Helena Holmström Olsson Professor of Computer Science Dept. of Computer Science and Media Technology Malmö University, Sweden

1. From Traditional to Digital:
How software, data and AI are transforming the embedded
systems industry
Helena Holmström Olsson
Professor of Computer Science
Dept. of Computer Science and Media Technology
Malmö University, Sweden

2. • Who am I? And what is Software Center?
• Digitalization and Digital Transformation
• From Traditional to Digital:
Business models: Continuous value delivery and recurring revenue
Ways-of-working: Data-driven decision making and Ops practices
Ecosystems: Transformation and co-existence of existing and emerging
• Conclusion
Overview

4. Software Center
Mission: To significantly improve the digitalization capability of the European Software-Intensive industry

7. Stairway to Heaven: Speed
R&D teams R&D teams
V&V
R&D teams
V&V
Release
Cust. Sup.
R&D teams
V&V
Release
Cust. sup.
Prod. mgmt.
Sales & mrkt
Olsson, H.H., Alahyari, H. and Bosch, J. (2012). Climbing the “Stairway to Heaven”. A Mulitiple-Case Study Exploring Barriers in the Transition from Agile
Development towards Continuous Deployment of Software. In Proceedings of the 38th Euromicro Conference on Software Engineering and Advanced
Applications (pp. 392-399). IEEE.

8. • Who am I? And what is So>ware Center?
• Digitaliza?on and Digital Transforma?on
• From Tradi?onal to Digital:
Business models: Con<nuous value delivery and recurring revenue
Ways-of-working: Data-driven decision making and Ops prac<ces
Ecosystems: Transforma<on and co-existence of exis<ng and emerging
• Conclusion
Overview

9. Digitalization
Digitalization is the use of digital
technologies to change a business model
and provide new revenue and value-
producing opportunities; it is the process
of moving to a digital business.
- Gartner

10. Digitalization

11. • …”is an effect that changes the fundamental expectations and
behaviors in a culture, market, industry or process that is
caused by, or expressed through, digital capabilities, channels
or assets.” (Gartner.com)
• …the change that happens when new digital technologies,
services, capabilities, and business models affect and change
the value of the industry's existing services and goods.”
(Simplilearn.com)
Digital disruption
ZipCar, Lyft,
Uber P2P
commerce,
e.g., Airbnb
Online
payment,
digital wallets,
cryptocurrency
3D printing
Apple, Google
etc.
Voice, video,
media (Skype,
Reddit, Discord)

12. Technology Evolution
mechanics electronics so;ware data artificial intelligence
digitalization

13. Business Evolution
Transactional model(s) Subscription-based model(s)
Value-based and continuous model(s)
Business model
evolu9on
New
partner(s)
New
partner(s)
New
partner(s)
Ecosystem
evolution
DevOps
DataOps
MLOps
Ways-of-
working
evolu9on
Olsson, H. H., & Bosch, J. (2020). Going digital: Disruption and transformation in software-intensive embedded systems ecosystems.
Journal of Software: Evolution and Process, e2249.

14. Product sales
(limited service
revenue)
Product-as-a-
service sales
Complementary
services around
products
Customer KPI’s-
based business
model
Multi-sided
ecosystem
model
Focus is on the product as a
mechanical and physical item
Product sold ”as-is”
Focus is on the product
but with a few services in
areas such as e.g. support
Reactive SW updates
Focus is on expanding service
offerings around the product
Proactive and continuous SW
updates
Focus shifts to customer outcomes and data
is monetized with the primary customer
base
Continuous upgrade of SW and periodic
upgrade of electronics and HW
Data for
product
performance
Data for QA
and
diagnostics
Data for
primary
customer
base
Data for
secondary
customer
base
Data from
one customer
is used for
that
customer
Data for human
interpretation
Static ML models trained
on fixed data sets
AI integrated into DevOps Mass customization with ML
models adjusting to products and
users
Autonomous system experimentation
Product updates and changes
because of the potential to
monetize the secondary customer
base
Full digitalized offering
AI/ML/DL
dimension
Business
model
dimension
Data
exploitation
dimension
Product
upgrade
dimension
From Traditional to Digital: The evolution path along four dimensions
Bosch, J., and Olsson, H.H. (2021). "Digital for real: A multicase study on
the digital transformation of companies in the embedded systems
domain.” Journal of Software: Evolution and Process 33, no. 5: e2333.

15. • Who am I? And what is Software Center?
• Digitalization and Digital Transformation
• From Traditional to Digital:
Business models: Continuous value delivery and recurring revenue
Ways-of-working: Data-driven decision making and Ops practices
Ecosystems: Transformation and co-existence of existing and emerging
• Conclusion
Overview

16. Traditional Value Delivery

17. Continuous Value Delivery
continuous software deployment

18. service revenue
product revenue
secondary customer base
total revenue
(2) The business model dimension
Product sales
(limited service
revenue)
Product-as-a-
service sales
Complementary
services around
products
Customer KPI’s-
based business
model
Multi-sided
ecosystem
model
100% product sales 20% product sales
Time
Business model dimension
Bosch, Jan, and Helena H. Olsson. "Digital for real: A multicase study on the digital transformation of companies in the embedded
systems domain.” Journal of Software: Evolution and Process 33, no. 5 (2021): e2333.

19. Product-
oriented value
Outcome-
oriented value
Comparative
value
Two - (multi)
sided markets
Product performance,
product health,
preventive maintenence
Customer insights
(productivity, efficiency,
quality), life cycle
management, device and
asset management
Comparison to
compeAtors,
benchmarking, trend
analysis, acAons and
recommendaAons
Data from one customer
is used for another (new)
customer/ (and vice
versa)
Commodity
Differentiating
Innovation
Few opportunities
for monetization
and no generation
of new (recurring)
revenue streams
Customer 1 Customer 2
data
data
Premium priced
product with
product-oriented
value “as a package”
Value is paid for separately
(on top of existing sales)
Value is paid for separately
(beyond existing sales)
Data is monetized,
e.g., data-as-a-service
Opportunities for
monetization and
generation of new
(recurring) revenue
streams
New
(recurring)
revenue
streams
Value
provided to
stakeholders:
Value Offerings Framework
Olsson, H.H., and Bosch, J. (2022). Living in a Pink Cloud or Fighting a Whack-a-Mole? On the Creation of Recurring Revenue
Streams in the Embedded Systems Domain. SEAA, August 31 – September 2, Maspalomas, Gran Canaria, Spain, 2022.

20. • Who am I? And what is Software Center?
• Digitalization and Digital Transformation
• From Traditional to Digital:
Business models: Continuous value delivery and recurring revenue
Ways-of-working: Data-driven decision making and Ops practices
Ecosystems: Transformation and co-existence of existing and emerging
• Conclusion
Overview

21. “Featuritis”

22. Experimentation Data is what keeps us alive. Everything
we do ends up with, or start with, an
experiment. We have passed the point
where we make decisions on what to
ship or not. Everything is instrumented
and we base what to ship on the results
we get from experimentation”.
(Product Manager, Microsoft)
“We know how much you click, how fast
you click, how long you stay, when you
come back… This translates into metrics
that span across page level metrics, script
errors, performance, frequency of pop-up’s,
queries per user, click through rate, sessions
per user, re-visits of users...”.
(Product Manager, Microso4)

23. • Due to increasing connectivity and data collection from
products in the field, data-driven practices are being adopted
also in software-intensive embedded systems companies
• Experiments are run on selected instances of the system or as
comparisons of previously computed data to ensure value
delivery to customers, improve quality and explore new value
propositions
What we see happening…
Performance
Stability
Efficiency
Feedback
time
Flexibility
Security

24. Olsson, H.H, and Bosch, J. (2014). The HYPEX model: From opinions to data-
driven software development. In Continuous software engineering, pp. 155-
164. Springer, Cham, 2014.
Strategic product goal
Feature: expected behavior (Bexp)
select
implement MVF
actual behavior (Bact)
generate
Bexp
Experimentation
relevant gap (Bact ≠ Bexp)
no gap (Bact = Bexp)
Business strategy and goals
Feature
backlog
Gap
analysis
Develop
hypotheses implement alternative MVF
Product
extend MVF
abandon
DevOps: The HYPEX Model

25. The ‘Experimentation Evolution Model’
Fabijan, A., Dmitriev, P., Olsson, H. H., and Bosch J. (2017). The Evolution of Continuous Experimentation in Software Product
Development: From Data to a Data-driven Organization at Scale. In Proceedings of the 39th International Conference on
Software Engineering (ICSE), May 20 – 28th, Buenos Aires, Argentina.

26. • Process support for
continuous experimentation in
mission-critical systems
• Includes the different
experimentation objectives
and provides a classification of
the different experimentation
techniques and when these
can be used
Development
Pre-study
CI
Internal
laboratory
evaluation
Simulation
Customer
laboratory
evaluation
Passive
launch
Restricted
launch
One-customer
gradual rollout
Customer
laboratory
evaluation
Internal Feedback channel
Gradual
rollout
Customer
request
Market
R&D goals
R&D organization
Internal validation
Single customer
validation
Multiple customer
validation
Ideas
General
availability
Customer Feedback channel
A/B testing,
Crossover exp.
A/B testing,
Quasi-
experiments,
Crossover exp.
A/B testing,
Crossover
experiments
New features
Software
corrections
New
configurations
CE in mission-critical B2B systems
David Issa MaVos, Anas Dakkak, Jan Bosch, and Helena Holmström Olsson. 2020. ExperimentaYon for Business-to-
Business Mission-CriYcal Systems: A Case Study. In InternaHonal Conference on SoIware and Systems Process (ICSSP ’20),
October 10–11, 2020, Seoul, Republic of Korea.

27. BML Loop and MVP Development

28. Design Thinking – Lean – Agile (Gartner)

29. Continuous practices: DevOps
DevOps aims at bringing development and operations together in order to develop and deploy
resilient and high-quality software in short release cycles
Technical transformations include, e.g., automated deployments using build and continuous
integration tools, treating infrastructure as code, and continuous monitoring of infrastructure
and system behavior in production
On the organizational side, it is crucial to build and strengthen a collaborative culture to
successfully establish a straightforward communication and shared responsibilities within and
across teams

30. Continuous practices: DataOps
An approach that accelerates the delivery of high-quality results by automa@on and orchestra@on
of data life cycle stages
Adopts the best practices, processes, tools and technologies from Agile software engineering and
DevOps for governing analytics development, optimizing code verification, building and
delivering new analytics
Aims to help promoting the culture of collaboration and continuous improvement of data

31. Continuous practices: MLOps
MLOps involves data and model validations, in the context of CI
Processed datasets and trained models are automatically and continuously supplied
by data scientists to ML systems engineers
The introduction of new data and degradation of model performance require a
trigger to retrain model or improve model performance through online methods

32. Evolution of AI/ML/DL technology use
ExperimentaTon
& prototyping
Non-critical
deployment of
ML/DL
components
Critical
deployment of
ML/DL
components
Cascading
deployment of
ML/DL
components
Autonomous
ML/DL
components
Each of the steps requires increased activities of ‘AI engineering’.
Lwakatare, L., Raj, A.M., Bosch, J., Olsson, H.H., and Crnkovic, I. (2019). A Taxonomy
of Software Engineering Challenges for Machine Learning Systems: An empirical
investigation. In Proceedings of the International Conference of Agile Software
Development (XP), pp. 227 – 243, Springer, Cham.

33. • An end-to-end approach that
covers all steps from data
collection to retraining
deployed ML models
• The framework supports
practitioners in advancing
their prototypical analysis to a
deployed and continuously
retrained ML model
Framework for productive use of ML
Figalist, I., Elsner, C., Bosch, J. and Olsson, H.H., 2020, November. An End-to-End Framework for Productive Use of Machine
Learning in Software Analytics and Business Intelligence Solutions. In International Conference on Product-Focused Software
Process Improvement (pp. 217-233). Springer, Cham.

34. • Continuous delivery of ML systems:
• Development – Training – Deployment –
Integration - Evolution
• Includes phases
• Involves roles
• Requires collaboration and iteration
• Takes place in the context of a larger
system
AI driven Business Development
John, M.M., Olsson, H.H. and Bosch, J., 2022. Towards an AI-driven business development framework: A multi-case
study. Journal of Software: Evolution and Process, p.e2432.

35. AI engineering: Research Agenda
Generic AI engineering challenges Domain specific AI engineering challenges
Data science
Bosch, J., Olsson, H.H., and Crnkovic, I. (2021). Engineering AI systems: A research agenda, In Artificial Intelligence
Paradigms for Smart Cyber-Physical Systems. IGI Global
Strategic focus areas that are
related to the four main
phases of an ML/DL project.

36. • Who am I? And what is Software Center?
• Digitalization and Digital Transformation
• From Traditional to Digital:
Business models: Continuous value delivery and recurring revenue
Ways-of-working: Data-driven decision making and Ops practices
Ecosystems: Transformation and co-existence of existing and emerging
• Conclusion
Overview

37. Customer Need Evolution Model
Olsson, H.H. and Bosch, J., 2020. Going digital: Disruption and transformation in software-intensive
embedded systems ecosystems. Journal of Software: Evolution and Process, 32(6), p.e2249.

38. Ecosystem transformation
Olsson, H.H. and Bosch, J., 2020. Going digital: DisrupTon and transformaTon in soaware-intensive
embedded systems ecosystems. Journal of So4ware: Evolu>on and Process, 32(6), p.e2249.

39. Strategies used by incumbents
Olsson, H.H. and Bosch, J., 2020. Going digital: Disruption and transformation in software-intensive
embedded systems ecosystems. Journal of Software: Evolution and Process, 32(6), p.e2249.

40. Strategies used by new entrants
Olsson, H.H. and Bosch, J., 2020. Going digital: Disruption and transformation in software-intensive
embedded systems ecosystems. Journal of Software: Evolution and Process, 32(6), p.e2249.

41. • Digitalization is transforming (and disrupting) the embedded systems
industry to an extent that we have only seen the beginnings of
• To thrive in a digital age – companies need to continuously rethink,
reposition and reinvent their:
Business models: Continuous value delivery and recurring revenue
Ways-of-working: Ops practices and data-driven decision making
Ecosystems: Transformation, co-existence and strategic engagement
• Transforming from a traditional towards a digital company requires
effective use of software, data and AI for continuous delivery and
improvement of customer value
Conclusions

43. Thank you!
Thank you!
helena.holmstrom.olsson@mau.se