With his team, Wouter has been building chat- and voicebots for almost 3 years. In this session, you will learn about the pitfalls of a conversational AI implementation. Wouter will tell you about common mistakes and share best practices about how you can create intelligent and personal virtual agents based on a number of previous projects.

1. HOW TO CREATE INTELLIGENT &
PERSONAL CONVERSATIONAL AI
Openba[a]r Kontich
12-12-2019

2. What do you do?
We are an Applied Artificial
Intelligence Solution Provider. We
build solutions for a future where
machine learning and data
science will be the driving
factors of your business in a
constantly changing reality.
Openba[a]rKontich

3. Who are you?
We are a unique mix of Data
Scientists, AI Engineers,
Statisticians and Developers. Our
projects are on the crossroads of
those domains and will typically
require a multi-disciplinary
approach.
Openba[a]rKontich

4. From simple informational
chat - or voicebots to
more advanced bot
platforms requiring
‘Natural Language
Processing’ and complex
integrations.
From data exploration to predictive
algorithms, real -time analytics and deep
learning. We can use pre -built models and
existing tools or develop our own code in R
and Python
What do you do?
Openba[a]rKontich
Development of simple games
and informational applications
on humanoïd robots. Advanced
research projects examining
the use of humanoïd robots in
education, training, medical
sector, city services, security, ...
Basic trainings in Bot
Development, Machine
Learning and Advanced
Analytics, so you can start
building your own
competences in the field of
Data Science & AI.

5. Customers
Openba[a]rKontich

6. What is Artificial Intelligence?

7. Artificial Intelligence (AI)
“The theory and development of computer systems able to perform
tasks normally requiring human intelligence…”

8. Many forms of AI
• Image recognition
• Face detection
• Natural Language Processing
• Chatbots
• Object detection
• Predictive Analytics
• Robotics
• Speech recognition
• Translation services
• AR/VR
• ...
Openba[a]rKontich

9. Chatbots = AI?
• NLP
• Recognize the intention of the user
• Analyze the context and retrieve
relevant details
• Is the city hall open
• Advanced search
• Cognitive search in your knowledge
base
• Learning capabilities
• Train and re-train your chatbot
Openba[a]rKontich

10. HOW TO CREATE INTELLIGENT &
PERSONAL CONVERSATIONAL AI
Openba[a]r Kontich
12-12-2019

11. Content
What is the information we want to offer our users?

12. Expectation management
Common pitfalls
Content
Openba[a]rKontich

13. Not enough content or training
Common pitfalls
Content
Openba[a]rKontich

14. Too much content
Common pitfalls
Content
Openba[a]rKontich

15. User Experience
How do I make using my bot as easy as possible

16. Clickbot
Common pitfalls
User experience
Openba[a]rKontich

17. No re-use of available tools, services and API’s
Common pitfalls
User experience
Openba[a]rKontich

18. No re-use of available tools, services and API’s
Common pitfalls
User experience
Openba[a]rKontich

19. No hints for further engagement
Common pitfalls
User Experience
Openba[a]rKontich

20. Personality
Digital vs. human

21. Don’t fake human
Common pitfalls
Personality
Openba[a]rKontich

22. Don’t fake human
...BUT: you need a good (human-
centric) conversation design!
Don’t have your bot designed by
engineers only!
Common pitfalls
Personality
Openba[a]rKontich

23. Your bot will fail
Common pitfalls
Personality
Openba[a]rKontich

24. Your bot will be tested
Common pitfalls
Personality
Openba[a]rKontich

25. Feedback
Continuous improvement

26. No quality control or re-training
Common pitfalls
Feedback
Openba[a]rKontich

27. No analytics
Common pitfalls
Feedback
Openba[a]rKontich

28. No user feedback or impact quantification
Common pitfalls
Feedback
Openba[a]rKontich

29. ROI
What’s the added value for my organization?

30. No clear business case
Common pitfalls
ROI
Openba[a]rKontich
 Process throughput time reduced from +1 hour with 2 employees to 15 minutes with the
help of the chatbot
 Shift mundane tasks to digital workforce
 Revenue generation
 FTE reduction
 Higher accuracy
 Make life easier for my employees

31. Reference cases

32. Stad Roeselare
• 62.700 inhabitants
• Complex structure: different
departments, teams and
partnerorganizations
• Website mainly aimed at inhabitants and
visitors
AppliedAI–usecase'Bertje'

33. Stad Roeselare
• Large variety of information on website
• Underused search functionality
• Contact center receives lots of repetitive
questions while they should focus more
on complex issues
• Analytics show top FAQ’s and long tail of
other more specific questions
AppliedAI–usecase'Bertje'

34. Stad Roeselare
• January 2018: start development framework
(webscraping, creating first MLP)
• Summer 2018: First test phase, low testing
rates resulting in few additional content
• October 2018: Local elections – freeze for all
projects in the months leading up to and
after the elections
• Winter 2018: training, training, training!
• Summer 2019: alpha and beta testing with
citizens (first ‘real’ testers), re-training of bot
• October 2019: go-live
AppliedAI–usecase'Bertje'

35. Stad Roeselare
• Able to answer ±650 questions and continuously
growing thanks to re-training and evaluation of chatlogs
• Most important content gathered with actual users
• Satisfied users for general questions, exceptions remain
tough to handle
• 340 chat sessions in the first 18 days after go-live,
without any promo!
• 782 unique questions during that same period, 2.5
questions / conversation on average
• Project won the 2019 AI Cup (selected by Hangar K,
Start It @ KBC, HOWEST and VOKA West-Vlaanderen)
AppliedAI–usecase'Bertje'

36. “Bertje was already 80% ready in May 2018, but we wanted to be
sure that the functionalities and ease of use were fully on point
before going live. That is why we have carried out various internal
tests and have thus further fine-tuned the UX design and flows.
The different people in the team all also found other points for
improvement, which was ideal. It is especially important for us that
we find the errors and mistakes and not our external customers”
-Kimberley Van Luchem, Project Manager

37. Partena Professional
• HR-services provider
• 32 offices, 1800 employees
• Customers: 81.000 businesses
and 140.000 freelancers
• €195 Mio. Revenue
• Entrepreneurship with
entrepreneurs
AppliedAI–usecase‘Louise’

38. • Expert HR advice, with a strong
focus on human-to-human
interactions
• Complex, repetitive and system
heavy end-of-contract procedures:
• Legal advice (outplacement, early
retirement, industry specific rules,
etc.)
• Calculation of dismissal period and
resignation fee (different
applications)
• Ticket creation in Topdesk
• Adjust resignation letter template
Partena Professional
AppliedAI–usecase‘Louise’

39. Partena Professional
• Fall 2018: start development
Resignation API and prototype
chatbot Louise
• Integration services with:
• Calculation engines
• Document generation engine
• Ticketing system
• February 2019: testing and roll-out
to 900 payroll consultants
AppliedAI–usecase‘Louise’

42. Partena Professional
• September 2019: standard
operating procedure
• Process throughput time reduced
from +1 hour to 15 – 20 minutes
• Revenue generation: €200 / case
• HR Excellence Award?
• 2020: a public brother or sister for
Louise
AppliedAI–usecase‘Louise’

43. “By investing in a scoped project that
was used throughout the company,
the adoption rate of AI technology
grew enormously.”
-Jonas Pollet, Innovation Manager

44. Q&A

45. +32 472 44 01 43
wouter.baetens@arinti.ai
www.arinti.ai
Stationsstraat 55 – 2800 Mechelen
Gaston Crommelaan 4 – 9050 Gent
Wouter Baetens
Managing Partner