EA Research: GenAI to support Capability Maturity Assessment

Fri, 14 Nov 2025 10:12:35 GMT

Good day,

I am contacting fellow Enterprise Architecture practitioners to request approximately ten minutes of your time to share your experience and perspective. I am currently undertaking a research project with The Open University (UK) examining the feasibility of applying Generative AI to automate Business Capability Maturity Assessments and improve strategic execution.

As part of this study, I am gathering practitioner insights to understand current practices, challenges, and views on AI-enabled assessment. If you are willing to contribute, I would greatly appreciate your participation in a short questionnaire of around twenty questions:

👉 Survey link: https://forms.cloud.microsoft/r/M8arm5tckD

Your professional insight would be extremely valuable, and all responses are anonymous. Thank you in advance for considering this contribution to the EA community.

Kind regards,
Ralfe Poisson

Wed, 19 Nov 2025 13:03:35 GMT

Hi Ralfe, Thanks for your post. I would like to have a call to discuss how the AEA can help with this. I will be contacting you.

Thu, 20 Nov 2025 12:36:43 GMT

Hi Ralfe, Hi All , The assessment is a great initiative.
I happened to come across an interesting article on that subject
Summarized:
Impact and Implications of Generative AI for Enterprise Architects in Agile Environments: A Systematic Literature Review

Link: https://arxiv.org/html/2510.22003v1

1. Core Opportunities of GenAI in EA
• Artifact generation: Automates code, models, diagrams, and documentation → reduces cycle time and supports agile sprints.
• Design ideation: Rapid exploration of architectural alternatives → helps balance scalability, performance, and maintainability.
• Decision support: Scenario simulation and trade-off analysis → strengthens architects’ advisory role in complex environments.
• Knowledge retrieval: Organizes architectural knowledge and requirements → improves stakeholder communication and alignment.
Practical takeaway: EA teams can embed GenAI into modeling tools and documentation workflows to accelerate delivery while maintaining traceability.

2. Risks and Constraints
• Opacity (“black box”): Limits explainability and governance.
• Bias & hallucinations: Outputs may be contextually wrong, requiring rework.
• Compliance concerns: Privacy and regulatory risks when handling sensitive data.
• Skill erosion: Over-reliance may weaken architects’ analytical judgment.
• Scalability limits: Context windows restrict large-scale enterprise modeling.
Practical takeaway: Always validate GenAI outputs with human oversight, embed audit trails, and enforce compliance checks before adoption.

3. Evolving Role of the Architect
• Shift from creator → curator/validator of AI outputs.
• New competencies:
o Prompt engineering (designing effective queries)
o Model evaluation (assessing reliability and bias)
o Governance oversight (ensuring ethical and compliant use)
• Expanded roles: strategic advisor, technical coach, crisis manager, and facilitator across agile teams.
Practical takeaway: EA leaders should invest in training architects on AI literacy, governance, and oversight skills.

4. Organizational Enablers
• Education & upskilling: Prompt engineering, GenAI evaluation, responsible AI use.
• Governance maturity: Policies, ethical oversight, traceability mechanisms.
• Readiness domains: Infrastructure, skilled personnel, data governance, legal compliance, strategic alignment.
• Culture: Avoid “social loafing” (over-reliance on AI) by fostering accountability.
Practical takeaway: Build a GenAI adoption roadmap that combines technical readiness with governance frameworks and cultural adaptation.

5. Adaptations Required
• Dynamic governance frameworks: Iterative, evolving with model performance and regulation.
• Data-handling protocols: Privacy-preserving measures, audit trails, compliance with standards (e.g., GDPR).
• Trust-building: Transparent communication, responsible usage guidelines, stakeholder engagement.
Practical takeaway: EA offices should integrate GenAI governance into existing frameworks (TOGAF, SAFe) and establish clear accountability structures.

Final Practical Guidance for EA
• Use GenAI for speed and ideation, but never delegate final judgment.
• Treat architects as human-AI orchestrators, ensuring alignment with enterprise strategy.
• Build capability stacks: technical (tools), organizational (governance), and human (skills).
• Focus on responsible experimentation: pilot GenAI in low-risk areas, scale with governance maturity.