Devo Lab foundation, AI in Business

1. Digital Enterprise Value and Organization
Economics, Organization and Technology
for the Digital Enterprise
AI in Business – HIT Radar
Severino Meregalli
Scientific Coordinator DEVO Lab
severino.meregalli@sdabocconi.it

2. DEVO LAB FOUNDATION
The DEVO Lab merges the ideas
and insights from the Think Tank
with the contributions of a broad and
multidisciplinary community of
professionals.
The aim of DEVO Lab is to bring
together diversified perspectives,
experiences and backgrounds to
consistently assess the business
implications of technologies and the
value generation they can enable.
Plateau of
Inflated
Expectations
Time
Expectation
Technology
Trigger
Trough of
Disillusionment
Slope of
Enlightenment
Plateau of
Productivity
Value

3. DEVO LAB MEMBERS
DEVO research program and
activities are assessed and
suggested by the DEVO LAB
Think Tank.
All Members have a long-term
commitment to the DEVO Lab
and are entitled to have a
representative in the DEVO
LAB’s Think Tank.
Other participants could include
representatives of sponsors of
large research projects and
domain experts with
international standing.

4. Artificial Intelligence

5. ARTIFICIAL INTELLIGENCE HYPE
Sources: McKinsey Global Institute on US BureaBureau of Labor Statistics; Gartner; CB
Insight State of AI 2018 Report; Forbes, 2017.

6. HIT RADAR
ASSESSMENT DIMENSIONS
Impact
(icon shape)
Dynamism
(icon color)
Distance
(ring)
Adoption
Technology
Maturity
Economic Impact (Revenues, Costs, Value)
Workforce
Competitive Advantage
Infrastructure Coherence
Business Model Coherence
Regulation
Skills & Knowledge
Legal Fit
Adoption Growth Rate
Upstream Value Chain Support
Changing Regulations
Accessibility to Specialized Education
Infrastructure Trends
Business Model Knowledge
1 2 3 4 5

7. HIT RADAR 2017
TECHNOLOGICAL FOCUS VS. MANAGERIAL FOCUS
CLOUD
BIG
DATA
DIGITAL
BIOMETRICS
MACHINE
LEARNINGINTELLIGENT
VISION
VR
AR
5G
S-R IoT
L-R IoT
ENTERPRISE
3D PRINTING
COLLABORATIVE
DRONES
IMCCLOUD
BIG
DATA
DIGITAL
BIOMETRICS
BLOCK
CHAIN
FOG/
EDGE
QUANTUM
MACHINE
LEARNING
NLP
INTELLIGENT
VISION
AR
5G
S-R IoT
L-R IoT
ENTERPRISE
3D PRINTING
COLLABORATIVE
DRONES
VR
Artificial
Intelligence
Human
Augmentation
Digital
Infrastructure
Internet of
Things
Materials
Printing
Advanced
Robotics
Artificial
Intelligence
Human
Augmentation
Digital
Infrastructure
Internet of
Things
Materials
Printing
Advanced
Robotics
NLP
QUANTUM
BLOCKCHAIN
IMC
FOG &
EDGE

8. HIT RADAR 2017
ARTIFICIAL INTELLIGENCE
MACHINE
LEARNING
NLP
INTELLIGENT
VISION
HIGHLIGHTS
▪ Machine Learning has moved closer to the center due to the improvements assessed on
technology maturity and infrastructure coherence, but it still remains in the “Watch” ring.
The technology switches from “Take-Off” to “Accelerating” in Dynamism thanks to a
significant increase in the adoption growth rate and advancements in changing regulation;
▪ Natural Language Processing advances slightly within the “Consider” ring and keeps a
very high overall Dynamism assessment score, as we expect it to reach the next step of
adoption within the next half year. Impact has grown on the workforce side, but remains
mid-low overall;
▪ Intelligent Vision has been introduced as a new AI technology positioned in the “Watch”
ring, given its early availability maturity, neutral business model coherence and lack of
specific regulatory addressing. Its overall impact is assessed as mid-low and Dynamism is
assessed as “Take Off”.

9. HIT RADAR 2017
ARTIFICIAL INTELLIGENCE – MACHINE LEARNING
MACHINE
LEARNING
Technology maturity
Infrastructure coherence
Business model coherence
Regulation
Skills and knowledge
Legal fit
Distance
Adoption
Workforce
Economic impact
Competitiveness
Impact
Adoption growth rate
Upstream value chain support
Changing regulation
Accessible knowledge
Infrastructure trends
Business model knowledge
Dynamism
Machine learning is advancing significantly and can enable enhanced business results at many levels. Specialists should
be dedicated to the study of the matter, at least from a basic tech understanding (model training and development
procedures, key capabilities) to evaluate where and how AI can have place in the company, as solid applications are set to
emerge in the short- to mid-term.
Advice
WATCH
51.7%
MID-HIGH
52%
ACCELERATING
74.1%

10. HIT RADAR 2017
ARTIFICIAL INTELLIGENCE – NATURAL LANGUAGE PROCESSING
Technology maturity
Infrastructure coherence
Business model coherence
Regulation
Skills and knowledge
Legal fit
Distance
Adoption
Workforce
Economic impact
Competitiveness
Impact
Adoption growth rate
Upstream value chain support
Changing regulation
Accessible knowledge
Infrastructure trends
Business model knowledge
Dynamism
NLP has advanced significantly in the last year and many valuable applications are available to set up conversational
interfaces, both for external (consumer engagement and customer management) and internal (employee support and
process automation) applications. The evaluation of specific PoCs and pilots is advised, in order to actually assess the
business potential of the technology within your company.
Advice
CONSIDER
65.5%
MID-LOW
39%
ACCELERATING
70.4%
NLP

11. HIT RADAR 2017
ARTIFICIAL INTELLIGENCE – INTELLIGENT VISION
Technology maturity
Infrastructure coherence
Business model coherence
Regulation
Skills and knowledge
Legal fit
Distance
Adoption
Workforce
Economic impact
Competitiveness
Impact
Adoption growth rate
Upstream value chain support
Changing regulation
Accessible knowledge
Infrastructure trends
Business model knowledge
Dynamism
Accuracy of intelligent vision is not perfect but constantly improving and many applications are available, from more
consolidated (automated image indexing for process automation) to yet nascent and developing (e.g. proximity marketing,
dynamic emotion recognition and intelligent image-based advertising). Yet, regulation over consumer-related data acquired
through images is still unclear. An assessment of the business potential of intelligent vision applications within your
company is advised, in order to get ready for fast adoption of those solutions that will consolidate in the coming few years.
Advice
WATCH
51.7%
MID-LOW
36%
ACCELERATING
63%
INTELLIGENT
VISION

12. AI in Business

13. THE AI APPLICATION LANDSCAPE
DEVO LAB ©
Business
Intelligence
Quality
Control
Predictive
Maintenance
Learning &
Development
Data
Integration,
Preparation &
Cleaning
Conversational
Interfaces
Knowledge
Management
Geospatial
Analytics
Sales
Support
Assessment
& Selection
Recruiting
Payment
Solutions
Fraud
Detection
Drone
Solutions
Fleet
Maintenance
Credit
Scoring
Investment
Automation
Industrial
Optimization
Fleet
Monitoring
Supply Chain
Optimization
Warehouse
Management
Visual
Investigation
Predictive
Cybersecurity
Virtual
Assistants
Call Center
Solutions
Auditing &
Reconciliation
Advertising
Sentiment
Analysis
Translation,
Summarization
& Reporting
Legal
Analytics
Cognitive
Business
Intelligence
Predictive
Marketing
Analytics
Document &
Contract
Management
Process
Automation
Information
Discovery
Social
Analytics
Engagement &
Personalization
Information
Capturing
Logistics
Analytics
Anomaly
Detection
Authentication
Financial Data
Exploration
Financial Services & Insurance
BI & Analytics
Marketing & Sales
Industrial
Logistics & Supply Chain
Security
HR Management
Back-Office, Legal & Support

14. COMPLICATED VS COMPLEX PROBLEMS
Complicated problems can be hard to solve, but they are addressable with rules and
algorithms and with computational capacity. They also can be resolved with systems
and processes. They are characterized a huge variety and variability of the variables
involved. An optimal solution of complicated problem is always possible, even if difficult
to get.
Complex problems involve too many unknowns and too many interrelated factors to
reduce to rules and processes. There’s no algorithm that will tell you how to respond.
They are characterized by a high level of interdependence of the multiple variables
involved. There is always uncertainty about the possible outcome and there is not an
optimal solution.

15. COMPLICATION VS COMPLEXITY
Complication
• Reliable data
• Knowledge and models are available
• Measurable outcomes
Complexity
• Only partial explanation
• Scarce Data Sources
• Lack of metrics or fuzzy results

16. NATURAL SCIENCES VS SOCIAL SCIENCIES
Natural science is a branch of science concerned with the description, prediction, and
understanding of natural phenomena
Complication
Social sciences are disciplines concerned with the study of the social life of human
groups and individuals (e.g. anthropology, economics, history, political science,
psychology, sociology)
Complexity

17. A FRAMEWORK FOR AI IN BUSINESS
Completely
Automated
Systems
Performance
Not
Automated
Complexity
Human / Machine
Interaction
Human
Augmentation
Machine with
Human support
HumansMachines
LowHigh
Complication
© DEVO Lab SDA Bocconi

18. A PROMISING SCENARIO (IN SELECTED DOMAINS)
Machine
Learning
Domain
knowledge

19. A FRAMEWORK FOR AI IN BUSINESS
Completely
Automated
Systems
Performance
Not
Automated
Complexity
Human / Machine
Interaction
Human
Augmentation
Machine with
Human support
HumansMachines
LowHigh
Combined Effect
Complication
© DEVO Lab SDA Bocconi
ML synergies in KD

20. WRAP UP
AI is here and can be used to provide «human» augmentation and / or substitution
One key aspect is to better understand were it can be used providing real value to
companies avoiding the hype effect
Delaying AI adoption in Natural Sciences / Complicated Problems that can be solved by
these technologies is (in most cases) a big mistake
Investing in IA projects with no understanding of how difficult it is to achieve a
competitive performance in Social Sciences / Complex Problems is a huge waste of
human and financial resource
In a context where Hype and Real Opportunities are quite difficult to tell
management judgement, research centers and world class suppliers are a key
resource

21. “Think like a man of action, act
like a man of thought.“
Henri Bergson