AI in Strategic Communication
LECTURE 1
Strategic Communication
= encompasses all communication that is substantial for the survival and sustained success of an entity
(Zerfass, 2018)
Entity
= humans and non-humans (companies, NGOs, etc)
3 criteria
1. sphere of responsibility / influence
2. contested purpose (mission → advancing AI)
3. limited resources (prioritization, protecting assets)
(All) Communication
= interactive process of meaning construction
3 lenses (Van Ruler, 2018)
1. One-way (linear) process
- no feedback, predictable pattern
2. Two-way process
- interactive, meanings not fixed, receivers not passive (Altman responding)
, 3. Omnidirectional Diachronic process
= interplay between social actors, related to each other only in the context of developing their
meaning continuously over time, constructing society itself and constructing ideas about how
organizations in society should behave (Van Ruler, 2018)
Omnidirectional
- interaction with the process of meaning construction
- not linear → goes in multiple directions
- actors not related or in proximity
Diachronic
- not static → development happens over time
- continuously moving forward, always dependent on the past
- informing present and future
- a continuous cycle
Implications of the omnidirectional diachronic model
→ communication is about LISTENING, not only Messaging
→ communication is NOT LIMITED TO FORMAL MESSAGES, but also ACTIONS that convey
meaning
→ communication happens in INTERNAL and EXTERNAL arenas
Substantial
= objective + subjective dimension
- subjective = CEOs identify issue as strategic
- objective = the real (retrospective) impact
,7 factors that make an issue strategic
1. Resource-driven → demands allocation of high-value assets or valuable resources
- pouring money into AI = strategic
2. Competition-driven → strengthens a competitive edge/bypasses direct competition
- AI poses a competitive advantage, keeps companies competitive
3. Environment-driven → comes with political, technological or ecological changes
- AI is a political issue; companies want to have their own software to remain
independent from larger companies
4. Risk-driven → escalates into a high-risk scenario and forces a critical decision/triggers crisis
- AI’s decision-making can be biased and cause unequal treatment
5. Innovation-driven → introduces groundbreaking changes that disrupt existing structures or
methods
- becomes substantial for the survival of the organization
6. Engagement-driven → leverages free resources to signal strategic priorities and influence
stakeholders
- investing in AI signals priorities and further direction of the company
7. Operationally-driven → overhauls organization and processes of the organization
- organizations change as a consequence of AI implementation
strategic significance = can only be understood in retrospect
- easily missed, inaccurately prioritized or deprioritized
Defining AI (System)
Demystification Approach
→ mathematical, focused on processes
→ treats AI as a tool
A machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how
to generate outputs such as predictions, content, recommendations, or decisions that can influence
physical or virtual environments. Different AI systems vary in their levels of autonomy and
adaptiveness after deployment (OECD, 2024)
Anthropomorphic Approach
→ treats AI as human, refers to human intelligence
→ effectance motivation → humans want to make sense of the complex world & connect
Efforts to understand human intelligence by recreating a mind within a machine and to develop
technologies that perform tasks associated with some level of human intelligence (Guzman & Lewis,
2020) → centers around AI’s human abilities
, Effectance Motivation Example
→ humans have the innate need to control, master and understand the environment and when AI
behaves in unpredictable ways, they project human characteristics (anthropomorphism) onto it to make
sense of its actions and regain a sense of control
General AI = human-like version of AI capable of executing multiple complex tasks, smart robots
Narrow AI = specific focus (image or text generation), autonomous driving
Autonomy = degree to which a system can learn or act without human involvement (OECD, 2024)
- self-driving cars
Adaptiveness = degree to which a system can continue to evolve after initial deployment (OECD,
2024)
- using user behavior (moving emails to spam) to continuously refine filters
Mind Matters (Grey et al.)
- explored how people explore different entities (person, animals, god, AI)
- abilities are attributed based on two factors
1. Agency → act & intent
- thought, self-control, memory, morality, planning, communication
2. Experience → feel & sense
- personality, hunger/pain/pleasure, fear/rage/joy, pride/embarrassment, desires,
consciousness
Findings
→ humans score high on agency and experience
→ AI scores high on agency (autonomous tasks), but low on experience (no feelings or free will)
→ when we humanize AI → AI appears more likable, less dangerous, relatable, engaging, desirable
__________________________________________________________________________________
LECTURE 2 - Organizational Sensitivity & Machine Learning
LECTURE 1
Strategic Communication
= encompasses all communication that is substantial for the survival and sustained success of an entity
(Zerfass, 2018)
Entity
= humans and non-humans (companies, NGOs, etc)
3 criteria
1. sphere of responsibility / influence
2. contested purpose (mission → advancing AI)
3. limited resources (prioritization, protecting assets)
(All) Communication
= interactive process of meaning construction
3 lenses (Van Ruler, 2018)
1. One-way (linear) process
- no feedback, predictable pattern
2. Two-way process
- interactive, meanings not fixed, receivers not passive (Altman responding)
, 3. Omnidirectional Diachronic process
= interplay between social actors, related to each other only in the context of developing their
meaning continuously over time, constructing society itself and constructing ideas about how
organizations in society should behave (Van Ruler, 2018)
Omnidirectional
- interaction with the process of meaning construction
- not linear → goes in multiple directions
- actors not related or in proximity
Diachronic
- not static → development happens over time
- continuously moving forward, always dependent on the past
- informing present and future
- a continuous cycle
Implications of the omnidirectional diachronic model
→ communication is about LISTENING, not only Messaging
→ communication is NOT LIMITED TO FORMAL MESSAGES, but also ACTIONS that convey
meaning
→ communication happens in INTERNAL and EXTERNAL arenas
Substantial
= objective + subjective dimension
- subjective = CEOs identify issue as strategic
- objective = the real (retrospective) impact
,7 factors that make an issue strategic
1. Resource-driven → demands allocation of high-value assets or valuable resources
- pouring money into AI = strategic
2. Competition-driven → strengthens a competitive edge/bypasses direct competition
- AI poses a competitive advantage, keeps companies competitive
3. Environment-driven → comes with political, technological or ecological changes
- AI is a political issue; companies want to have their own software to remain
independent from larger companies
4. Risk-driven → escalates into a high-risk scenario and forces a critical decision/triggers crisis
- AI’s decision-making can be biased and cause unequal treatment
5. Innovation-driven → introduces groundbreaking changes that disrupt existing structures or
methods
- becomes substantial for the survival of the organization
6. Engagement-driven → leverages free resources to signal strategic priorities and influence
stakeholders
- investing in AI signals priorities and further direction of the company
7. Operationally-driven → overhauls organization and processes of the organization
- organizations change as a consequence of AI implementation
strategic significance = can only be understood in retrospect
- easily missed, inaccurately prioritized or deprioritized
Defining AI (System)
Demystification Approach
→ mathematical, focused on processes
→ treats AI as a tool
A machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how
to generate outputs such as predictions, content, recommendations, or decisions that can influence
physical or virtual environments. Different AI systems vary in their levels of autonomy and
adaptiveness after deployment (OECD, 2024)
Anthropomorphic Approach
→ treats AI as human, refers to human intelligence
→ effectance motivation → humans want to make sense of the complex world & connect
Efforts to understand human intelligence by recreating a mind within a machine and to develop
technologies that perform tasks associated with some level of human intelligence (Guzman & Lewis,
2020) → centers around AI’s human abilities
, Effectance Motivation Example
→ humans have the innate need to control, master and understand the environment and when AI
behaves in unpredictable ways, they project human characteristics (anthropomorphism) onto it to make
sense of its actions and regain a sense of control
General AI = human-like version of AI capable of executing multiple complex tasks, smart robots
Narrow AI = specific focus (image or text generation), autonomous driving
Autonomy = degree to which a system can learn or act without human involvement (OECD, 2024)
- self-driving cars
Adaptiveness = degree to which a system can continue to evolve after initial deployment (OECD,
2024)
- using user behavior (moving emails to spam) to continuously refine filters
Mind Matters (Grey et al.)
- explored how people explore different entities (person, animals, god, AI)
- abilities are attributed based on two factors
1. Agency → act & intent
- thought, self-control, memory, morality, planning, communication
2. Experience → feel & sense
- personality, hunger/pain/pleasure, fear/rage/joy, pride/embarrassment, desires,
consciousness
Findings
→ humans score high on agency and experience
→ AI scores high on agency (autonomous tasks), but low on experience (no feelings or free will)
→ when we humanize AI → AI appears more likable, less dangerous, relatable, engaging, desirable
__________________________________________________________________________________
LECTURE 2 - Organizational Sensitivity & Machine Learning
