Kick-Off Presentation of IBM Challenge Zürich

IBM Presentation

1. IBM watsonx
Gen AI Challenge
Welcome Event &
Lecture Information
Zurich, 27.2.2024

2. Agenda

18:15
Welcome speech by Christian Keller CEO IBM Switzerland
Course introduction by Dean Heizmann
18:50
Use case presentations:
PostFinance
Twerenbold
Freitag
Schweizer Armee
Victorinox
Komax Group
Zweifel
Hirslanden
SIX Group
19:50
Questions
Closing & Aperitif

3. Course Description

•Real use cases – real partners
•All use cases in the field of generative AI
•Your task is to form groups and think of a solution
•Develop a PoC
•Present it to the use case provider
•We provide you with:
oTechnical tools and necessary education
oGuidance on how to tackle your use case

4. General Information
•Cross university Master's course - 6 ECTS
•6 Assignments (Not graded but indispensable)
•Lectures online – Tuesday's 6:15PM to 8 PM
•Lectures are recorded
•Midterm presentation (ungraded): 23.4.
•Final presentation:29. 5. & 30. 5.
•Best teams receive an invitation to visit the IBM Research Lab
in Rüschlikon with an aperitif and a certificate
•Unregister deadline: 1.3. 12:00

5. Goals and Requirements
•Learning Goals:
oGenerative AI in business
oManagement of an AI project
oDevelopment of a Gen AI PoC
•Requirements:
oNo coding needed but helpful
oTechnical affinity required
oCommunication and taking the initiative is key!
•Target group: Wide range of master students from several
economics and technical backgrounds.

6. Evaluation
Grading happens in semi steps
1.Are the main requirements satisfied?
2.How are the technical possibilities explored?
3.How was the solution packaged and presented?

7. Schedule
Welcome Event
27.02.2024 |6:15 pm - 8:15 pm
General introduction, goals and expectations,Apero (aperitive)
and networking
Introduction to generative AI in business
05.03.2024 |6:15 pm - 8:15 pm
Technical overview, use cases, and challenges of generative AI
Large Language Models and their use cases
12.03.2024 |6:15 pm - 8:15 pm
Watsonx.ai overview & technical lab deep dive
Chatbots and user interaction
19.03.2024 |6:15 pm - 8:15 pm
Watson Assistant overview & technical lab deep dive
Foundations of a successful AI project
26.03.2024 | 6:15 pm - 8:15 pm
Requirement engineering and Design Thinking for AI projects
RAG
09.04.2024 |6:15 pm - 8:15 pm
Watson Discovery andNeuralseekoverview
Technical Deep Dive
16.04.2024 |6:15 pm - 8:15 pm
Generative AI: Transformers, GPT, training, deployment.
embeddings, vectors
Midterm Presentations
23.04.2024 |6:15 pm - 8:15 pm
Presentation of current state of project
Integration Possibilities
30th April 2024 |6:15 pm - 8:15 pm
Overview of possibilities and specific integration examples
Q&A
7th May 2024 |6:15 pm - 7:15 pm
We’re here to support you with your questions; Questions to be
handed in beforehand
Q&A
14th May 2024 |6:15 pm - 7:15 pm
We’re here to support you with your questions; Questions to be
handed in beforehand
Q&A
21th May 2024 |6:15 pm - 7:15 pm
We’re here to support you with your questions; Questions to be
handed in beforehand
Final presentation 1
28th May 2024 |6:15 pm - 8:15 pm
Final Group Presentation of Solution
Final presentation 2
29th May 2024 |6:15 pm - 8:15 pm
Final Group Presentation of Solution
Final Event
TBA | TBA
RüschlikonLAB Tour for challenge winner groups

8. Team

9. Next Steps
•You will receive an invitation to Slack and Box
•Use Slack to look for team members or to find a group (4 to 5 per group)
•Use case descriptions are in the box
•Write an email to [email protected] with:
•Group members (email, university, spoken languages and course of study)
•Your top 5 use case preferences
•We will have to distribute use cases evenly considering required
languages and technical difficulty
•Important: To unregister from the course write an email to
[email protected] 1.3., 12:00 AM!
•Please do it asap so we can free slots for students on waiting list!

10. Questions?

11. USE CASES

12. About PostFinance
2.5 million
Customers
Total Assets
114 billion
Swiss Francs (CHF)
1.3 billion
Payment Transactions
34Branchesand
57Consulting Offices ~ 3,600
Employees
Every year567,026Coffeesareconsumedacrosslocations.
Average per day: 2,250
tellmemore…
let’sconnect…

13. BUSINESSPROBLEM DIRECTION OF SOLUTION
Scenario:
Employees at the Customer Center PostFinance aim to resolve
client issues, offer new products/services, or enhance existing
client relationships through customer interactions. The goal is to
gain a deeper understanding of the reasons customers reach out
to the Customer Center.
Users:
Customer Center, Product Owner, Product Development
Pain points:
1.Lack ofunderstandingofcustomerneedsand problems
2.High, manualeffortforpost-processingofcalls
3.Large, previouslyunusedamountofdata
Goal of the solution:
Automatically convert and analyze Customer Calls to identify
intents and suggest product or service enhancements.
Scope to address:
Focus on Swiss German Customer Calls. The processincludes:
1.Transcribingspoken recordingstotext
2.UsingAI modelstounderstandand anayzeconversationcontent
3.Displayingcallintentsin a UI ofchoice
Data:
Syntheticaudio transcripts of customercalls
ClientClarityPostFinance
Technical Difficulty: 1 Language Required: German
Use Case Value in a Nutshell
2.7 million
CustomersCalls in
2023 at PostFinance CC
Transcribing
Customersasksabout
savingaccountinterest
Client Clarity
Customersseeksbetter
investmentoptions

14. •Classical group tour operator
•1‘000 Trips
•3‘700 Departures
•50‘000 Bookings
•+ 3‘000 pages of print brochures
•Own busses & own river cruise boats
•Online booking ratio >70%
•130 years old family-owned business
•Digital strategy
•Improve personalization of all trave services
•Increase automation / efficiency
•No own coding resources
About Twerenbold

15. BUSINESSPROBLEM DIRECTION OF SOLUTION
Scenario:
A customer has request and needs support from a Twerenbold sales
agent
Salas assitant:
•Use Twerenbold’s online Documentation to serve clients' interests
Customer requests
•Change of personal information
•Travel information
Users:
Twerenbold Customers with an active/valid booking, prospects
Pain points:
•Those simple requests consume valuable time from our agents
•Customer can call/answer during office hours only (9-12; 13:17 )
•Caller identification with GenAI (elderly people)
Goal of the solution:
•Customer requests are done manually. With the support of GenAI,
we aim to achieve a higher client self-service adoption rate.
•Office hours are restricted to 09.00-17.00; GenAi should increase
service levels toward a 24/7 client service hub.
Scope to address:
• Focus on clients with an active booking in place.
Data needed:
•Client booking data provided by Twerenbold AG
•Customer Data / Traveler Data
•Trip information
•Website / Catalogs (API)
Travel Advisor
Technical Difficulty: 1 Language Required: German

16. ABOUT FREITAG
<<INTELLIGENT DESIGN
FOR A
CIRCULAR FUTURE>>

17. BUSINESSPROBLEM DIRECTION OF SOLUTION
Scenario:
"Guuru" isa chatapplicationthatinvolvesthecommunity. Our
own communityisourfirstportofcallwhena customerhas
questionsand wantstochatwithsomeone. So far, thedata
generatedhasneitherbeenanalyzednorusedforourown
chatbotsolution.
Users:
•Employees
•Community
•Customer
Pain points:
Lack of insight into user behaviour and customer problems.
Lack of quality assurance over our “Guuru” community.
Goal of the solution:
•Evaluatethedatafromthetool
•Chatbot asan outputagainstwhichwecananalyzethedatain the
tool.
Scope to address:
•2023 untiltodaytranscripts
•focuson English requests
•metadatascopewill bedefined
•LLM isneededtogeneratetheoutputofthequestionstobe
analyzed

Data needed:
Tosolvethischallenge, wewill eitherprovideyouwitha SQL
databaseoran API accesstopull thedatafromthesystem
WhattheF-uckaretheytalkingabout?
Technological Difficulty: 1 Language Required: English

18. Kommando CyberAuftrag
The commandoCyber...
-responsiblefortheprovisionofpower in thecyberand electromagneticspace
(CER). This in theareasofactionmanagementand mission-criticalICT;
-ensurespreparednessand assessesthefeasibilityofCERs in theoperational
spheres;
-protectsthemission-criticalICT infrastructureofthearmyin theCER;
... in favouroftheSwiss ArmedForces and theirpartnersin theSwiss security
network.

19. Kommando CyberMission
Wegivethearmedforcesthenecessaryknowledgeand decision-
makingadvantagein all situations.
Wecombineinnovation, technology, know-howand enthusiasm
formissionfulfillmentin a powerful KdoCy oftheSwiss Armed
Forces.
Weprovidetheexpectedand demandedservicesfortheSwiss
Army, theSVS and partneralwaysprecisely, tothepoint,
coordinated, robust andsecure.

20. BUSINESSPROBLEM DIRECTION OF SOLUTION
Scenario:
This PoC aims to demonstrate how Gen AI can be utilized to
aggregate, analyse, and disseminate complex and rapidly
changing information related to military technology
advancements, use cases, capabilities and global defence trends.
Users:
Cyber command: Anticipation & Innovation
Pain points:
1.Data Overload
2.Accuracy and Reliability
3.Predictive Analysis and Trend Identification
4.Resource constraints
5.Adapting to Technological Advancements
6.Cross-Departmental Coordination
Goal of the solution:
Developing an anticipation engine which allows Swiss Armed Forces
Cyber Command to navigate future trends in accordance to
predefined business rules. Create a user interface to communicate
findings.
Scope to address:
Define Sources ,Scrapping, Matching, Recommendations,
Visualization of output
Data needed:
Use algorithms are designed to collect data from diverse and reliable
sources. Including intelligence reports, global surveillance data,
research publications, and real-time news feeds. The AI system then
processes this data, identifying key patterns, trends, and actionable
insights. Swiss Army Business Rules. The sources will be distributed
to the participating teams.
Swiss ArmedForces CyberCommand AnticipationEngine
Technical Difficulty: 3 Language Required: German, English

21. About Victorinox
188419451989 / 19992005

22. About Victorinox
2021
Launch of our digital
learning platform C.A.R.L.
We create a positive and
vibrant learning culture
that embraces growth and
transformation.
•200+ trainings
•2.300 users worldwide
(1.850 internal users)
•8 languages

23. How can generative artificial intelligence support us incustomizingouruser’s
experience(duringonboarding) on thedigital learningplatform?
About thechallenge
Internal Users
•Indirectlyproductiveemployees
•Sales subsidiaries(worldwide)
•Own retail
•Directlyproductiveemployees
External Users
•Distributorsand Retailers
total = 2.300 usersworldwide
Scenario
•50+ new trainings every year
•Mandatory / recommended /
voluntary trainings
•Automatic assignment to new
user accounts (all at once) during
onboarding
Pain Points
•High number of trainings
•Missing overview of where to
start and where to end
•Depending on the
function/position number of
trainings to complete varies
•No automatic recommendations
on trainings matching the
employee’s job profile / work
topics

24. About thechallenge
Scope
•Creation of a recommendation
engine for general trainings (e.g.
Code of Conduct) & role-specific
trainings (e.g. S&OP)
•Focus on indirectly productive
employees (=office workers) and
Sales Subsidiaries; own retail
and directly productive
employees as target group are
out of scope
•Ensure that the ideas/concept
developed may also be
applicable to onboarding of
external users.
Goal of the Solution
•Provide orientation on their
learning/onboarding journey
•Ensure relevance (match &
recommend trainings based on
user’s position within company,
job profile, preferences &
interests)
•Automatically adjust the degree of
difficulty to ensure users stay
engaged and challenged
•New approach (Victorinox already
uses Bing AI Enterprise which
could be integrated/applied;
recommend other options?)
Data Needed
•Training Descriptionsand Target
Group Information
•User Feedback / Ratings
•User Profiles
Technical Difficulty
Language
English
German isbeneficial

25. Questions?
Joël Maier
Digital Learning Manager &
UX Architect
Katharina Neumann
Learning Consultant &
Project Manager
Victorinox AGSchmiedgasse 576438 Ibach-SchwyzSwitzerland
T [phone]www.victorinox.com

26. About Komax
Komax Group: Global market and technology leader in
solutions for automatic and semiautomatic wire and
cable processing.
•Cutting and stripping
•Attaching connectors
•Testing
•Production planning – MES software
•And much more
In virtually every industry:
•Cars, trains, airplanes
•Electronic devices, communications equipment,
appliances
•Medical devices
•Etc.
Wire Processing
TestingMES Software

27. BUSINESS PROBLEM DIRECTION OF SOLUTION
Scenario:
The aim is to convert solution proposals from existing service
cases into know-how articles. The service cases are available as
e-mail dialogs between customers and service technicians.
Users:
Internal technical service team
Pain points:
•Very large amount of data that is not manageable by hand.
•Complexity is too high to be solved with a normal algorithm.
Goal of the solution:
Solution approaches (know-how articles) for previous service
problems that are as meaningful as possible.
Scope to address:
The LLM should be able to recognize — independently of the dialog —
how to fill the know-how article (Excel).
Data needed:
•Export of the service cases as an e-mail dialog
•Example of a know-how article
Service Cases
Technical Difficulty: 2 Language Required: German, English

28. •founded in 1958 and family-owned ever since
•400 employees
•more than 70 types of chips & snacks
•10’000 tonsof chipsand snacks
•Swiss madewithswissingredients
•SBTicommitted
About Zweifel Pomy-Chips AG

29. BUSINESSPROBLEM DIRECTION OF SOLUTION
Scenario:
This PoC aims to demonstrate how GenAI can be utilized to:
1.segment complaints accordingly (ideally also taking provided
images into account)
2.create responses for the customer: the answers generated by
the GenAI engine should be provided with an accuracy rating
such that selective and manual checking is possible for
ambiguous complaints
Users:
Zweifel consumer complaint department
Pain points:
•time consuming process à Consumer complaints department
is currently reading and manually answering 2’000 cases p.a.
•limited knowledge of the complaints structure and the
changes over time
•Adapting to Technological Advancements
Goal of the solution:
Developing an AI engine which allows Zweifel to first classify the
type of complaint and create a ready-to-send response to the
customer. Ideally, the complaint department must only quickly look
over the text and can send it out without the need of further
modification.
Scope to address:
•mandatory: use of language mode to cluster client text and
generate replies
•ideally: reviewing the existing accumulation of complaints and
questioning them based on the most recent complaint data
•ideally: use of image models to use uploaded consumer photos for
complaint segmentation
Data needed:
existingcomplaints: about13’000 datarecordsincludingimages
(from2017 untilnow)
ComplaintSegmentation and (Semi-)Automation
Technological Difficulty: 2 Language Required: German, English

30. •Hirslanden represents high-quality, integrated healthcare for individuals at all stages of life, from birth to old age and
from prevention to cure, both outpatient and inpatient.
•The company differentiates itself through superior medical and nursing quality, service excellence, and a warm
atmosphere, supported by highly qualified specialists, excellent care, and specialized medical centers.
•As Switzerland's largest medical network, Hirslanden plays a crucial role and commits to innovation to meet the evolving
needs of society and to continue providing high-quality healthcare in the future. Through additional services and the use
of technology, the differentiation in service offerings is emphasized.
About Hirslanden Group

31. Klinik Hirslanden Zurich
Klinik Hirslanden has been committed to the well-being
of its patients with passion and dedication since 1932.
We are proud that we have been contributing to the
provision of healthcare in the Canton of Zurich for over
90 years.
Klinik Im Park Zurich
For over 30 years, Klinik Im Park has been a guarantor of medical excellence,
professional care and a warm atmosphere. We see it as our task to provide our
guests with personalised care to ensure their safety and well-being. Medical
services at the highest level and a modern infrastructure are a matter of course
for us.
About Hirslanden Group

32. BUSINESS PROBLEM
Scenario:
The Klinik Hirslanden and the Hirslanden Klinik Im Park operate their
own LinkedIn accounts and have an internal communication app
"Beekeeper," which functions similarly to a social media platform. The
Hirslanden Clinics in Zurich specifically encourage employees and
partner physicians to act as ambassadors for the clinics by publishing
their own posts on these platforms.
Users:
Employees and partner physicians
Pain points:
For authentic content, grammatically correct and adapted in form and
language to the Hirslanden CI/CD, as well as properly prepared for the
two channels, there is often a lack of time or the necessary
experience.
DIRECTION OF SOLUTION
Goal of the solution:
Automatic creation of channel-specific prepared posts, incorporating
the linguistic specifications of publicly available information from both
clinics (websites, LinkedIn, etc.) and including the CI/CD of the
Hirslanden Group.
Scope to address:
The essence of the prepared posts is crucial: authentic, grammatically
correct, according to the guidelines of the Hirslanden Group, including
the use of emoticons and the creation of image suggestions. It should
be quick and easy for all employees to create LinkedIn or Beekeeper
posts without extensive prior experience.
Data needed:
Websites, LinkedIn, Hirslanden Language Guideline
Technological Difficulty: 2 Language Required: German, English
Hirslanden Voicehub

33. SIX Swiss Exchange, BME Exchange,
BME Derivatives Exchange, SIX Digital
Exchange
Listing
Trading
Market Data
Exchanges
Clearing
Settlement and Custody
Securities Finance
Tax Services
Trade Repositories
Cash
Connectivity (Open Banking)
Debit and Mobile Solutions
Billing and Payments
Banking Services
Securities Services
Third-largest stock exchange group
in Europe
Smooth payment transactions
Unbeatablepost-trade services
fromA toZ and moreFourAreas of
Activity.
OneCompany.
Reference, Corporate Actions
and Market Data
Tax and Regulatory Services
Indices
ESG Data & Solutons
Display and Data Feed
Financial Information
Data You Trust
IT
Human Resources
Marketing & Communications
Legal & Regulatory
Risk, Security & Compliance
Finance & Services
Corporate Functions

34. Sensitivity: C1 Public
ESG Data Challenge
Supporting Swiss market participants on their ESG
journey
258Swiss Listed companies
2Pillars: Environmental and Social
2Themes: Carbon Emissions and Social Risks
3Data source types: Annual reports. ESG reports, Company
websites

35. BUSINESSPROBLEM DIRECTION OF SOLUTION
Scenario:
As financial infrastructure provider in Switzerland, we see it as our
responsibility to support the Swiss market participants in their ESG
journey. Today, there is a lack of transparencyin the waycompanies
perform, when it relatesto ESG. Two main areas need urgent actions:
environmental footprint and social risks. We want to provide SIX listed
companies with greatervisibility on how they compare against others,
allowing them to further improve.
Users:
•Companies listed / considering listing on SIX Exchange
•Investors
•SIX Product Development, Marketing, Communication
Pain points:
1.Lack oftransparency
2.Lack ofdata-harmonisation
3.Dependency of data providers
4.Green- & social washingprevention – isthe reported datathe same as
what is communicated?
Goal of the solution:
To increaseESG performance and transparency, with specific focus
on carbon emissions and social risks.
Use Case 1: As a company, the user can understand how well the
company is doing compared to its peers.
Use Case 2: As investor, the user can improve their investment
decisions (e.g. «green» investments based on more reliable and
neutral data).
The solution could be made available on a portal, powered by
ChatGPT, with the ability torun queries on specific themes and topics.
Scope to address:
•Carbon emissions : Scope 1, 2 and 3
•Socialrisks: modern slavery, human rights, child labour
policiesand or controversies
•Listed companies on the SIX Swiss Exchange
Data needed:
Data shouldbefetchedfromreliable publicsourcessuch asAnnual
reports, ESG reportsand companywebsites.
ESG Carbon Emissions& SocialRisk Benchmark
Language Required: English, German Technological Difficulty:3

37. Thank you!