2019 London Data Science Festival. Making Money in AI, Machine Learning and Data Science.

Who’s going to make
money in AI?
The secrets of successful AI and
data science in the real world
Simon Greenman
Partner, Best Practice AI
@simongreenman
www.bestpractice.ai

(c) 2019 Best Practice Artificial Intelligence Ltd
Simon Greenman, Partner Best Practice AI Ltd
2
• BA in Computing & Artificial Intelligence from the University of
Sussex
• Co-founder of the early AI-enabled mapping service
MapQuest.com
• Chief Digital Officer, GM, CEO with over twenty years of
leading digital transformations internationally through
technology, data science and AI
• Highly active in the early stage eco-system as Co-President
of the Harvard Business School Alumni Angels of London, an
advisor at DN Capital, and AI Expert in Residence at
Seedcamp
• Board advisor at Seldon, a ML deployment company
• Member World Economic Forum Center for 4th Industrial
Revolution - AI.
S I M O N G R E E N M A N
Partner, Best Practice AI Ltd
AI Management Consultancy
www.bestpractice.ai
Create Competitive Advantage with AI
2

(c) 2019 Best Practice Artificial Intelligence Ltd 3
https://www.forbes.com/sites/artimanmanage
ment/2014/10/28/drone-technology-
investment-bet-on-the-picks-and-
shovels/#7bb7b4cc5313

Agenda
• The AI Gold Rush - who’s making money in AI?
• Ensuring corporate AI and data science business success:
A. Plan
B. People
C. Data
D. Technology
E. Operationalisation
F. Risk Management
• The secrets of successful AI and data science start-ups.
The secrets of success with AI and data science

So. Are you ready for another AI gold rush?
https://www.forbes.com/sites/artimanmanage
ment/2014/10/28/drone-technology-
investment-bet-on-the-picks-and-
shovels/#7bb7b4cc5313

AI is a story of many boom and busts
Within a generation…I am
convinced…the problems of
creating artificial intelligence
will be substantially solved.
“
M a r v i n M i n s k y
M I T
Within our lifetime machines
may surpass humans in
general intelligence
“
1961 1967

But is it different now? The power of exponential!
ESTIMATE OF AMOUNT OF DATA BYTES
THAT WILL BE CREATED EVERY SECOND
BY EVERY PERSON ON THE PLANET IN 2020
1 , 7 0 0 , 0 0 0
B Y T E S / S E C
1. Data
THE SPEED OF A CAR TODAY IF ITS
SPEED HAD IMPROVED AT THE SAME
RATE AS COMPUTATIONAL CHIPS
SINCE 1971
4 2 0 , 0 0 0 , 0 0 0
M P H
2. Comp Power
THE COST OF MANY OF THE AI
FRAMEWORKS, LIBRARY AND
TOOLS
$ 0
3. Software
THE COST OF MANY OF THE AI
FRAMEWORKS, LIBRARY AND
TOOLS
$ 0

Your are the champions of change
Bringing AI and data science to the enterprise
and society
8

So who’s going to make
money in AI?

Enterprise
Solutions
4
Chips &
hardware
1
Platform &
infrastructure
2
Vertical
Industry
Solutions
5
Corporates6
Healthcare & Life
Sciences
Finance &
Insurance
Agriculture Automotive Legal &
Compliance
Industrials, Robotics
& Logistics
Frameworks &
algorithms
3
Nations7
* Excludes SMB sectors. The companies noted are
representative of larger players in each category but in no
way is this list intended to be comprehensive or predictive.
** Acquired by Cisco and Google respectively.
Conversational
agents**
VisionCore
Algorithms
NLP &
Semantics
Speech
There is a huge race going on in AI from the chip makers to enterprise
software providers to corporates to countries with $billions being invested
AutomotiveFinance &
Insurance
Healthcare Agriculture Legal &
Compliance
Industrials, Retail, media,
other
Tech & Telco
Customer
Management
Intelligence &
Analytics
CybersecurityMarketing &
Sales
HR & Talent RPA,
Other
ConsultantsTools
Sources: CBInsights,
Crunch Base, and
misc. others
AI is going to be ubiquitous and woven into the fabric of society and organisations globally

Chips &
hardware
1
Platform &
infrastructure
2
Frameworks &
algorithms
3
Conversational
agents**
VisionCore
Algorithms
NLP &
Semantics
Speech
The picks and shovels of this gold rush will be the tech giants
Crunch Base, and
misc. others
“This Tech Company May Be Near a
‘Tipping Point’ in Dominating Artificial
Intelligence.” Barron’s on Nvidia Sep 30th
2018
1 2
“There are 1.2 million
developers using our cognitive
services while 300,000 use
conversational AI.”
Microsoft’s GM of AI, David
Carmona
3
The providers of chips, platform and the frameworks / algorithms will power everyone who is
looking to find gold with AI services regardless of who finds it

The AI enterprise & vertical solution provider race is intense
Crunch Base, and
misc. others
Enterprise
Solutions
4
Vertical
Industry
solutions
5
Healthcare & Life
Sciences
Finance &
Insurance
Agriculture Automotive Legal &
Compliance
Industrials, Robotics
& Logistics
$156M
$25M
$93M
$137M
$448M
$104M
$230M
$108M
<
$202M
$1000M
$217M
$95M
$77M
$182M
“Salesforce Strengthens Its
AI Capabilities With an $800
Million Purchase [of
Datorama],” July 16th 2018
“SAP Acquires Recast.AI
to Accelerate Natural
Language Processing
Capabilities,” 22nd Jan
2018.
$557M
Customer
Management
Intelligence &
Analytics
CybersecurityMarketing &
Sales
HR & Talent RPA,
Other
ConsultantsTools
$147M
The winners will be the incumbents and those start-ups who gain category leadership with scale
of customers, data, capital and talent

* https://towardsdatascience.com/the-secrets-of-successful-
ai-startups-whos-making-money-in-ai-part-ii-207fea92a8d5
But watch out for fool’s gold
• MMC Ventures noted 40% of 2,830 so
called AI startups in Europe are not really
using AI
“Tech companies benefit from the
perception that they’ve built sophisticated
automation and A.I., rather than a system
that relies on manual labor.
Nearly every startup today claims to be an AI powered company

But what are corporates doing
with AI?

https://www.forbes.com/sites/alexknapp/2018/04/25/gart
ner-estimates-ai-business-value-to-reach-nearly-4-
Gartner Research predicts AI-derived business value will reach up to $3.9
trillion by 2022

Source: Capgemini Digital Transformation Institute, State of AI survey, N=993 companies that are implementing AI, June 2017
https://www.capgemini.com/gb-en/wp-content/uploads/sites/3/2017/09/dti-ai-report_final1-1.pdf
Revenue
75% of
organisations
implementing AI
increase sales of
products and
services by more
than 10%
Deeper
Insights
79% of
organisations
implementing AI
generate new
insights and
better analysis
There are four main business benefits of AI
Customer
Engagement
75% of
organisations
using AI enhance
customer
satisfaction by
more than 10%
Automation
& Operational
Efficiency
78% of
organisations
implementing AI
increase
operational
efficiency by more
than 10%

To help leaders better understand the value of AI, Best Practice AI
created the world’s largest AI use case and case study library
Free at www.bestpractice.ai
6 0 0 +
Use Cases
1 0 0 0 +
Case Studies
6 0 +
Territories
3 0 0 0 +
Vendors
U s e
c a s e
KPIs
Technology
Data
Vendors
C a s e
s t u d y
Geography
Players
Impact
Data
Vendors
M e m b e r o f
W o r l d E c o n o m i c F o r u m A I p r o g r a m &
U K A l l P a r l i a m e n t a r y G r o u p o n A I

In node graph format - functions and use cases

Most case studies contain details on the reported project details,
ROI, data and technology

We created a heat map of where AI technology can be used to
create value today
Predict future customer demand - Help screen CVs - Optimise supply
chain purchasing - Reduce cyber risks - Improve customer service -
Automate data entry with RPA - Better market and engage prospective
customers - Improve product offerings - Predict customer churn -
Score top customer prospects
20

There is also an AI arms race
across nations
21https://www.gartner.com/technology/pressRoom.do?id=3872933

Countries are vying for AI supremacy
https://www.prnewswire.com/news-releases/london-tech-week---london-is-named-artificial-intelligence-ai-capital-of-europe-by-new-report-
685101231.html
https://medium.com/creation-being-human/who-will-win-the-ai-arms-race-187ac2ae8927
• France announces a $1.85B AI investment over the next five and that foreign take over
of AI companies will require government approval
• UK announces a $1.3B corporate and government AI investment
• Europe announces a $22B AI investment
• And then the Chinese city of Tianjin announces it is setting up a $16B AI fund
• China has developed the ‘Big Fund’ estimated total investment at ~$140B to grow their
semiconductor industry.
China has an explicit goal developed at the highest level of government to make itself
the global leader in AI by the year 2030 with more favourable structural environment
including looser data and privacy regulation

“China is the Saudi Arabia in data” Kai Fu-Lee
https://www.slideshare.net/AIFrontiers/kaifu-lee-at-ai-frontiers-the-era-of-artificial-intelligence?from_action=save
Watch China closely in the battle for AI supremacy

To learn more search for this article

Agenda
A. Plan
B. People
C. Data
D. Technology
F. Risk Management

Achieving real business outcomes
from AI and data science
26https://www.gartner.com/technology/pressRoom.do?id=3872933

Getting to POC is the tip of the AI transformation iceberg
Above the waterline proving AI works requires:
• AI talent
• Data wrangling and training set labelling
• Frameworks and algorithms
• Compute access
• Project support management including subject
matter expertise.

(c) 2019 Best Practice Artificial Intelligence Ltd 2 8
Below the waterline much more needs to be done to capture value
Biggest challenge faced by organisations operationalising AI:
1.Lack of results and efficacy - ROI
2.Lack of technology maturity especially chatbots
3.Change management – getting humans workers
comfortable and educated on AI solutions
4.Business process redesign - how is AI integrated with
humans in business processes
5.Technology integration and scale-up – difficult to
integrate cognitive projects with existing processes and
systems
6.Technology deployment - difficult to deploy and update
the technology in production.
h t t p s : / / b i t . l y / 2 F F G F y T
If the business is not bought in then technology will
struggle to deliver

People challenges are reported as the greatest barrier to becoming data-
driven organisation
2018 New Vantage Partners survey of executives including Bank of America, Goldman Sachs, Capital
One, Morgan Stanley, UBS, Travelers
2 9
h t t p : / / n e w v a n t a g e . c o m / w p - c o n t e n t / u p l o a d s / 2 0 1 8 / 0 2 / B i g - D a t a - E x e c u t i v e - S u r v e y - 2 0 1 8 - F i n d i n g s . p d f
“58% of executives cite insufficient organisational
alignment or cultural resistance as the biggest barrier
to business adoption of data driven organisation
AI and data science is a team sport

Agenda
A. Agree a Plan
B. People
C. Data
D. Technology
F. Risk Management

Find strong use cases that will resonate with executives
For example fraudulent activity using unusual payment transaction patterns and other
data
HSBC automated its anti-money
laundering (AML) investigations to
increase efficiency and
effectiveness in its regulatory
compliance. Historically AML was
carried out by humans but the bank
has turned to Ayasdi and its
machine learning software to
monitor transactions and automate
identification of potential criminal
activity.
https://www.bestpractice.ai/studies/hsbc_reduces_false_pos
itives_for_money_laundering_detection_by_20_using_ai_to
_automate_the_system_rules
Danish Danske Bank had a problem
with false positives in its payment
fraud detection that was
approaching 99.5%. Using
advanced machine learning they
were able to increases fraud
detection in real-time by 60% and
reduce false positives by 50%.
https://www.bestpractice.ai/studies/danish_danske_bank_increases
_payment_fraud_detection_by_60_and_reduces_false_positives_b
y_50_with_machine_learning
OCBC bank of Singapore analyses
transaction activity to identify unusual
payments that might be financial
crime. In a pilot OCBC was able to
reduce the number of false positives
by 35% by using machine learning.
They were also able to classify
transaction alerts into 48 unique risk
clusters allowing the compliance
team to better prioritise based on risk.
https://www.bestpractice.ai/studies/ocbc_bank_reduces_nu
mber_of_false_positive_financial_transaction_alerts_by_35_
with_machine_learning

Find out what your competition is doing
McKinsey found the largest motivator for a European company to adopt AI is competition
McKinsey Global Institute - Notes from the AI frontier
Tackling Europe’s gap

Ensure you quantify the potential business benefits
AI delivers different potential benefits by function. Manufacturing, Operations and Supply
Chain offer significant cost saving potential, Marketing and sales revenue.

Agenda
• Ensuring corporate AI and data science project success:
A. Plan
B. People
C. Data
D. Technology
F. Risk Management

The challenge of senior management understanding and buy-in
Source:McKinsey, June 2017;Deloitte, May 2018
“41 percent of the 3,000
executives surveyed in a
recent McKinsey Global
Institute study admitted
they’ve yet to adopt AI
because they’re unsure about
how it can help their
organisation.”
“Half of respondents do not
believe their organisation’s
leadership has a clear
understanding of AI”
Deloitte UK survey 2018

But what is AI?
36
You need to educate, excite but set expectations
with your executives

Deep neural networks are being used for unfathomably complex statistical pattern recognition
The big breakthrough in AI - deep learning (ML)
https://www.youtube.com/watch?v=3JQ3hYko51Y
#1 AI is Data Science on steroids

#2 AI is Sensing and Cognition
* Thomas Davenport, AI Advantage,
https://mitpress.mit.edu/books/ai-advantage**
Diagnose diseases from MRIs
Identify persons with facial recognition
Recognise spoken words
Analyse text of legal documents
Identify defects on production line
Improve customer services with bots
Seeing, Hearing, Reading, Understanding, Reasoning

#3 AI is Creativity
* Thomas Davenport, AI Advantage,
https://mitpress.mit.edu/books/ai-advantage**
https://www.theverge.com/2017/10/30/16569402/ai-generate-fake-faces-celebs-nvidia-gan
The era of automated content generation…and deep fakes

#4 AI is Robotics
* https://www.youtube.com/watch?v=g0TaYhjpOfo

#5 AI is Robotic Process Automation (RPA)*
https://youtu.be/FV8lM9SIFQ8
* maybe
RPA allows anyone today to configure computer software, or a “robot” to emulate and integrate the
actions of a human interacting within digital systems to execute a business process.

Management can set frothy expectations…
"I would actually welcome a correction in public
opinion about what AI can and cannot do. This
has happened to me multiple times, where I
would listen to a CEO on stage make an
announcement about what their company is
doing with AI, and then 20 minutes later I’d talk
to one of their engineers, and they’d say, “No,
we’re not doing that, and we have no idea how
to do it.”
Andrew Ng, ex-Google Brain, ex-Baidu
quoted in the WSJ

(c) 2019 Best Practice Artificial Intelligence LtdSource: https://arxiv.org/pdf/1811.11553.pdf
Set expectations - AI is far from “solved”
School Bus
100%
Garbage Truck
99%
Punching Bag
100%
Snow Plough
92%
What happens when AI fails?
There are many legal and ethical issues around AI failures, such as autonomous vehicle crashes
43
For example deep learning has no human like concept of objects

Skills remain a key challenge for projects
Survey of O’Reilly subscribers

Agenda
A. Plan
B. People
C. Data
D. Technology
F. Risk Management

Big trends we are seeing in ML - data science tech
1. Cloud-native - Serverless, containerisation
and microservices
2. Automation of data science tasks with
Gartner Research estimating that by 2020
more than 40% of data science tasks will be
automated
3. Rise of ML as a platform and service model
4. The introduction of AutoML - a system for
automatically searching and discovering model
configurations (algorithm, feature sets, hyper-
parameter values, etc.) and automatically build
the production data pipelines to generate the
features and labels
5. NLP and conversational analytics
6. Explainable AI
7. Increasing control and empowerment of
data science with the business.
h t t p s : / / a i - a u d i t i n g f r a m e w o r k . b l o g s p o t . c o m / 2 0 1 9 / 0 3 / a n - o v e r v i e w - o f - a u d i t i n g - f r a m e w o r k - f o r _ 2 6 . h t m l
Example of an ML platform to
help manage end-to-end
workflow (Algorithmia)

Start-up experience of enterprises can be challenging
Start-ups often underestimate corporate complexity
Speed is an issue
• Why cannot they move as fast as us?
• How long to get sign-off? Contract finalized? Payments made?
Decisions feel needlessly complicated
• Why cannot things be agreed on the spot?
• How many committees / stakeholders do we need to respond to?
• Did we not answer all those questions from compliance 3 months ago?
Corporate ability to implement a challenge
• How many systems do we need to interface with?
• Do we need to work through these internal resources (who are over-worked already)?
• How much communication do we need to do?
Resource imbalance
• There is only so long that the CEO / CTO / key data scientist can be focused in one place
• One-off implementation costs (e.g. legal fees) can be a real challenge

Procurement frameworks are being created to help
ensure effective and responsible AI supplier solutions
For example the WEF is working with UK Government on a new framework
Example: The Institute for Ethical AI & Machine Learning

Agenda
A. Plan
B. People
C. Data
D. Technology
F. Risk Management

Scaling and managing ML models in production is really hard
Data scientists are facing many roadblocks spending much of their time on infrastructure,
deployment and data engineering across the ML end-to-end workflow
h t t p s : / / b i t . l y / 2 F F G F y T
“38% report difficulty in deploying models to the
needed scale as result of resource challenges in
DevOps, lack of infrastructure, etc
“30% report challenges in supporting
different programming languages and
frameworks”
“30% report challenges in model management
tasks such as versioning and reproducibility”

Agenda
A. Plan
B. People
C. Data
D. Technology
F. Risk Management

Much of AI Ethics is not really about the AI
Data issues reflecting broader societal issues such as mirroring our language
Engineering
IncentivesStakeholders
Bluff / crooks
Ethics
Mirror
Uber crash
Tesla automation
IBM Watson healthcare
Boston bus
Job automation
Google Duplex calls
Admiral Insurance
Cadillac Fairview
facial recognition
FB newsfeeds
You Tube video
ranking
Amazon HR
Microsoft’s Tay
US judicial system
Cambridge Analytica
“20% of AI start-ups
have no AI”
Did the
technology
go wrong?
Yes
No User issues
Builder
issues
Were
Human
motives
wrong?
No

Multiple Ethical AI frameworks being created
Over 30 such examples by our recent count
Example: The Institute for Ethical AI & Machine Learning

And the regulators are stepping into the fray
The ICO just announced a draft AI audit framework for comment
Under GDPR today there are clauses that are
relevant to the use of AI. For example:
• Article 22(1) of the GDPR limits the
circumstances in which you can make solely
automated decisions, including those based
on profiling, that have a legal or similarly
significant effect on individuals. (e.g.
employment and loan decisions)
• Decisions need to be explainable and
individuals have the right to opt out.
The ICO has just released this AI audit
framework for comment. Designed to:
• Support the work of the ICO investigation
and assurance teams to assess data
controller compliance
• Help guide organisations on the
management of data protection risks arising
from AI applications
h t t p s : / / i c o . o r g . u k / f o r - o r g a n i s a t i o n s / g u i d e - t o - d a t a - p r o t e c t i o n / g u i d e - t o - t h e - g e n e r a l - d a t a - p r o t e c t i o n -
r e g u l a t i o n - g d p r / a u t o m a t e d - d e c i s i o n - m a k i n g - a n d - p r o f i l i n g / w h a t - d o e s - t h e - g d p r - s a y - a b o u t -
a u t o m a t e d - d e c i s i o n - m a k i n g - a n d - p r o f i l i n g /

Successful AI-driven projects needs more than just data science
AI & data science requires alignment of six factors to ensure it realises
its value across an operation
Have the
right plan
Mobilise
the right
people
Have the
data
Deploy
the right
tech
Make it
operat-
ional
Manage
the risks
Agree project
ownership +
governance /
oversight
Ensure domain
expertise and
cross-functional
buy-in in place
Ensure skilled
team in place –
whether
internal or
vendor
Plan how to get
the right data at
right stage of
preparation
(e.g. labeled
and complete)
Consider what
bias issues
potentially
faced
Ensure relevant
regulations
complied with
Plan approach,
testing and
refinement of
key
algorithm(s)
Ensure right IT
architectural
stack in place
Check impact of
tech choices on
broader tech
stack
Decide how
prediction will
lead to action
(degree of
automation)
Ensure
measurement
and feedback
loops in place
Map and
manage
barriers to
deployment
Ensure key
staff bought in
Identify
potential
regulatory /
legal issues (eg
GDPR)
Map
stakeholder set
and prepare
clear comms
plan
Ensure
alignment and
information to
empowered
’ethics’
governance
Be very clear
what you want
to achieve
Understand
what you need
to predict /
optimise – and
levels of
confidence
required
Agreed metrics
that measure
success
1 2 3 4 5 6

Agenda
A. Plan
B. People
C. Data
D. Technology
F. Risk Management

Can your startup cross the commercial divide from
technology to the enterprise?

Raising money - AI is a different beast for most UK investors
VCs are not that comfortable evaluating AI opportunities but are getting better
The past five years of VC investing has really been focused on B2C marketplaces, commence and mobile
AI investment opportunities are harder to evaluate. Why?
• They are often based on academics and their research which is a DNA anathema to VCs
• They are often“deep tech” and it is hard to find real experts to opine
• Go-to-Market (GTM) is enterprise and not consumer digital marketing funnel
• The market dynamics are evolving very quickly
• Massive talent war
• Scale is critical to AI startups and data scale is often with incumbent corporates
• Long product development cycles with hard to measure progress.

Make it easier for VCs to evaluate your AI startup
1.What is the business use case you are solving?
2.Is it a valuable use case?
3.Can your AI company be a global leader and not be squashed by tech giants?
4.Do you have unique domain knowledge?
5.Do you have access to unique, large and quality data sets?
6.Do you have access to AI talent?
7.Do you have a unique and proprietary technology? Why is it good?
But make sure your VCs have patience and are smart about deep tech investment profiles.

The foundations of most AI startups
(1)
Get Data
(2)
Get
Talent
(3)
Get
Domain
Expertise

Cross the AI commercial divide from technology to the enterprise
Six key factors for success:
1. Don’t move too fast and break things; embrace responsible AI
2. Solve really high value use cases, not nice to haves
3. Master B2B enterprise sales and learn calculated patience
4. Translate AI for the real world
5. Lower the barriers to a trial
6. Technical founders need to hire business people.

To learn more search for this article

AI Management Consultants
Helping you create competitive
advantage with AI
Simon @ bestpractice. Ai
+44 7824 557979
63
www.bestpractice.ai
@simongreenman

2019 London Data Science Festival. Making Money in AI, Machine Learning and Data Science.

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