Book-First Route
Start with Diagnosing AI Projects if you want the practical entry point.
The book is the shortest path into the Issue Space method: figures, reading sample, and a direct Amazon link for readers deciding whether to buy.
Doctoral Research Completed, made public-friendly
AI Competence 2035
A public-friendly gateway to the dissertation, with one clear focus: AI preparedness for organizations and society.
Direct Contact
If the book maps a live AI problem, contact me directly.
Readers, consultants, and teams can use this site to move from diagnosis to conversation: advisory exchange, speaking, workshop ideas, or follow-up questions.
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.
Defense completed (20 Feb. 2026)
- 2035 Foresight horizon
- 4 Methods combined
- Focus AI preparedness
Why This Matters
AI is moving fast. Skills and judgment must keep up.
The challenge
AI changes work, decisions, and public services faster than many institutions can adapt.
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.
Chapter 7 Implications
Practical implications for society, organizations, and policy
For citizens
As AI scales, preserving meaningful human discretion, workforce sustainability, and trustworthy use becomes a core public concern.
For organizations
Use the Issue Space as a diagnostic tool, reconfigure competences across AI lifecycle phases, and build special competences to reduce platform lock-in risks.
For policy and education systems
Combine anticipatory policy tools with curriculum redesign to protect entry-level pathways and strengthen AI literacy, ethics, and collaboration capabilities.
From Thesis to Book
Diagnosing AI Projects turns the research into a shorter field guide
If you are looking specifically for Diagnosing AI Projects, this is the shorter public-facing entry point built around AI project diagnosis, the Issue Space method, and a direct sample PDF.
What it covers
AI project diagnosis before teams overspend, overhire, or scale the wrong response.
Core method
Issue Space: a six-dimension way to test a complaint before locking into one explanation.
Best next step
Read the sample, review the visuals, and if the diagnosis mirrors your own situation, contact the author directly.
Essay Series
Absurdism in the AI Era writes about contradiction, judgment, and what should still remain human.
This blog section turns workflow tools into bilingual essays. The first two posts come from two open-source skills: one on subagent delegation discipline, one on sprint-based paper revision.
Essay 01 · Workflow Judgment
Defaulting to One Agent Is Not Conservative. It Defends the Cost of Meaning.
A bilingual essay built from token-efficient-subagent-decomposition: why multi-agent enthusiasm often confuses visible activity with real progress.
Essay 02 · Writing Systems
PaperSprint: When Writing Speeds Up, Final Responsibility Becomes Scarcer.
A bilingual essay built from PaperSprint: why academic writing needs structured sprints, not endless AI-assisted drift.
About the Author
Shengxing Yang, PhD
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?
Yes. This page can be shared as a post-defense summary. The full thesis text is temporarily unavailable.
Sources
Source package timeline
Update Notice
Full thesis text is temporarily unavailable.
Defense is completed and graduation is confirmed. Public source materials and the full-text package will be uploaded once final publication packaging is ready.
Release timing: To be announced (defense completed on February 20, 2026).
For urgent academic inquiries: shengxing.yang@universite-paris-saclay.fr
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