For CTOs & IT Leadership
Which AI architecture fits your tech stack?
12 technical questions — 5 minutes — a clear architecture recommendation.
Use standard tools via APIs, minimal custom build. Fits when speed beats control and data sensitivity is low.
points: 0–10
Build a governance layer, integrate with existing systems, use managed platforms. Fits with growing complexity and compliance requirements.
points: 11–16
Own infrastructure, own models, full data control. Fits when data sovereignty, scaling, and independence are business-critical.
points: 17–24
Inventory
Which AI tools are running productively in your company today — with or without IT approval?
Data Architecture
How well is your company knowledge structured and accessible — for humans and for AI systems?
Integration Base
Which of your core systems — CRM, ERP, document storage, communication — are API-ready for AI integration?
Data Security
Where does company data leave your infrastructure today — through AI tools, APIs, or cloud services?
Compliance
Can you demonstrate EU AI Act, GDPR, and industry-specific requirements for AI systems today?
Vendor Lock-in
How dependent are you on individual AI vendors — and what would a vendor switch cost you in 3 years?
Build vs. Buy
Where do you want to build your own AI capabilities — and where are standard solutions sufficient?
Data Sovereignty
Must your data and AI models run in your own infrastructure — or is an EU-compliant cloud sufficient?
Scalability
Will your planned AI architecture still be viable in 3 years — when usage, data volume, and requirements grow?
Team & Skills
Do you have internal competence to build, operate, and evolve AI systems — or do you need external partners?
Roadmap
Do you have a prioritised list of AI use cases with clear business cases — or are you starting from scratch?
Governance
Who owns AI architecture decisions in your company — and how do you ensure technology and business strategy stay aligned?