AI ja ohjelmistorobotiikka elämyksien ja tuottavuuden mahdollistajana – Case Elisa Kimmo Pentikäinen, Vice President, Business Development, Elisa

1. AI & RPA
bringing experiences
and productivity into
everyday life
Kimmo Pentikäinen
May 08, 2017

2. trial users Vast majority
innovators Early
adopters
early majority late majority laggards
Source:Downes,Nunes,“Big-BangDisruption,”HarvardBusinessReview,March2013
Big-Bang disruptions change the
business logic
Big-Bang

3. Big-Bang
disruption case:
smart devices
in 2007

4. 0
2
4
6
8
10
12
As % of total US trail sales
Source: Cowen and Company, The Economist
Forecast
Big-Bang disruption case: retail in US 2016

5. 0
2
4
6
8
10
12
14
16
Volkswagen Samsung Intel Microsoft Apple Tesla
R&D investments 2015
Investment different than ability to innovate

6. Source: Gartner 2016
S&P 500
$235 billion R&D
$914 billion stock buybacks
Don’t give up the confidence in the future

7. Source: World Economic Forum The Global Competitiveness Index 2016–2017
5.5
5.55
5.6
5.65
5.7
5.75
5.8
Switzerland Israel Finland United States Germany
Innovation – Finland is the 3rd in the world

8. Source: World Economic Forum - The Global Information Technology Report 2016
University-industry collaboration in R&D
1.
Switzerland
2.
Finland 3. Israel
4.United
States
5.
Netherlands

9. Source: World Economic Forum - The Global Information Technology Report 2016
5
5.2
5.4
5.6
5.8
6
6.2
6.4
6.6
6.8
Finland United States Norway Sweden United Kingdom
Availability of latest technologies
– Finland is the 1st in the world
gdom

10. PERFORMANCE
PRODUCTS
SCIENCE
MOORE’S
LAW
BENEFITS AND SERVICES
PLATFORMS
CONNECTIONS
METCALFE’S
LAW
10
Digitalisation
Digital technology creates opportunities, networks enable them

11. Intelligent automation will
be the next disruption

12. Intelligent automation will
be the next disruption

13. *Forty-six countries used in this calculation, representing about 80% of global labor force. Source: McKinsey Global
Institute analysis, published in a report “A Future that works: Automation, employment, and productivity”, January 2017.
of the current work activities in global
economy can be automated, but
adoption will take decades and there
is significant uncertainty on timing*49%

14. Case #1 Elisa Viihde voice user interface
•! Google voice to
text API
•! Available in
Android TV

15. Case #2 Habbo hotel chat moderation
•! 20 million chat
lines daily
•! Reduced over 50%
of manual work
and approximately
50% of OPEX

16. Case #3 marketing automation
•! Upsell recommendation for
sales representatives
•! On average, 60-70k
automated e-mails a week,
click-through rate over 3
times higher compared to
manual campaigns

17. Case #4 incorrect reference numbers in payments
•! Several thousands
incorrect
references
•! RPA makes
corrections
•! Payback in three
months

18. Case #5 Viasat telemarketing
•! Automatizing the
manual interaction
for pay TV orders
•! Payback in three
months

19. Case #6 IoT machine learning
•! 20.000 machine incident
maintenance
•! identified 20% reduction
based on quality of
maintenance actions

20. Case #7 Predictive incident management
Three machine learning
projects for proactive
reducing of incidents
based on 9,5 million
measurements in
broadband connections
(ADSL2, Ethernet, VDSL2)
2014
2015
2016
LE17
Major incidents Total incidents

21. Kimmo Pentikäinen
Vice President, Business Development
+358 50 506 5412 kimmo.pentikainen@elisa.fi
twitter @pentikainen