Strategic decision-making

Scenario 1

Your team is embracing AI tools for strategic decision-making.
How can you ensure a balanced human-machine relationship for optimal outcomes?

Navigate uncertainties and promote AI augemented success

 Digital transformation demands profound shifts in perspective and renewed mindset:

Be curious about AI, view it as a collaborator, embrace uncertainties and complexities, understand the strength of AI and intuition in different domains and how they complement each other,  keep a visionary, innovative approach, focus on ethical standard and related unique contributions/value creation as tasks become automated, emphasize the need for accelerated learning trajectories and upskilling and manage a blended human-machine workforce

Sustainabilty versus optimum in decision making 

 AI and human intuition are not opposing forces but complement each other. They excel in different areas respectively where the other falls short.

AI excels in automation, optimization and forecasts in stable context but tends to overoptimize solutions, i.e. results are statistically accurate but lacking context relevance. 
In latter, human intuition excels with simple, robust and sustainable responses.

Humans and machines should train in their distinct domains,
- AI focusing on optimization and automation and
- humans on a.o. intuition, flexibility, holistic thinking, AI competences

What remains for decision-makers and creative people as AI tools shoulder some of their responsibilities?

Maintaining the advantage of a human-centric approach 
in strategic planning, navigating uncertainties and complexity, balancing human-machine workforce and strength, choosing sustainability over optimum in decision-making
Shifting organizational culture towards innovation, agility and continuous learning
Pursuing an ethical AI use 
Accelerated learning trajectories for teams and AI
Upskilling and empowering teams in intuition, emotional intelligence, critical thinking, active listening, flexibility, adaptability, communication and motivation


Leadership and business strategy

Scenario 2

You are tasked with creating a business stratgey that incorporates AI for innovation. 
Why is it important  to align AI incorporating strategies with long-term goals?

AI and leadership

AI increases complexities and responsibilities in leadership: visionary thinking remains crucial
AI cannot replace human capability for imagination and a vision rooted in meaning and purpose 
Ethical deployment of AI is essential to ensure fairness and prevent harm and to guarantee good business practice
Managing a blended human-AI workforce leads to useful and sustainable innovation and an improved ROI in AI.

AI impact on teams and collaboration?

AI integration in platforms, communication and workflows, is leading to more global interdisciplinary collaboration and enhance innovation:
Real-time translation tools break language barriers, enabling global teams.
Blurring lines between sectors and expertise enables new co-creation in more diverse global teams 

Creativity and innovation - a sole human strength?

AI operates rationally, weighing alternatives, probabilities and benefits, 
while a human decision -in volatile context- is selective and intuitive 
AI is relying on big data sets, humans are relying on past experiences and accumulated knowledge based on emotions
Human intuition drives creativity and innovation through intuitive decision-making 
Creativity and innovation stem from human intuition, which AI cannot achieve due to its inability to adopt to diverse perspectives or adapt to unusual environments autonomously.

Change management and employee concerns

Scenario 3
Your organization is implementing AI to optimize customer service. Team members express concern about job displacements.

What are most relevant measures during AI adoption, respectively transformative times?

Pivotal shifts in workforce dynamics, roles and responsibilities of individuals, teams and organizations

Approximately 30% of tasks in 60% of jobs have the potential for automation according to a McKinsey study. However, in most cases it will augment rather than replace workforce. Following  becomes crucial in the transformative era:

Recognize that AI lacks intrinsic human understanding, making soft skills such as intuition, critical thinking and active listening in leadership vital 

Adapt to AI’s unknown impacts, emphasizing the need for adaptation, flexibility and accelerated learning trajectories and upskilling
Advocate for ethical AI use, ensuring that AI guidelines/policies respect privacy, fairness, and transparency not just as a matter of ethics but also good business, building trust with consumers.
Ensure a clear and effective communication, managing uncertainties and empowering individuals and teams

Future Corporate Culture with Artificial Intelligence?

Integrating AI shifts organizational culture towards:
Ethos/ principles regarding routine tasks, innovation, agility and continuous learning
Adaptation, i.e. balancing human and machine strengths is essential for success 
Ethical deployment of AI to ensure fairness and prevent harm and to guarantee good business.

Decision-makers and creative people  play a crucial role in understanding human needs and motivations and bridging the gap between machine-driven insights and human understanding and motivation:

Embracing uncertainties and AI as a collaborator
Understanding AI implications and adapt accordingly
AI can provide insights and analysis, but it lacks intrinsic human ability to understand emotions, culture and nuances
Intuition, emotional intelligence, critical thinking and active listening gain importance 
Bridging the gap between data-driven insights generated by AI and human understanding/motivation is crucial
Providing a vision with meaning and purpose, unique value creation, human understanding, team motivation

Ethical AI use I Considerations

Scenario 4

AI has identified potential cost-cutting, but proposed changes might be over-optimized and impact employee morale and motivation. 
What are essential considerations?

AI versus human intuition: 

Artificial Intelligence excels in automation, optimization and forecasts/predictions due to independent learning and pattern/correlations recognition in large datasets in stable conditions, when complexity and uncertainty is low.
It may over-optimize, i.e. results are statistically accurate but lacking contextual relevance. 
Humans are overwhelmed by processing large datasets but excel in understanding a situation/challenge instinctively, without the need for conscious reasoning (=intuition)

Sustainability versus optimum in decision-making

AI systems (=neuronal networks) aim to replicate human brain functions but lack the simplicity, robustness, and resilience found in human intuition. 
 AI models focus on optimizing tasks with vast amount of data while human intuition seeks simple, robust and resilient solutions based on individual experiences and skills, emphasizing sustainability.
 The fundamental similarity between both is training. 

For Human intervention it is crucial to apply and consciously train competencies that AI lacks, emphasizing the need for sustainable decision-making in transformative times
Ethical deployment of AI is essential to ensure fairness and prevent harm and to guarantee good team dynamics and business practice
A profound understanding of human intuition, ethics and motivation is crucial to manage a successfully blended human-AI workforce dynamic

Criteria for trusting AI or intuition

Decision-making involves considering specific criteria such as task, data sets, context, complexity and uncertainty to determine whether to rely on AI or human intuition, emphasizing the need to trust each in their appropriate domain.
 Recognition, allocation and comparison requires a lot of data, stable context while complexity and uncertainties are low, here AI can find a fit for purpose solution
Creativity, innovation or challenges in a volatile context, with  insufficient data, high in complexity and uncertainties, here human intuition will give a fit for purpose response: simple, robust and sustainable. 
Optimization and prediction/forecast can be handled in a mixed approach, i.e. mainly by AI with human reflection and checks.