The document discusses building enterprise artificial intelligence (AI) products. It provides definitions of key terms like AI, machine learning, and deep learning. It outlines the unique challenges of building enterprise AI products compared to consumer products. These include dealing with complex business problems, datasets, and regulatory requirements. The document also presents examples of enterprise AI use cases and platforms. It discusses barriers to adoption like data and expertise requirements. Finally, it provides tips for planning an enterprise AI journey and pursuing a career in enterprise product management.

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10. Manoj Bapat
TONIGHT’S SPEAKER

11. Management
Consultant
Product
Manager
Engineer
Engineer/Product/
Program Manager
About me
Manoj Bapat
11

12. Key takeaways
➔ Introduction to AI in the enterprise
➔ Unique challenges and opportunities for
building enterprise AI products
➔ Planning your enterprise AI journey
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
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13. Some working
definitions...
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
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14. Enterprise Products satisfy the needs
of an organization for business
functions and processes such as
procurement, compliance, hr, finance,
marketing and customer support
Consumer Products satisfy the day
to day needs of an individual user
such as communication, health,
entertainment, finance, news, and
travel
Source: Wikipedia Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
14

15. What is
Artificial
Intelligence
(AI)?
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
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16. Source: Mckinsey and Company- An executive’s guide to AI
Deep Learning
(DL) is a type of
machine learning
based on neural
networks that
requires less data
pre-processing by
humans more
accurate results than
traditional ML
approaches
Artificial
Intelligence (AI)
is the ability of a
machine/software to
perform cognitive
functions such as to
see, listen,
understand and
reason
Machine
Learning (ML)
refers to algorithms
that detect patterns
and make predictions
by processing data,
rather than by explicit
programming; ML
has powered recent AI
advances
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
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17. ● Application of AI/ML/DL to complex business
problems by performing predictions, making
recommendations, providing insights, driving
automation and engaging in natural language
● Typical business objectives are to enhance
customer experience, scale revenue, increase
employee productivity and mitigate risk
● Different use cases from consumer facing
applications, but they do overlap!
Enterprise AI
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
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Source: IBM AI Ladder

18. ● Exponential growth in compute, bandwidth and
storage
● Prolific data generation by consumers,
enterprises and devices
● Algorithmic advances in areas of machine
learning and deep-learning
● Emergence of a strong AI ecosystem
Catalysts for AI
growth today
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
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19. AI market size and impact
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
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In 2021, AI will generate $2.9
trillion in business value and
recover 6.2 billion hours of
worker productivity - Gartner
Spending on AI systems will
grow at 38%to reach
$79.2B by 2022
- IDC
Source:1) IDC 2) Gartner

20. Enterprise AI
finds
applications in
a variety of
industries and
domains
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
20Source: Mckinsey Global Institute

21. Enterprise AI building blocks: pre-built services and
applications
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
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Conversational
Assistants
AI Insights
Engine
Also,
Speech<->Text
Personality Insights
Tone Analyzer
Language Translation
...and many more
Image
Recognition
Source: IBM Watson
Natural Language
Understanding
Pre-built AI services and
apps are built on top of
Machine Learning (ML)
and Deep Learning (DL)
models

22. Components of an enterprise AI
platform
Tools to build and train AI/ML
modTools to build and train AI/ML
models
els
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
22
Development
environment to
build and train
AI/ML/DL models
Pre-built AI services and applications
Runtime to deploy
models into
production
Management of AI
usage and
outcomes in
production
Data discovery, cataloging and activation
Public/Private/Hybrid Cloud Deployment models
Source: IBM AI Ladder

23. Representative use case:
Customer Care Transformation
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
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Source: IBM Watson
Virtual Agent - creates an effortless customer
experience that goes beyond basic FAQs by
giving customers the ability to answer their
most complex questions
Agent Assist- surface relevant and accurate
information through AI powered search, giving
agents the ability to respond to customer
questions faster
Voice Agent - lets you to improve efficiency by
interacting with callers using natural language
to provide self-service over the phone

24. Enterprise AI adoption today
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
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Source: O’Reilly, August 2018

25. ● Requires deep understanding of business
domain and processes along with skilled
resources
● Data keeps growing at scale, but, labeled data
is expensive
● Enterprise AI needs to learn from less data
● Machine-learned models begin degrading as
soon as they’re deployed and must adapt to a
changing environment
● Protection of data and insights is paramount
for enterprises
● Decisions and recommendations need to
traceable, explainable and fair
Barriers to enterprise AI adoption
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019
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“85% of AI projects will
deliver erroneous outcomes
due to bias in data,
algorithms or the teams
responsible for managing
them”
-Gartner

26. ● Start with a clear focus on business
strategy and objectives
● Create a strong data and digital
foundation
● Set criteria for moving pilots to production
● Focus on workforce transformation and
change management
● Adapt processes used in software
development such as Agile to AI/ML
product development
Planning your enterprise AI journey
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“Creating a solid AI product
that provides either customer,
employee, operational or
investor value is about 40%
problem and product
definition, 40% data sourcing,
cleaning, filling, and merging,
and only 20% algorithm
development.”
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019

27. ● Understand AI/ML business and technology basics through courses
offered online and offline
● Get hands on experience by working on enterprise AI platform
resources available for free and open source resources
● Follow AI experts on social media and podcasts
● Identify the product management opportunities in AI/ML interest you:
role and industry background and preference and skill up
● Talk to practitioners!
Enterprise AI is still nascent: multiple
opportunities to pursue a product management
career
27
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019

28. Thank you!
Building Enterprise Artificial Intelligence(AI) Products- Manoj Bapat, March 27,2019

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