AI is super-exciting. Especially the field of machine learning (and sub-field of "deep learning" - an area I've studied for over 25 years). AI and ML offer to revolutionize the way we monitor, and model, and generate answers from all the big and small data that is swirling around of us. But it’s not going to happen overnight. Especially since many organizations still struggle with the best ways to select, apply and monetize AI for practical, everyday use. This presentation aims to cut through the hype and lays out 7 rules for AI project sponsors, managers and practitioners alike - each supported by examples and resources to learn more, as well as key influencers and vendors worth checking out.

1. small data group
7 RULES FOR
SURVIVING THE AI
HYPE MACHINE
Practical insights, research and
frameworks for organizations
looking to apply smart, data-
driven apps to everyday work
Allen Bonde April 2018 update

2. 1,931 companies
$22B in funding
Dollars flowing into AI
Venture Scanner Q3 2017
300% increase in corp
investment
From 2016 to 2017 (Forrester)
© Allen Bonde 2018 / Small Data Group

3. Taxonomy of AI + ML
Intelligent
Agents
© Allen Bonde 2018 / Small Data Group
Bayes
Regressions
Clustering
Reasoning Systems
Programmed Supervised Unsupervised
PCA
Fuzzy Logic
Control Systems
Planning
Systems
Decision Trees
Simple Neural
Nets
Reinforcement
Genetic
Algorithms
TD Learning
Recommendation
Engines
Expert
Systems
NLP
Multilayered NNs
“classic AI”
Machine
learning
Deep
Learning
RNN CNNMLP
Learning Systems
Knowledge
Discovery
AI

4. Seeking everyday AI
AI and machine learning (ML) offer to revolutionize
the way we monitor, and model, and generate
answers from all the big and small data* swirling
around us. But it’s not going to happen overnight.
While organizations look forward to “big wins” with AI,
they also need to start small and look for opportunities to
select, apply and monetize AI for practical, everyday use.
The following 7 rules are offered as a starting point.
© Allen Bonde 2018 / Small Data Group
*defined here: https://smalldatagroup.com/2013/10/18/defining-small-data/

5. That Taxonomy slide was awesome, right? But unless you
are selling to data nerds, your customers don’t care
about your technology. Really.
Its generally more important to focus on the “why” than
the “how” when selling the value of your AI initiative or
solution to internal and external stakeholders. Why is the
problem hard to solve with traditional approaches? Why
is AI more repeatable/scalable vs. one-off solution? What
unique value does AI provide? Where (in the org) are
other AI opportunities? How will you show ROI?
Rule#1: Sell Value Not Approach
DEEP DIVE: check
out Simon Sinek’s
books and 2009
TEDx Puget Sound
presentation
© Allen Bonde 2018 / Small Data Group

6. Creating a general-purpose thinking machine is hard. So
get specific when scoping your first project.
Creating an intelligent agent (or bot) that automates a
single or small set of everyday, repetitive, “standard”
tasks is a lot more tractable.
Just as the key to early AI was finding narrow but high
value applications like streamlining problem resolution in
call centers or processing loan applications, the same
approach applied to today’s AI is equally important.
Rule#2: Scope Matters
DEEP DIVE: Using
small data thinking
can help narrow
focus – are you
looking to
understand OR
engage users?
Where are customers
in their journey?
See Small Data: a
New Design
Philosophy on
SlideShare
© Allen Bonde 2018 / Small Data Group

7. It’s not easy to get ML right the first time. Or second.
Experimenting with tools like RStudio or RapidMiner or
DataRobot can help – and embracing an agile approach
is the way to go – whether you are a coder or not.
Neural network models aka “deep learning” also hold
great promise, yet training them is both an art and a
science (aka hard). One solution is Reinforcement
Learning, which reduces the need for training pairs and is
the technique behind the AlphaGo program.
Rule#3: ML is not Magic
DEEP DIVE: check
out Rich Sutton’s
Reinforcement
Learning: An
Introduction, 1998
© Allen Bonde 2018 / Small Data Group
FUN FACT: if you like C programming you can start with the TD learning code I
created here: http://www.incompleteideas.net/td-backprop-pseudo-code.text

8. Data-driven insights are key to getting closer to
customers. And to understanding context and needs at
each stage of the buyers journey.
The appeal of AI (supported by ML) is to compress the
timeline and cost of producing and delivering insights –
at scale – so they are readily available to front line teams
and customers themselves via smarter search, messaging,
or personalized experiences. Thinking bigger? For a no-
hype view of how big data fits in the picture, a great
follow is Kirk Borne at Booz Allen (@KirkDBorne).
Rule#4: Data is King
DEEP DIVE: 10
vendors offering AI
powered solutions
for optimizing online
commerce
BloomReach
Celebros
Coveo
EasyAsk
Episerver
Evergage
Nextopia
PureClarity
Scalefast
SLI Systems
© Allen Bonde 2018 / Small Data Group

9. Think of AI as the coach rather than the player for front
office functions (whether you are providing self-service or
assisted service) – even as other algorithms get better at
automating things behind the scenes.
The concept of conversational intelligence extends this
idea and is worth exploring. Check out the work of Dan
Miller (@dnm54) and Mitch Lieberman (@mjayliebs), as
well as Dan Roth + team at Semantic Machines (behind
some core tech for conversational AI) to learn more.
Rule#5: People Matter
DEEP DIVE: 5
vendors offering AI
tools to improve call
center or other
assisted service
functions
Cogito
Interactions
LivePerson
Salesforce
TalkIQ
FUN FACT: 31 percent of major companies are using AI in customer service (Tata)
© Allen Bonde 2018 / Small Data Group

10. Many of today’s most successful smart apps have AI or
machine learning “in them” but all the user sees is a
helpful product recommendation or a tailored ad or an
alert on their phone. That’s the point.
There are many ways to add intelligent capabilities to
your apps, from open source frameworks and engines
from Apache, Eclipse, TensorFlow, etc., to dev platforms
from IBM and Microsoft with tools for ML or search, to
consultancies like Deloitte and SapientRazorfish. Looking
for the latest buzz on various tools and ML models? A
great follow is KDnuggets (@kdnuggets).
Rule#6: Embedding drives Adoption
DEEP DIVE: 5
vendors providing
embedded BI and
analytics tools
Domo
Logi Analytics
OpenText
Sisense
TIBCO Jaspersoft
© Allen Bonde 2018 / Small Data Group

11. Think global, act local. AI can solve really big problems.
But there’s many smaller ones worthy of attention in
functional areas like marketing, e-commerce, customer
support and even human resources or R&D.
Within marketing, there are proven use cases for applying
data and knowledge (see the piece I did for DMN), and
readers should look at the excellent roundup in
Forbes.com on ML and marketing by Louis Columbus
(@LouisColumbus), and also check out the Marketing AI
Institute (@MktgAi) for other coverage.
Rule#7: Focus on Everyday Work
DEEP DIVE: see
McKinsey’s work on
“where machines
could replace
humans – and where
they can’t (yet)”
© Allen Bonde 2018 / Small Data Group

12. Allen Bonde is a 5-time CMO, long-time AI researcher and advisor to
disruptive start-ups and global brands. He started his career as a data
scientist in the telecom sector, applying AI and machine learning to
network management, coding and testing early reinforcement learning
techniques, and receiving a patent for some of this work.
He has held executive roles at Repsly, Placester, OpenText and
eVergance (now KANA), and was cofounder of Offerpop (now Wyng).
He’s also been an analyst and practice leader at McKinsey, Yankee
Group, Extraprise and DCG. He attended Brown, UVA and WPI.
Read more about Allen’s views on building smart, data-driven apps
and applying small data thinking to marketing and design at
www.smalldatagroup.com
Contact Allen at allen@allenbonde.com or via Twitter (@abonde).
About Allen
© Allen Bonde 2018 / Small Data Group