A brief overview of artificial intelligence (AI), followed by a few examples of practical use within small businesses, large enterprises, and nonprofits.

1. Copyright © 2018, DATALOG, INC., all rights reserved. All trademarks are the property of their respective holders.
Artificial Intelligence
Introduction and
Use Cases for Business
June 2018

2. 10:30 – 10:45 Get acquainted
10:45 – 11:30 What is AI anyway? How is it being applied today in business?
11:30 – 13:00 Eat and explore (group discussion)
Key use cases for AI in business
Machine learning methods that generate insights
Intelligent process automation
More personalized customer experiences
What’s on today’s agenda?

3. Your facilitators
Jack Crawford
Managing Partner at Datalog.ai, a
global consulting and solutions
firm which helps growing
businesses attain an unfair
competitive advantage with AI
George Kings
Founder at Rethink Process which
help companies save money by
automating manual processes
and making operations more
efficient
Bob Anderson
Director of Customer Engagement
at Rethink Process where he
helps customers define the
essential value in a process,
manage change, and permit a
business to focus on its day to
day operations

4. A short history of
Artificial Intelligence (AI)
& Machine Learning (ML)

5. Arthur L. Samuel, 1959
Possible Moves, 5x1020 (500,000,000,000,000,000,000)

7. Tom Cook, King-Casey
“History has proven that technology
always takes longer to implement
than anticipated, and often
produces unintended consequences
when it does so.”

8. From 1959 to 2016
CHECKERS
Possible Moves, 5x1020
500,000,000,000,000,000,000
With consideration of a
human making one poor
move, 5x1014
500,000,000,000,000
GO
More than the number of
atoms in the entire universe

10. Deep Learning Demystified, March 2018, NVIDIA

15. 15

16. AI systems fall into six categories *
* Applied AI, a Handbook for Business Leaders, 2018, Yao, Jia, and Zhou
Act Lowest level (rule-based “automata”). Fire alarms, cruise control
Predict Data quality dependent. Next best action, anomaly detection
Learn Unsupervised learning (or hybrid). Real-time decision support
Create Limited largely to audio/visual. Music, image fakery, CAD
Relate Simulated empathy. Virtual assistants, chatbots, smart things
FUTURE: Master & Evolve Abstract concepts & self-improving. SCARY SCI-FICTION

17. Act / Predict / Learn
It’s been a
journey of
discovery and
application

18. 18

19. When an AI model learns, does it need our help?
How?What
Machine
Learning
Supervised
Classification
Regression
Unsupervised
Clustering
Association

20. Learning Systems
Adapted from Applied AI, a Handbook for Business Leaders, 2018, Yao, Jia, and Zhou
Data
Prediction
Judgement
Action
Outcome
Feedback

22. Classifying systems create clusters?
So it’s both supervised and unsupervised?
A map based on 2013
Wikipedia article data
Similar topics are
close to each other
(Image, Denoir, Wikimedia
Commons)

24. ARTIFICIAL INTELLIGENCE
DATA SCIENCE Human decision
MACHINE LEARNING
Data Features
Prediction
Insight
Action
Transformation
Analytics
Automated decision
Unsupervised learning
Supervised
learningDeep learning
“What’s the difference between data science, machine learning, and artificial intelligence?” PaulvanderLaken.com January 16, 2018, Paul van der Laken
Data science produces insights
Machine learning produces predictions
Artificial intelligence produces actions

25. Shivon Zilis

26. Using Artificial Intelligence
in Business
26

27. McKinsey
Quarterly
“What AI can and
can’t do (yet) for
your business”
January 2018

29. Employee facing and customer facing AI
Corporate Internal Uses
šHuman Resources
šFinance, Legal and Compliance
šRecords Management
šInformation Technology
Product Related
šSales
šMarketing
šCustomer Support
šManufacturing

30. Selected opportunities
Sales and marketing
šNext best action
šSales-rep coaching
šCustomer switch
Supply chain
šSupplier commitment prediction
šConversational bots
Customer care
šPropensity to call
šConversational AI
šPersonalization
Research and development
šKnowledge retention
šSuccess factor identification

31. AI agents can
learn
…
what your
customers
want

32. Personalization for shoes … who knew?

33. Behind the
scenes …

34. Discovering what
and why …

35. On the phone, or at work …

36. “Anticipation is a huge
plus of what AI can do …
and how we work with
and create experiences
for guests”
~ Caspar Mason, VP at Jack Morton

37. 37
Image: How will Artificial Intelligence in Hotels impact the Operational Dynamics and Customer Experience?, Medium, Maruti Techlabs
Hospitality
In-room concierge
Real-time personalized
recommendations
Housekeeping process
improvement
Early alerts to service issues

39. “ NATURAL–LANGUAGE interactions
will soon become the norm in
terms of how people expect to
interact with technology ”
~ Gartner Research

41. ¾ of millennials
would rather become
mute than lose the
ability to TEXT
~ 2017 OpenMarket survey
75%

42. 75%

43. Virtual assistance and engagement
“25% of customer service operations are
expected to feature virtual customer
assistant and/or chatbot technologies by
2020.”
~ Gartner, Gene Alvarez, Managing Vice President, February 2018

44. Automated messaging agents
simulate human interaction.

45. Automated
Messaging Agents
Core
Systems
Custom
AI
Models
Natural Language
I would like
to transfer $350 to my checking account
to cover my automatic loan payment

48. Advantages of automated agents over other
forms of engagement
1. Immediate and always available
2. Easier to support and integrate
3. Natural, personalized interaction
4. Gain insights from your customer’s conversation
(voice of the consumer)
5. Optimize investments, not just reduce costs

50. Patients: Virtual assistants can complete a
“Circle of Care”
Health goals
Medication adherence
Clinical appointments
Notifications
Questionnaires

52. Hear the patient’s voice
Hi Janet, how are you today?
I don’t feel well
Did you take your medication this
morning?
I think so
Do you feel warm or cold?
Neither, but I’m hungry

53. In closing
Some helpful advice

54. Jeff Erhardt, VP of intelligent systems at GE
“Assess the value-producing, data-
driven workflows within the business
to determine where to define
existing data and how to
strategically implement it.”

55. Copyright © 2018, DATALOG, INC., all rights reserved. All trademarks are the property of their respective holders.
Thank you
jack@datalog.ai
linkedin.com/in/jackcrawford
georgek@rethinkprocess.com
linkedin.com/in/georgekings