The document discusses artificial intelligence (AI) and its applications for customer experience (CX). It provides an overview of different AI technologies and categorizes them using the AI Knowledge Map. This map classifies AI technologies according to their paradigm (symbolic, statistical, subsymbolic) and problem domain (reasoning, knowledge, etc.). The document recommends using this map to identify suitable AI technologies based on the type of input needed and problem to be solved. It also notes challenges for AI adoption such as trust, security, privacy and the need for explainability.

1. Luisa Mich
Bruneck/Brunico - South Tyrol, Italy Campus, December 12-14 2018
CBTS2018 - Consumer Behavior & Tourism Summit
Artificial Intelligence
for Customer Experience
AI4CX

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
AI for CX (AI4CX)
• AI. Beyond bits and bots (definition, history,
…), Wien 2017
• How can artificial Intelligence (AI) “support”
customer experience (CX)?
• What can AI do for your organization and your
customers?
• Challenging questions as AI is not one thing,
or tool: it offers a range of technologies
“mimicking” human intelligence

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
AI toolbox
A jungle, not an
Italian Garden

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
AI adoption
• “AI solutions are growing as one of the hottest
investments across industries. 37% of
organisations have already started
implementing AI for CX, while another 41%
have plans to implement it by 2020 to boost
their CX capabilities. What does AI mean for
CX and how do we use it to deliver results for
our business?”
(https://cxartificalintelligence.iqpc.co.uk/)

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
Business challenges
“As customer expectations increase, enterprises
will need to keep pace by using machine
learning and artificial intelligence to offer a
personalized experience”
(Gal Oren, CEO Zoomin)
Companies are using AI to give employees new
analytical tools to improve CX and discover new
possibilities for products, services and business
models

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
• “AI is a powerhouse when it comes to
customer experience. This technology not only
allows companies to create faster, more
personalized experiences, but to gain insights
and advantages to lead customer experience
in the future.”
(Blake Morgan, 2018)

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
CX vs Type of Information System
• Narrow CX: “voice of the customer and other
data used to create smarter, more actionable
CX ecosystems”
-> CX as an evolution of CRM and CEM
• Sources of information:
– Surveys (conversational) -> feedback
– Interactive spaces (contact centers) -> interaction
– Social networking websites -> social
(Sid Banerjee, Clarabridge)

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
• General CX: “Customer experience
encompasses every aspect of a company’s
offering—the quality of customer care, of
course, but also advertising, packaging,
product and service features, ease of use, and
reliability”
(Andre Schwager and Chris Meyer, 2007)
-> CX needs a full fledged (cyber-physical)
information system; AI applied for all the value
chain activities

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
Relevant issues
• To plan and design effective systems
exploiting AI for CX it is necessary to take
into account that:
–there are many AI areas and applications
–there are many and very different
technologies, at different maturity level

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
For a successful AI4CX
• Identify your customer’s needs
• Identify where to integrate AI into the CX
strategy
• Support personalization and proactive services
• Integrate AI into the analytical strategy
• Missed issue: know and identify AI
technologies

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
Classifying AI technologies
• There are multiple classifications and models
to represent AI toolbox
• Typical classifications refer to the
functionalities …
• The AI Knowledge Map (AIKM) by Francesco
Corea: a comprehensive representation to
illustrate where AI is used (what is out there)

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
The AI Knowledge Map (AIKM)
• Technology solutions classified according to
the AI paradigm and the AI problem domain
• 6 AI paradigms: classified into 3 approaches –
symbolic, statistical and subsymbolic - used to
solve specific AI problems
• 5 AI problem domains: type of problems AI
can solve (capabilities)

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
Corea F., AI Knowledge Map: how to classify AI technology, Aug 2018

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
AI problem domains
• Reasoning: capability to solve problems
• Knowledge: ability to represent and understand
the world
• Planning: capability of setting and achieving goals
• Communication: ability to understand language
and communicate
• Perception: ability to transform raw sensorial
inputs (images, sounds, ...) into usable information

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
AI paradigms: symbolic approaches
• Logic-based tools: used for KW representation
and problem-solving
• Knowledge-based tools: based on ontologies
and huge databases of information and rules

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
Symbolic technologies
• Robotic Process Automation (RPA):
technology that extracts the list of rules and
actions to perform by watching the user doing
a certain task
• Expert Systems: based on hard-coded rules to
emulate the human decision-making process
• Inductive Logic Programming (ILP): formal
logic used to represent a database of facts and
formulate hypothesis deriving from those data

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
AI paradigms: statistical
approaches
• Probabilistic methods: tools that allow agents
to act in incomplete information scenarios
• Machine learning: tools that allow computers
to learn from data

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
Statistical technologies:
• Decision Networks: a generalization of
the Bayesian networks representing a set of
variables and their probabilistic relationships
through a map (directed acyclic graph)
• Probabilistic Programming: does not force to
hardcode specific variable but rather works
with probabilistic models

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
• Neural Networks: loosely modeled after the
neuronal structure of the human brain
(include Deep Learning and Generative
Adversarial Networks)
• Computer Vision (CV): methods to process
digital images (activities recognition, images
recognition and machine vision)
• Natural Language Processing (NLP): handles
natural language input (language generation,
language understanding & machine
translation) Note: these technologies uses a
combination of approaches

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
AI paradigms: subsymbolic
approaches
• Embodied intelligence: which assumes that a
body (or at least a partial set of functions such
as movement, perception, interaction, and
visualization) is required for higher
intelligence
• Search and optimization: tools that allow
intelligent search with many possible solutions

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
Subsymbolic technologies
• Autonomous Systems: at the intersection
between robotics and intelligent systems (e.g.,
intelligent perception, dexterous object
manipulation, plan-based robot control, etc.)
• Distributed Artificial Intelligence: solve
problems by distributing them to autonomous
interacting “agents” (multi-agent systems,
agent-based modeling and Swarm
Intelligence)

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
• Ambient Intelligence: demands physical
devices to sense, and respond with context
awareness to an external stimulus (usually a
human action)
• Evolutionary Algorithms: a subset of
evolutionary computation that uses
mechanisms inspired by biology (e.g.,
mutation, reproduction, etc.) to look for
optimal solutions (Genetic algorithms)
• Affective Computing: deals with emotions
recognition, interpretation and simulation

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
How to use AI Knowledge Map
• Narrow vs general applications: to evaluate
maturity and risks
• Type of input: to adopt the most adequate
(innovative) technology
• Type(s) of problem domain: to identify (users’
and company’s) needs
• New applications: to classify and evaluate
them

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
Recommendations
• AI challenges: trust, security, privacy, safety
– Ethical issues
– Explainability and accountability
– Biased data

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Luisa Mich – AI4CX CBTS2018 Summit – Bruneck/Brunico, December 12 –14 2018
• Ready or not, AI is coming to us!