6. AI, IoT, data science & business requirements

Question 1

What is AI?
Ransbotham et al. 2017

Answer 1

AI is the theory and development of
computer systems able to perform tasks
normally requiring human intelligence,
such as visual perception, speech,
recognition, decision-making, and
translation between languages

Question 2

What expectations does organizations have to AI?

Answer 2

Most organizations foresee effects on

• information technology,
• operations and manufacturing,
• supply chain management
• customer-facing activities

Question 3

What are maturity clusters of AI?
Ransbotham et al. 2017

Answer 3

• Pioneers: Organizations that both understand
  and have adopted AI.
• Investigators: Organizations that understand AI
  but are not deploying it beyond the pilot stage.
• Experimenters: Organizations that are piloting
  or adopting AI without deep understanding:
  “learning by doing”
• Passives: Organizations with no adoption or
  much understanding of AI.

–> Most telling difference: their understanding of
the critical interdependence between data and
AI algorithms

Question 4

What are challenges for adopting AI?
Ransbotham et al. 2017

Answer 4

• Misunderstanding about data and AI
• Make vs. Buy Decisions
• Privacy and Regulations

Management Challenges
1.) Develop an intuitive understanding of AI
2.) Organize for AI –> Ross et al.
3.) Re-think the Competitive Landscape –> G&R

Question 5

What are five key results of AI?
Ransbotham et al. 2017

Answer 5

1. A majority of individual workers
   personally obtain value from AI.
2. A majority of individuals regard AI as a
   coworker, not a job threat.
3. Requiring individuals to use AI
   encourages its use more than building
   trust in AI does.
4. Mandatory use, despite seeming
   oppressive, still leads to individual value.
5. Organizations get value when
   individuals get value, not at the expense
   of individual value.

Question 6

What can you do next after adopting AI?
Ransbotham et al. 2017

Answer 6

1. Ensure customer trust
2. Perform AI health check
3. Brace for uncertainty
4. Adopt scenario-based planning
5. Add a workforce focus

Question 7

THE EVOLUTION OF DECISION SUPPORT
Watson (2017)

Answer 7

• Cognitive decision support system:
  business intelligence analytics where AI
  will be a highly prominent feature
• Approx. every 10 years: significant
  development
• Generational perspective: Provides a
  maturity model perspective of decision
  support systems –> historical contexts
  helps to communicate change

Question 8

What are 10 maturity factors of decision support?
Watson (2017)

Answer 8

1. Strategic importance
2. Scope
3. Focus
4. Decisions supported
5. Users
6. Data management
7. BI/analytics
8. Architectural complecity
9. Governance
10. Value

• Each generation builds on the previous generation
• Need for systematic, organized progression in each characteristics
• Business issues stayed mainly the same
• Hot topics have been envisioned years ago; technology needed to catch up
• Available technology is changing at an accelerating rate

Question 9

WHAT IS COGNITIVE GENERATION DECISION SUPPORT?
Watson (2017)

Answer 9

• Headline characteristic: The use of AI
• Other important developments:
  o continuing movement of BI/analytics to the cloud
  o Greater use of IoT, sensors, streaming data
  o BI/analytics becoming more pervasive
  o Greater monetarization of data

Examples:
* Natural Language Interfaces
* Chatbots
* Visualization Systems/ Facial Action Coding systems
* IBM’s AI-based Cognitive Computing Initiative

Question 10

IBM’S AI-BASED COGNITIVE COMPUTING INITIATIVE
Watson (2017)

Answer 10

• Watson and other tools are
  available as a platform-as-aservice
  in the cloud to build,
  deploy, run, and manage
  applications.
• Technology advances are
  making it possible to create,
  collect, store, and analyze
  “dark data” (e.g., images, IoT
  data streams)

Question 11

What could 10 recommendations be for using AI?

Answer 11

1. Start Small with a specific business problem or opportunity
2. Prepare use cases
3. Create the cognitive generation architecture
4. Recognize the changing governance needs
5. Help management understand BI/Analytics Models and Output
6. Be creative in Meeting Data Science Staffing Needs
7. Recognize that Advanced BI/Analytics is a Team Effort
8. Further Develop a Fact-Based Decision-making culture
9. Hire the smartest people you can
10. Be concerned about privacy

Question 12

ETHICS AND OPPORTUNITIES
Kho 2019

Answer 12

How to protect the privacy and security of end-users
who don’t realize what they are giving away while
interacting with IoT or other digital offerings?
Complex ethical questions (examples):
* Individualized content: journalism vs. algorithms –>
danger of misinformation
* Intrusiveness of the “surveillance economy”
* Combination of data-sets – who owns data if
company is sold?

Question 13

TRANSPARENCY VS. TRUST

Answer 13

• Problem of transparency (e.g. GDPR):
  Unrealistic that consumer understand what
  data is being collected and what is
  happening to it
• Trust becomes an important part of treating
  data respectfully, securely, and solely for the
  purpose of serving the customer better
• Inclusive approach to ethics training,
  security protocols, and data handling should
  be on every organization’s radar –> training
  for employees

Question 14

What is surveillance economy?

Answer 14

• Surveillance economy = “the unilateral
  claiming of private human experience as
  free raw material for translation into
  behavioral data. These data are then
  computed and packaged as prediction
  products and sold into behavioral futures
  markets — business customers with a
  commercial interest in knowing what we
  will do now, soon, and later.”