The challenge
AI changes work, decisions, and public services faster than many institutions can adapt.
Doctoral Research, made public-friendly
AI Competence 2035
A public-friendly gateway to the dissertation, with one clear focus: AI preparedness for organizations and society.
Thesis Brief
Architecting AI Competences Toward 2035: A Mixed-Methods Study on AI Issue Space Mapping and the Articulation of an Organizational Competence Taxonomy.
Supervisor Professor Ahmed Bounfour, Emeritus Professor, Universite Paris-Saclay.
- 2035 Foresight horizon
- 4 Methods combined
- Focus AI preparedness
Why This Matters
AI is moving fast. Skills and judgment must keep up.
The question
Which competences should organizations build now to stay responsible and effective by 2035?
The contribution
A practical taxonomy linking AI issues to concrete competence choices.
How It Was Studied
Mixed methods, one clear logic
01
Map the issue space
Systematic review of AI transformation issues in management and policy contexts.
02
Refine the taxonomy
Semi-Delphi expert dialogue to test, adjust, and strengthen competence categories.
03
Check on real-world signals
International survey and complementary empirical analysis across institutional settings.
Key Findings
Three ideas ordinary readers can use
Competence is not only technical
Governance, discretion, ethics, and coordination are as critical as model-building skills.
Different sectors need different mixes
There is no one-size-fits-all AI skill model; context and institutional role matter.
Platforms shape capabilities
Platform choices influence what organizations can learn, govern, and scale over time.
Chapter 5 Lens
Three-level challenges identified in Chapter 5
Micro level
Discretion Crisis
As AI guidance expands, human discretion in professional judgment can narrow, creating tension between efficiency and responsible decision-making.
Meso level
Platform Dependency
Organizations may become dependent on a few platforms for models, data, and tooling, which can reduce strategic autonomy and bargaining power.
Macro level
AI Acceleration
The speed of AI deployment can outpace regulation, institutional learning, and social adaptation, amplifying systemic risk.
Issue-Driven Framework
Issue Space categories and Competence categories
In this thesis, competences are not listed first. They are derived from issues: the Issue Space diagnoses challenge dimensions, and competence categories define organizational responses.
Issue Space (6 dimensions)
AI Competence Categories (6 dimensions)
01
Domain-Specific Aspects
Context fit of AI with sector workflows and knowledge.
01
Sector-Specific Domain Expertise
Industry and task understanding to make AI relevant in context.
02
Technical and Technological Aspects
Data quality, integration, infrastructure, and cybersecurity.
02
Technological (or Material) Competences
Technical and infrastructural foundations to build and run AI.
03
Managerial Leadership
Strategic alignment, culture, resources, trust, and leadership support.
03
Strategic and Organizational Competences
Ongoing alignment between AI initiatives, goals, and change capacity.
04
Organizational Intelligence
Learning, adaptation, readiness assessment, and resource configuration.
04
Cognitive Competences
Organizational learning, sensemaking, and adaptive decision capability.
05
Relationships and Networking
Human-AI interaction, cross-functional coordination, and stakeholder alignment.
05
Interactional Competences
Collaboration and coordination among humans, AI systems, and stakeholders.
06
Ethical and Wider Impacts
Agency, labor effects, bias, regulation, and societal concerns.
06
Ethical and Societal Competences
Ability to anticipate and handle legal, ethical, and social impacts.
Selected mapping 01 / 06
Domain-Specific Aspects -> Sector-Specific Domain Expertise
Issue: Context fit of AI with sector workflows and knowledge. | Competence: Industry and task understanding to make AI relevant in context.
How they connect (issue → competence)
This mapping is a starting structure. In practice, competences overlap and must be bundled dynamically across contexts and transformation stages.
What This Means For People
From abstract theory to daily impact
For citizens
Better AI competences can improve fairness, transparency, and accountability in digital services.
For organizations
A competence-based approach helps prioritize training, governance design, and long-term strategy.
For policy communities
It offers a shared language to align innovation goals with human-centric safeguards.
About the Author
Shengxing Yang
FAQ
Common questions, plain answers
Why should non-experts care about this thesis?
Because AI decisions increasingly affect education, jobs, rights, and access to services.
Is this only about engineers?
No. The research shows managerial, legal, and ethical competences are central too.
Does it provide practical guidance?
Yes. It proposes a taxonomy that can guide competence planning and strategic priorities.
Can I cite or share this?
Absolutely. Use the source materials below and mention the dissertation title.
Sources
Source package timeline
Update Notice
Source files will be published after formal thesis approval.
To keep consistency with the final validated manuscript, public source materials will be uploaded once the dissertation is officially approved.
Expected release window: After the official approval process (post-defense on February 20, 2026).
For urgent academic inquiries: shengxing.yang@universite-paris-saclay.fr
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