Digital Labor | Course Paper

Capitalizing

on robotics

Driving savings

with digital labor

kpmg.com

1Capitalizing on robotics

Digital labor, a term that encompasses

robotic process automation, is the

application of software technology to

automate business processes ranging from

transactional swivel-chair activities to more

complex strategic undertakings.

For most organizations, it is no longer a question of if, but more

about when, where, and how fast they can apply digital labor

as a differentiator. Answers to those questions often rely on

understanding the financial investments required and setting

expectations on the timing and magnitude of the associated returns.

Understanding the investments and expected returns for digital labor

is complicated by the fact that no two automation opportunities are

the same—i.e., your mileage WILL vary. Furthermore, digital labor

can be categorized into three different classes—each requiring

different investments and providing returns—varying not only in

magnitude, but also in the drivers that impact those savings.

This paper suggests some answers to this financial puzzle by

identifying and exploring those financial considerations that are

common to all digital labor projects, as well as those that are more

specific to each of the three classes of digital labor.

If you have not yet started

implementing digital labor, it

is time to catch up with your

competitors. These days, it

is pretty much a given that

organizations will save money

when automating business

processes with digital labor. But

even for those organizations

that have begun the digital labor

journey, the answer to one

elusive question often remains—

am I saving all that I can? How

can organizations best position

themselves to capitalize on the

savings achievable through digital

labor implementations? This

paper identifies common savings

drivers for digital labor initiatives

in general, as well as identifying

those savings drivers that are

uniquely applicable to each of the

various classes of digital labor.

Before setting out on this digital labor

journey, it is beneficial to identify and address

concepts than can impact overall savings—the

foundational savings drivers:

1 Do we have clear executive sponsorship for the initiative?

2 Are we prepared for what could be a substantial impact

to our organizational structure—organizational change

management, training, etc.?

3 Are we thinking beyond the immediate? Do we have a

well-defined plan and strategy for labor automation—both

near term as well as long term?

4 Is there a defined approach to who and how we will

govern:

— The resulting automation capabilities?

— The automation initiatives moving forward?

— The changes in our business and/or technology?

5 Have we gained a basic consensus on mitigating security

and risk concerns?

As the journey begins, the considerations get

more tactical and detailed:

1 Have we limited our automations to a specific class of

digital labor that aligns both with the opportunities for

automation and the appetite for technology?

2 For each class of digital labor being leveraged, do we

understand the key characteristics and savings drivers?

3 Given the specific savings drivers, do we have a

strategic approach to identifying and prioritizing candidate

processes for automation?

4 What steps might we take to increase these savings

and/or accelerate the path to the savings?

5 When do we declare success and move on to the next

automation initiative?

While there may be a lot to consider when first venturing

into digital labor, it is these considerations that help provide

a more strategic approach to ensuring an organization is

appropriately benefiting from its digital labor initiatives. The

temptation is great to simply “jump in” and get started, and

indeed, taking action is far better than paralysis by analysis.

At the same time, the proper amount of up-front planning

will pay off in the long run, and digital labor, like any strategic

initiative, is better viewed as a journey rather than a race.

2Capitalizing on robotics

3Capitalizing on robotics

The spectrum of digital labor automation

When discussing the sources of savings from digital labor

automation, it is helpful to have a basic understanding of

the three primary types of automation. Each addresses

a different target opportunity and leverages tools with

differing capabilities.1

Processing of

Unstructured

Data and Base

Knowledge

01

02

03

Screen

Scraping

Work

Flow

Rules

Engines

Adaptive

Alteration

Machine

Learning

Natural

Language

Processing

Artificial

Intelligence

Large-scale

Processing

“Big Data”

Analytics

A

1 2 3

Basic Robotic Process

Automation (RPA)

Enhanced Process

Automation Cognitive Automation

– Macro-based applets

– Screen scrapping data collection

– Work flow

– Vision-type building blocks

– Process mapping

– Business Process Management

(BPM)

– Artificial Intelligence (AI)

– Teaching versus programming

– Natural language recognition and

processing

– Self-optimization/self-learning

– Digestion of super data sets

– Predictive analytics

– Hypothesis generation

– Evidence-based learning

– Built-in knowledge repository

– Learning capabilities (e.g.,

‘learning assist’ by watching and

recording)

– Ability to work with unstructured

data

– Pattern recognition

– Reading source data manuals

(e.g., Natural Language

Processing (NLP)

Classes of digital labor

1 “Demystifying Digital Labor – the Layman’s guide to the spectrum of robotics and automation,” David B. Kirk, June 2016,

http://www.kpmg-institutes.com/institutes/advisory-institute/articles/2016/06/demistifying-digital-labor.html

The spectrum of digital labor automation

Basic robotic process

automation

Class 1, which we refer to as “Basic

Robotic Process Automation,”

leverages several “tried and true”

technologies to automate rudimentary

swivel-chair processes found in

almost all organizations today. The

tools leverage capabilities such as

work flow, rules engines, and screen

scraping/data capture to automate

existing manual processes. An

ideal process candidate for basic

automation will have these types of

characteristics: repetitive in nature;

well-defined explicit activities that

are easily organized and sequenced;

Enhanced process

automation

Class 2, which we refer to as

“Enhanced Process Automation,”

leverages additional capabilities

to those discussed in Class 1 to

address automation of processes

that are less structured and often

more specialized. Tools and platforms

supporting Enhanced Process

Automation offer some combination

of capabilities such as “out-of-

the-box” (built-in) knowledge; an

understanding of natural language and

thereby the ability to consume and

leverage unstructured data (such as

e-mail, professional articles, etc.); an

Cognitive

automation

Class 3 is “Autonomic/Cognitive

Automation.” Cognitive systems

are systems that combine advanced

technologies such as natural language

processing, artificial intelligence,

machine learning, and data analytics

to mimic human activities such

as perceiving, inferring, gathering

evidence, hypothesizing, reasoning,

and interacting with human

counterparts. Cognitive systems are

“taught” rather than programmed

—a process that can take months to

years depending on the complexity of

the problem domain. These solutions

Class

1

Class

2

Class

3