Dispelling the myths surrounding AI and machine learning’s ability to automate and augment business processes reveals a wealth of potential on the not-too-distant horizon

1. 1Unleashing the Power of AI for Enterprise Automation
Unleashing
the Power of AI
for Enterprise
Automation
Dispelling the myths surrounding machine
learning’s ability to automate and augment
business processes reveals a wealth of potential
on the not-too-distant horizon.

2. 1Unleashing the Power of AI for Enterprise Automation
Artificial intelligence (AI) and machine learning are making rapid progress with consumers,
gaining a nearly ubiquitous presence in our daily lives. In the past three years alone, Amazon
has sold 10 million Echo smart speakers, and Apple’s new iPhone X has an AI chip dedicated
to handling tasks such as facial recognition and understanding voice commands.
And yet, AI is not nearly as pervasive for automating and enhancing business processes.
Although an Oxford study says AI could automate more than 50 percent of all jobs over the
next two decades, many executives—perhaps with perspectives clouded by media hype and
misinformation—doubt the potential impact of this burgeoning technology.
In this paper, we provide a practical fact-based view of the AI trends and implications for enter-
prise automation over the next five to seven years. Based on our work with a wide range of
companies and discussions with academia, start-ups, and enterprise users, we highlight four
important AI facts, discuss the implications, and offer strategies on how to get started.
Fact 1: The AI Boom Is Sustainable and Should
Not Be Ignored
For the first time, machine-learning algorithms are beating humans in tasks such as image
recognition and voice-to-text translation, and complex games such as Go. This AI boom is fueled
by a convergence of three factors: a breakthrough in deep-learning algorithms, the proliferation
of big data (structured data) to train these algorithms, and an exponential speedup in processing
power for machine-learning hardware, such as the graphics processing unit (GPU) chipsets that
cut down a machine’s training time from months to days and hours (see figure 1).
Notes: GPU is graphics processing unit. Patents are those filed with the US Patent and Trademark Office.
Sources: Cisco, Tractica, Nextbigfuture, Teqmine Analytics; A.T. Kearney analysis
Figure 1
Three trends are fueling the artificial intelligence boom
Proliferation of big data Faster processing power Advanced learning algorithms
Global data center IP traffic
(zettabytes per year)
2020f2019f2018f2017e20162015
15.3
12.9
10.8
8.6
6.5
4.7
+26.6%
• By 2020, 92% of workloads will
be processed by cloud-based
data centers
• By 2020, 68% of workloads
processed by cloud-based data
centers will be processed by
public cloud data centers and
32% by private cloud data centers
(2016e: 56% vs. 44%)
Number of annual patent
applications for machine learning
0
500
1,000
1,500
2,000
2,500
3,000
2016e2012200820042000
Deep learning chipset market
revenue ($ million)
Processor trends
2025f2016
12,200
513
+42.2%
102
103
104
105
106
107
2020f1980 1990 2000 2010
GPU computing performance
Single-threaded performance

3. 2Unleashing the Power of AI for Enterprise Automation
All three factors are expected to keep accelerating. In fact, by 2020, almost 70 percent of all
enterprise data will be stored and processed in cloud-based data centers—providing unprec-
edented big data infrastructure to train machine-learning algorithms. Similarly, hardware chips
that can speed up algorithm training are rapidly advancing. Google, NVIDIA, and Intel have
announced next-generation GPU chip hardware that will accelerate training by 10 to 100 times.
And based on the growing number of patent applications, advances in the underlying machine-
learning algorithms will continue to pick up pace.
These trends offer strong evidence that AI will continue to improve. For business leaders, this
means that ignoring AI is no longer an option.
Fact 2: AI Is Being Used across Organizations
but with a Limited Scope
So what will AI be able to do for enterprise automation in the next five to seven years? Most
experts say companies will adopt narrow AI, or supervised machine learning that is focused
on one task. AI algorithms will be able to use training data to learn how to automate a task, but
once the task is mastered, the solution will be narrow, and in most cases, the machine will not be
able to generalize that learning to perform other tasks (see figure 2). Widespread use of broad,
human-like general intelligence—in other words, unsupervised and context aware—could be
decades away.
Reasoning and
optimization
Notes: RPA is robotic process automation. AI is artificial intelligence.
Sources: World Economic Forum expert panel interviews, press releases, company websites; A.T. Kearney analysis
Figure 2
The use of artificial intelligence will be narrow in scope in the near term
Cognitive
modes
Natural
language
processing
Computer
vision
Pattern
recognition
Supervised
learning
Unsupervised
narrow
learning
Unsupervised
context-aware
learning
Self-aware
unsupervised
learning
Rule-based
inference
Rule-based RPA
2017–2025 2025 and beyondFive years ago
Narrow AI Broad human-like AI
• Spelling and
grammar check
• Voice-to-text
dictation
• Idiom, sarcasm,
and nuance
articulation
• Autonomous
exploration
agents
• Mimicking
intuition and
creative connect-
ing of dots
• History-based
predictive
forecasting
• Forecasting using
demand-sensing
input with learned
segmentation
• Beating best-in-
class human
forecaster in
several domains
• Personal assistant
apps with basic
voice Q&A
• Identifying hidden
biases from
forecasting data
and input
• Real-time
dialogue and
translation
• Beating
best-in-class
human forecaster
in specific domain
• Scanning typed
characters in
format forms
• Facial recognition
• Scanning
handwriting
• Complex
classification (for
example, video
segment search)
• Vision systems in
complex settings
(for example,
vehicles)
• Loans risk
inference based
on rules
• Fraud detection
(based on known
patterns)
• Product recommen-
dation based on
hidden customer
preference
• Real-time clinical
diagnosis
• Anticipate
cyberattacks

4. 3Unleashing the Power of AI for Enterprise Automation
For example, consider a company that wants an AI algorithm that can scan PDFs and hand-
written invoices, validate field formatting, and then automatically trigger the accounts payable
process. With more advanced algorithms, or unsupervised learning, the solution could also
work with unlabeled data, such as collecting past invoices without having fields that are labeled
as valid or invalid. However, the AI solution this company adopted is limited to automating the
task of text-field recognition and formatting. To use AI to do more advanced invoicing tasks,
such as detecting fraud, the company would need to design and train a completely new
solution by focusing on other underlying features and patterns.
In the foreseeable future, AI applications will fit a narrow paradigm of using training data for
supervised machine learning. This has two strategic implications: acquiring labeled data for
training becomes a strategic capability and a source of differentiation, and AI solutions require
deep functional and domain-specific human co-creation and process redesign.
Fact 3: AI Is Ready for Deployment on Select
Activities
The Japanese insurer Fukoko plans to use AI to replace more than two dozen human agents who
process claims, and Goldman Sachs used machine learning to transform its 600-person trader
unit into a much leaner 200-person team between 2000 and 2016. However, not all organiza-
tional activities are suitable for AI automation under today’s narrow paradigm.
One way to characterize machine-learnable tasks is what computer scientist Andrew Ng calls
A→B activities: processes that take a set of unambiguous inputs and produce a response
(see figure 3). For example, retail demand forecasting is an A→B activity. By taking an array of
English
sentence
Note: AI is artificial intelligence.
Sources: “What Artificial Intelligence Can and Can’t Do Right Now,” Harvard Business Review, 9 November 2016; A.T. Kearney analysis
Figure 3
Narrow AI is best used when basic data can generate a clear response
Input A
Picture
Loan
application
Ad plus user
information
French
sentence
Are there
human faces?
(0 or 1)
Will they repay
the loan?
(0 or 1)
Will the user
click on the ad?
(0 or 1)
Language
translation
Photo
tagging
Loan
approvals
Targeted
online
marketing
ExampleResponse B
Examples of A→B activities suitable
for narrow AI
Examples of complex tasks not suitable
for narrow AI
Perform tasks based on output
from past activity
Interpret and communicate
via tacit cues and signals
Perform complex inference
based on context rather than
direct training data
Personality
Context
Episodic
memory
Social
learning
Exhibit human-like emotions
and quirks

5. 4Unleashing the Power of AI for Enterprise Automation
information such as seasonality, regional sales history, point of sale trends, social media signals,
and data about pricing sensitivity, an algorithm can predict demand as well as a human can.
Financial trading is another good example. An algorithm can use historic prices, macro trend
drivers, and arbitrage rules compiled from past traders and determine whether the best option
is to buy or sell. Although making the right decision based only on these inputs may be a
challenge given the unpredictability of the market, an AI solution is still attractive if it can
outperform humans over a large number of trades.
Mapping enterprise processes and activities into A→B versus non-A→B categories can help
managers pinpoint opportunities to use AI automation and augmentation strategies.
The AI boom is fueled by a convergence of three
factors: faster processing power, the proliferation of
big data, and advanced deep-learning algorithms.
Fact 4: Adopting AI Is about More Than
Technical Feasibility
Some AI applications will be adopted faster than others, even though the technical require-
ments are comparable. Broader solutions can ensure that a company’s portfolio of AI initiatives
can unlock value in the near term while also paving the way for long-term aspirations.
Consider the following when deciding where to use AI for enterprise automation:
• One-time costs. Assess the initial capital outlay for a new AI solution, such as developing an
algorithm and acquiring training data. Open-source access to algorithms and pay-as-you-go
“AI as a service” platforms can lower the fixed-cost hurdles, but access to training data can
be either an expensive bottleneck or a powerful source of differentiation.
• Switching costs. Evaluate the costs associated with displacing the existing solution with an
AI solution, including technical hurdles such as the ability to open the AI algorithm’s black box
to trace and explain decisions and human obstacles such as political and cultural resistance
to change.
• Ecosystem requirements. Determine if an integrated solution will require any comple-
mentary technologies. For example, an AI solution that must be integrated with innovative
IoT sensors and emerging robotics technology will be more complex to adopt.
• System externality hurdles. Consider the extent to which the AI solution could negatively
affect third parties that did not choose to use the new technology, bearing in mind that the
value of the solution will increase as more users adopt it.
Companies face a variety of challenges when adopting AI solutions, along with different
potential timelines for achieving a broad impact. For example, a call center we worked with
was assessing one solution that would convert handwritten forms into structured database

6. 5Unleashing the Power of AI for Enterprise Automation
records (see figure 4). The software development and switching costs were modest because
the solution could be launched by using off-the-shelf machine-learning modules. In addition,
the company already had a large amount of labeled data to use for training, optical character
recognition scanners to process the information, and extensive databases. The solution faced
relatively low externality barriers, and proof-of-concept solutions in certain functions and
geographic units could serve as training data and points of reference for other units. The
company expects to have broad use of the solution by 2021.
Another solution would enhance the call center’s agent interactions by automating sentiment
analysis from consumers’ voice calls or chats. This solution has much higher switching costs in
terms of cultural and risk barriers. The company opted to start small to minimize any negative
impact on its customers. If the solution works, the company will need to redesign its end-to-end
training process to allow the AI engine to push suggested responses to agents. This solution has
relatively high network externalities, where higher adoption will generate more training data to
improve performance but collecting the initial critical mass of training data will take time and
require company leaders to take a leap of faith. Given such complexities, an AI sentiment analysis
bot will likely take seven to 10 years rather than the first solution’s two- to three-year adoption.
Charting the Way with an AI Road Map
AI and machine learning will continue to enjoy a boom phase, powered by more sophisticated
algorithms, greater availability of data, and more advanced hardware. However, forward-
thinking companies take a disciplined approach in evaluating the opportunities. Based on the
above trends and their implications, four steps can help organizations embark on a compre-
hensive AI automation strategy:
Notes: AI is artificial intelligence. BPO is business process outsourcing.
Source: A.T. Kearney analysis
Figure 4
Companies face four major challenges when looking for AI solutions
Low High
Examples
Use case 1.
Extracting data from a
structured context and
recognizing letter shapes
Challenges of technology adoption
One-time
costs
Switching
costs
Ecosystem
requirements
System externality
hurdles
Use case 2.
Extracting data from a
semi-structured context
with keyword extraction
Use case 3.
Behavioral profiling and sentiment
analysis for customer contact
engagements

7. 6Unleashing the Power of AI for Enterprise Automation
Map the A→B activities
Rather than using one-off AI solutions, systematically identify and map all A→B activities across
the value chain for potential machine-learning automation. This ensures a broad view for
downstream considerations such as value potential, time to impact, development costs, and
alignment with the overall business strategy.
Prioritize where AI will be used
Because AI solutions are not generalizable and require specific data resources and training,
prioritize the A→B activities to be automated based on the potential business impact along with
the complexity of adoption to ensure smart resource allocation.
Adopt a portfolio approach
Manage the AI automation pipeline like a product innovation portfolio to optimize both near-term
and long-term value creation while mitigating the risks. Segment the initiatives based on
available data and adoption risks.
Consider the full spectrum of options to make, buy, or partner
Using AI often requires multidisciplinary capabilities and insights. Capitalize on internal experts,
consultants, and corporate partners as well as AI technology start-ups to adopt best-of-breed
solutions and develop nimble proofs of concepts that can be launched in weeks instead of years.
For sectors where AI has disrupting potential, have a clear overarching acquisition strategy to
avoid expensive rushed bets or late entries in a landscape of record-high valuations.
AI automation is rapidly becoming a reality across organizations and value chains. Now is the
time for forward-thinking business leaders to adopt a disciplined, portfolio-based approach to
develop machine-learning capabilities, data, and partnerships to remain relevant.
Author
Michael Hu, partner, Chicago
michael.hu@atkearney.com
The author would like to thank Eric Gervet, Florian Dickgreber, Sean Monahan, Denis Bassler,
and Joseph Edwar for their valuable contributions to this paper.

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