HfS Webinar Slides - A (much-needed) reality-check on Enterprise Artificial Intelligence

Proprietary│Page 1© 2018 HfS Research Ltd.
HfS Webinar: A Reality-check on Enterprise
Artificial Intelligence (AI)
Tom Reuner
Managing Partner
tom.reuner@hfsresearch.com
April 12, 2018
Phil Fersht
CEO and Chief Analyst
phil.fersht@hfsresearch.com

Questions
• Attendees can submit questions throughout the webinar by typing it
in the ‘Question panel’ in the GoToWebinar control panel.
• All questions are submitted to the organizer and panelists. We will
try to answer as many as we can during the webinar
Recording and slides
• The webinar recording and slides will be made available on our
website. If you have registered for the webinar, you will receive an
email when they are available.

Phil Fersht, CEO and Chief Analyst, HfS Research

Our panellists
Jesus Mantas
Global Head of
Strategy and Offerings
IBM Global Business Services
Mike Salvino
Managing Director,
Carrick Capital Partners
and
Executive Chairman
Infinia ML
Phil Fersht
HfS Research
Tom Reuner
Managing Partner
Business Operations Strategy
HfS Research

HfS Research… separates the wheat from the chaff

The Six Value Change Agents Driving the Digital Operations Industry
Success in the future will be determined by how well clients, techniology and service providers are able to combine
the power of multiple change agents into integrated solutions that solve crucial business problems
Source: HfS Research, 2018

Q. What do you see as the primary benefits of breaking down barriers between front, middle and back
office moving toward an operating framework like the OneOffice?
C-Suite’s Desires from OneOffice reorganization: Better
Data and Alignment of Operations to Business Outcomes
Source: HfS Research 2018
Sample: C-Level Enterprise Executives = 100
9%
22%
19%
9%
31%
4%
4%
14%
6%
11%
15%
11%
34%
6%
12%
10%
10%
18%
7%
19%
10%
35%
38%
40%
42%
49%
57%
Increase competitiveness in the wake of digi disruption
Increased operational simplicity
Greater efficiency /reduced cost
Improved workplace culture
Improved quality and speed of execution
Stronger alignment of business operations to business
outcomes
Better data to drive the business forward
Rank 1 Rank 2 Rank 3

When do you believe AI automation to be applicable for you within the following processes?
All AI Techniques & Solutions Are Getting Evaluated
37%
42%
37%
41%
45%
19%
18%
21%
23%
21%
23%
21%
24%
19%
22%
NEURAL NETWORKS
NATURAL LANGUAGE PROCESSING (NLP)
COMPUTER VISION
VIRTUAL AGENTS
MACHINE LEARNING (ML)
Piloted / implemented Evaluating In next 2 years
Source: “State of Automation 2017”
Sample: Enterprise Buyers = 400

Over Half of Enterprises Bracing for Major Changes
in Internal Roles
Q. In terms of the number of transactional internal roles within the following process areas, what proportion do
you expect to be significantly impacted by automation in the next 2 years? ( Average Across Functions)
4%
8%
11%
26%
31%
21%
N/A Prefer not to say Under 10% 11-20% 21-50% 50%+
% Employees Impacted by
Automation
Source: HfS Research in Conjunction with KPMG, "State of Operations and Outsourcing 2018, March, 2018
Sample: Interim Enterprise Buyers = 250

A digital labor strategy: more emphasis on the LABOR!

Tom Reuner, Managing Partner, HfS Research
Shaken, definitely stirred
tom.reuner@hfsresearch.com
@tom_reuner
Overview
▪ Tom Reuner is Managing Partner, Business Operations Strategy and
M&A Advisory. Tom is responsible for driving strategic engagements in
business operations, IT as well as M&A advisory. The responsibilities
range from research over consulting to business development. This
involves advising clients on the formulation of strategies, guiding them
through methodologies and the analysis of research findings, as well as
interactive liaison with the client throughout the course of projects from
initial meeting to conclusion.
▪ Tom is driving thought-leadership and frameworks across business
operations and in particular Intelligent Automation and Artificial
Intelligence. Automation cuts across the whole gamut ranging from RPA
to Autonomics to Cognitive Computing and AI. This includes increasingly
the intersections of unstructured data, analytics, and Cognitive
Automation while mobilizing the HfS analysts to research Intelligent
Automation dynamics across specific industries and business functions.
▪ Previous Experience
▪ Tom’s deep understanding of the dynamics of this market comes from
having held senior positions with Gartner, Ovum and KPMG Consulting in
the UK and with IDC in Germany. He is frequently quoted in the leading
business and national press, appeared on TV and is a regular presenter
at conferences.
Education
▪ Tom has a PhD in History from the University of Göttingen in Germany.

Key lessons learned
Hype around chatbots is distorting the
marketing communications
The Enterprise AI market has a duplexity of
approaches: Industrialization and project-centric
Enterprise AI is still at the periphery of
the enterprise or applied as a bolt-on
The Holy Grail of AI is at the intersection
of iterative data inputs and minimal
training of algorithms

The frontier in service delivery is at the intersection of automation,
analytics and AI
The report is focused on Artificial
Intelligence (AI). Our definition of AI
includes cognitive solutions.
Forthcoming reports on RPA and
Smart Analytics
Definition of AI for the purpose of this study: AI aims to automate intelligent activities that humans associated with other
human minds through a combination of reasoning, knowledge, planning, learning, natural language processing
(communication), and perception (aka cognitive).

The Enterprise AI Market Has a Duplexity of Approaches: Industrialization and Project-Centric
Project-centric approaches are highly domain-specific and the strategic logic
is 1-to-1. Examples include the automation of a medical coding in a hospital
to support diagnosis and better patient records.
Industrialization Project-Centric
Service orchestration
Horizontal
Out-of-the-box
Alignment with Intelligent
Automation
Service delivery
Mega ISVs
Data lake
RPA, autonomics,
chatbots
Narrow AI
Sub-sector lens
Design Thinking
Expansions analytics
Data Science
Proprietary IP and
open source
Data silos
Proprietary algorithms,
Deep Learning
Strong AI
Specific requirements
Industrialization is all about finding as many commonalities across delivery backbones
as possible in order to scale and save costs at the same time.
The strategic logic is 1-to-many. Examples would be monitoring of infrastructure or
self-remediation technologies including IPsoft and Arago.

The journey toward AI has disparate starting points

Just like Intelligent Automation, AI should be seen as continuum
AI
Neural
Networks
Autonomics
Virtual
Agent
Machine
Learning
Image
Recognition
Machine
Reasoning Natural
Language
Processing
Chatbot
Deep
Learning
Computer
Vision
Speech
Recognition
Knowledge
Represen-
tation

AI Technology Partner Landscape
Virtual
Agents Neural
Networks
Machine/
Deep
Learning
Autonomics
Computer
Vision
NLP
AI Building
Blocks

Move toward AI will bring mega-ISVs to the fore
AI
Data
Algorithms
Platform
Compute
Google Tensor
Processing Unit
NVIDIA Volta
SAP Leonardo
Salesforce
Einstein
Google Neural
Machine Translations
Google WaveNet
Google TensorFlow
Amazon Machine Learning
Google Cloud
Machine Learning
Microsoft
Cognitive Services
Oracle
Data Cloud
Oracle
Adaptive Apps
IBM Watson
Data Insights
IBM Watson
API Explorer
HIRO
Knowledge Core
HIRO Engine
Wipro
Holmes
TCS ignio
IBM Watson
Knowledge Studio
Google DeepMind
Celaton Instream
Loop AI
Cortana
Intelligent Services
Azure Machine Learning
SAP
Data Hub
AWS Public
Datasets
Infor
Coleman
Amazon
Rekognition
Amazon
Lex
IBM Watson
Virtual Agents
Intel
Movidius
Amazon
Connect
Fujitsu DLU
AMD/GloFlo
Oracle
Intelligent Bots
Intel
Loihi
Adobe
Sensei
Infosys Nia

Moving toward a data-centric mindset necessitates new requirements for talent
Data
Data Scientist:
• Cleaning, organizing
data
• Custom algorithms
• Statistical
modelling
• Feature
engineering
• Exploratory
analysis
Data Engineer:
• Designing, testing,
maintaining scalable
data architectures
• Evaluation,
integration tools
• Data ingestion
• Deployment
• Solution architecture
Artificial Intelligence
Technologies:
• Ingestion of data
• Pattern analysis
• Knowledge representation
• Integration of disparate
approaches

The Holy Grail of AI is at the intersection of iterative data inputs and minimal training of algorithms
Limited (training data) Complex (expanding sources and formats)
Unsupervised LearningSupervised Learning Reinforcement Learning
problem
definition
data
selection
model
selection
model
training
model
improvement
model
deployment
input data complexity
necessary training of algorithms

Innovative AI use cases
Virtual Assistant integrated with Hadoop cluster: Big 4 for global bank, Hadoop
cluster to feed every customer channel; sentiment analysis and real-time analysis.
Example for scale and service orchestration; goes beyond chatbot hype
Setup of AI CoE and Intel Nervana AI Academy. TCS provides a platform to
connect researchers, developers, and startups. Example for ecosystem
enablement
Integration of disparate sources for General Ledger: Global SI. Clients can drop
disparate information for General Ledger requirements; Machine Learning and other
technology building blocks allow for seamless processing
Cross-fertilization from other sectors: Global SI has helped Australian company to
automatically identify telephone posts using Google Tensorflow and Street View
replacing manual inspection; broad replicability, think insurance scenarios
Leverage of ML for medical coding: Global SI (not Watson) helped European
hospital to apply medical coding at scale to allow for digital patient record and
diagnosis. Example for complex Data Science approach at scale for critical processes
Exploring quantum computing techniques on AI and Machine Learning
algorithms in Block Chain applications: Global SI for leading bank in Australia.
Example for integrated approach of next-gen technologies and approaches

HfS Blueprint: Enterprise AI Services 2018
WHAT THIS BLUEPRINT REPORT COVERS
The pace of change driven by the onset of AI is nothing short of astounding. Startups continuously
change perceptions of what best-of-breed might mean. At the same time, we see PoCs progressing
to projects literally within a few months. Consequently, many boards are paranoid about the emerging
notions of cognitive and AI, but all too often fail to turn those fears into actionable items. Against this
background this report takes stock where the enterprise market for AI really is at. How is AI enabling
organizations journey toward the OneOffice? A crucial element of this report is to play back the
lessons learned from the early deployments.
WHO SHOULD READ THIS REPORT
Executive leaders and business unit leaders, outsourcing and procurement managers, advisors,
investors who have responsibilities for innovation, digital transformation and for building out service
delivery capabilities.
SERVICE PROVIDERS WE DISCUSS
Accenture, Atos, Capgemini, Cognizant, Deloitte, DXC Technology, EY, Genpact, HCL, IBM, Infosys,
KPMG, LTI, PwC, Syntel, TCS, TechMahindra, Wipro
Access the document
(freely accessible for HfS premium subscribers)

Our panellists
Jesus Mantas
Global Head of
Strategy and Offerings
IBM Global Business Services
Mike Salvino
Managing Director,
Carrick Capital Partners
and
Executive Chairman
Infinia ML
Phil Fersht
HfS Research
Tom Reuner
Managing Partner
Business Operations Strategy
HfS Research

IBM Services
Enterprise-Grade AI
HfS Webinar
Jesus Mantas | Managing Partner, Cognitive Assets and GBS Ventures Global Head
of Strategy & Offerings, IBM Services
Webinar | April 12, 2018

2727
Trusted, Transparent &
Auditable
• Transparency of training
• Ability to explain decisions
• Auditability of
recommendations
Integrates with work
flows and talent
flows
• Business Purpose
• Secure
• Bidirectional human-
system design
Learns more from less
data
• Limited, not semantically
consistent
• Thousands, not billions
• Industry-context specific
Enterprise-Grade Artificial Intelligence
2

28
Data is the natural resource required for ML or AI to distill any
outcomes

2929
Risk & ComplianceCognitive Care
Knowledge Worker
Enterprise-Grade Artificial Intelligence Use Cases at Scale
HR / Talent
Global Financial Institution

30 ©2018 IBM Corporation 20 April 2018 IBM Services30 ©2018 IBM Corporation 20 April 2018 IBM Services30
Bridging the Gap Between Different Approaches to AI
Leveraging AI in an agile DevOps lifecycle to reduce cycle
time, automate tasks, and reduce trouble tickets to build
horizontal solutions
Industrialization
Cognitive Garages creates a rapid, scalable, and cost
effective way to design, develop, test and deploy
transformative AI-powered solutions
Cognitive
Garages
Project-Centric

The ML Epidemic
Epidemic: a widespread occurrence of a particular undesirable phenomenon
The undesirable phenomenon here is that all of a sudden everyone knows
how to do ML and is an Expert. (The ML Epidemic)
Don’t Fuel the ML Epidemic!

ML Epidemic–Point #1: I have a TEAM that knows ML!
Your team that knows how to do Simple Neural Networks…An example is converting English words to
French…These successful simple projects will inspire enterprises to want more out of ML and this is when
your ML team will STRUGGLE. Building Deep Learning Neural Networks (Multi-Layered) is not easy.

ML Epidemic–Point #2: The ML projects my team are
performing will scale to achieve true business IMPACT!
The AlphaGo breakthrough was great PR (there is now a documentary on Netflix) but provided little business impact.
Make sure your projects are answering these 3 questions. NO SCIENCE EXPERIMENTS!
1. Does the project solve a top 1, 2, or 3
question that a CEO or Executive wants
answered?
2. Does the project help your company to
reduce costs or increase revenue?
3. Does the project create a unique data
set for your company?

ML Epidemic–Point #3: My Data is READY to go!
There is NO ML without Data. Salvino prediction: “Companies will spend as much if not more money dealing
with Data in the next 5 years as they have spent implementing mission critical systems like SAP, etc.”
1. Accessible – Can your ML team actually access your data?
2. Clean – Is your data clean or is there “junk” in fields?
3. Data Sets – Have you created data sets or have you created data “swamps”?
4. Maintain – Do you have a process and team to maintain the data (Data Science Culture)?
5. Utilize – Do you have a strategy to answer question that make business impact?

Scarcity of resources is real and expensive. NIPS was a recruiting event this year instead of a research conference. Most
luminaries are not teaching any longer. They are tied up doing work for companies so new resources are not being created to
keep up with demand. Proficiency is developed by doing years of research and most companies don’t have access to labs.
How many ML experts have you met this year that were Cloud or Security experts last year?
How to Get Started–Point #1: EVALUATE your ML talent
1. Advanced degree in relevant quantitative field (statistics, computer science,
applied mathematics, etc.)
2. 7+ years experience in machine learning, data science, data engineering, and/or
computational software development
3. 3+ years development in Python, including libraries such as NumPy, SciPy,
pandas, TensorFlow, etc.
4. Experience with deep learning models, including CNN and RNN architectures
5. Experience working with large datasets, including NoSQL and relational databases
6. Experience with cloud computing

Big data, IoT, Analytics, Digital, etc. All good buzz words but it really is not that hard.
Create Data Sets and a Data Science Culture. It is not GLAMOROUS work but it matters!
How to Get Started–Point #2: Create a coherent data strategy across
your data warehouses, lakes, rivers, streams, puddles, swamps…

Magicians vs. Aliens. Magicians want to work with other Magicians not folks that view them as Aliens. This is
not inspiring to them. People leave People – they don’t leave Companies so ORGANIZE for success!
How to Get Started–Point #3: CENTRALIZE the ML Function

Q. What are your greatest challenges preventing you from achieving the OneOffice Concept?
IT Lacks Talent, Business Lacks Mindset
12%
10%
23%
29%
21%
0%
23%
25%
6%
35%
8%
8%
6%
13%
6%
6%
We’re held hostage by legacy technology
Lack of talent internally
Legacy thinking / lack of a “digital mindset” from IT
Legacy thinking / lack of a “digital mindset” from biz
functions
We’re held hostage by legacy technology
Lack of talent internally
Legacy thinking / lack of a “digital mindset” from IT
Legacy thinking / lack of a “digital mindset” from biz
functions
Rank 1 Rank 2
IT
C-Suite
Business
C-Suite
Source: HfS Research 2018
Sample: C-Level Enterprise Executives, Major Enterprises = 100

3%
1%
4%
2%
14%
10%
10%
6%
32%
18%
3%
4%
8%
7%
9%
13%
13%
14%
9%
22%
6%
9%
10%
13%
5%
8%
11%
21%
5%
10%
12%
14%
22%
22%
28%
31%
34%
41%
46%
50%
Understanding business processes and using automation and AI to
improve business performance
Understanding / using digital and cloud technology to improve business
performance, drive change
Analytical prowess to improve operations / productivity
Improving end-to-end processes across external and internal delivery
Defining business outcomes
Influencing senior executives
Vision and ability to drive change
Commercial acumen (balance process, technology and innovation
decisions with sustainable cost model)
Exploring new ways of partnering across the services ecosystem
Creative, entrepreneurial spirit, Curiosity for innovation;
First Second Third
Focus is on the Right-brain, not the Left!
Q. What are the top three workforce requirements required now?
Creative
thinkers to
solve
problems
and design
solutions
Less critical skills
shift – creates more
appetite to
outsource
Source: HfS Research, 2018
“Intelligent Operations Study” conducted in association with Accenture
Sample: Enterprise Buyers = 460

RPA, Cloud & IoT Lead Investment Focus
16%
19%
33%
33%
33%
37%
42%
44%
53%
32%
37%
48%
40%
30%
41%
35%
36%
28%
22%
20%
14%
23%
20%
18%
18%
14%
14%
31%
24%
5%
4%
18%
4%
5%
6%
5%
Driverless Vehicles
Drones
AI/ML/Cognitive
Blockchain
Virtual and Augmented Reality
Analytics
Internet of Things (IOT)
Cloud
RPA
Significant investment/focus Some investment/focus Limited / Modest investment / focus No investment /focus
Q. How much investment/focus is your organization making in the following in the next year to help you achieve operational cost
saving goals?
Source: HfS Research in Conjunction with KPMG, "State of Operations and Outsourcing 2018, March, 2018
Sample: Interim Enterprise Buyers = 250

The HfS Mission & Vision:
Defining Future Business Operations
• HfS defines and visualizes the future of business
operations across key industries with its OneOfficeTM
Framework.
• The HfS mission is to provide visionary insight into the
major innovations impacting business operations:
Automation, Artificial Intelligence, Blockchain, Internet of
Things, Digital Business Models and Smart Analytics.
• HfS influences the strategies of enterprise customers to
develop OneOffice backbones to be competitive and
partner with capable services providers, technology
suppliers, and third party advisors.

HfS Webinar Slides - A (much-needed) reality-check on Enterprise Artificial Intelligence

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Ähnlich wie HfS Webinar Slides - A (much-needed) reality-check on Enterprise Artificial Intelligence

Ähnlich wie HfS Webinar Slides - A (much-needed) reality-check on Enterprise Artificial Intelligence (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

HfS Webinar Slides - A (much-needed) reality-check on Enterprise Artificial Intelligence