

• Focus on “understanding” text,

image, documents and other

unstructured content

• Named to 2018 Gartner Cool

Vendors in September

About Indico


Agenda

What’s the Problem?

Traditional Approaches & Challenges

Introduction to AI for unstructured content

Re-Imagining document workflows

Conclusions


The Unstructured Content Challenge

Unstructured content in the

enterprise represents

unrealized opportunity &

unquantified risk…


The Trouble with Text

Context, not keywords, drives meaning


And then there’s this…


How it usually gets “solved”…

ManualHighly manual processes

involving many people

InconsistentIndividual understanding of

process rather than institutional

understanding

ExpensiveExcessive resources and

financial risk embedded

in process


The Goal

Decreased Cycle TimesEnhance internal and external

stakeholder experience

Redeployed ResourcesRedeploy critical resources to

higher-value opportunities

Increased EfficiencyDrive throughput and capture profitable

growth opportunities

8 | Copyright © 2019 Indico - Confidential

Decreased RiskConsistent & auditable workflows

with comprehensive “explainability”


Automation is Entering its Third Wave

Business Process

Management

Robotic Process

Automation

Intelligent Process

Automation

INPUT Structured Data Structured Data Unstructured Data

AUTOMATION

APPROACHProcess Models Task Models

Algorithmic/

Deep Learning Models

OUTPUT Deterministic Deterministic Probabilistic

VALUE

PROPOSITIONProcess Efficiency Digital Labor Arbitrage

Transparent and

scalable decisioning


AI & The “Automation Imperative”

10

“Across regions and industries, the survey results suggest that automating businesses is a global phenomenon.”

-

“The Automation Imperative”Sep 2018

AI Doubles the Success Rate of Automation Initiatives


Traditional Approaches

• Intense, time-consuming and

expensive manual effort

• Requires capturing all edge cases

• Brittle, requires endless

maintenance

Rule Engines



• Assumes forms are

homogeneous

• Variances in layout

breaks OCR rules

• Intensive manual

setup effort

• Inability to handle

scanned PDFsOCR



• Rigid representation of

language-parts of speech,

entities etc

• Requires extensive

dictionary-almost always a

pre-trained approach

• Must be endlessly “tuned”

to account for anomalies is

the data NLP


New Approach: AI?

Huge

Training Sets

Expensive

Hardware

Data Science

Expertise

Data

Leakage


Some Definitions

Machine Learning –

A field of computer science

that focuses on “teaching”

machines to make

decisions and

determinations based on

data rather than relying on

explicit programming


Some Definitions

Deep Learning –

A set of machine learning

algorithms that have become

increasing popular in recent

years due to their near-human

levels of performance for tasks

involved unstructured data –

primarily text, image, and audio

data.


Some Definitions

Transfer Learning –

A deep learning method

where a model

developed for a task is

reused as the starting

point for a model on a

second task.


We have had to understand the computer…


The computer learns to understand us…


Keys to Success: Labeled Data

1. Data

• Data prep is single most

important aspect of ML

• Labeled data that

sufficiently captures the

desired outcome is critical


Keys to Success: The SME

2. Expertise

• Subject matter experts

who understand the

business problem

• Some level of data

science understanding to

interpret and refine

approach


Keys to Success: Defining the Outcome

3. Definition of Success & ROI

Hypothesis

• A defined outcome (with consensus

across stakeholders) connected to a

tangible business benefit

• Ability to define the costs of the

current process- both hard costs and

opportunity costs

• Patience- several iterations will be

required. Scientific method.


Example AI-Based Approach to Unstructured Content

DATA LABELING

Inbound

Forms

Custom Extraction

Model

Custom Classification/

Transformation Model

Value 1

Value 2

Value 3Elements

B CA

DATA LABELING MANUAL REVIEW(AS NEEDED)

Structured

Output

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Conclusions

• Successful construct for AI-driven process automation is

“scaled decisioning”

• Pick a use case where there are heterogeneous inputs and

homogeneous outputs

• Capturing the understanding of the content and the process

directly from the SME increases probability of success

• AI can sometimes feel like a “black box”-pick an

approach/solution that includes explainabilty

Thank You

TOM WILDE | CEO

[email protected]

+1 781 985 4163

mailto:[email protected]

Documents

Process Automation for Unstructured Content