Private AI Readiness Assessment

Building a trusted foundation for Private AI

Many organizations see the potential of AI, but hesitate to move forward due to valid concerns around data privacy, sovereignty, security, and operational control. Public AI services are easy to start with, yet difficult to govern. Adopting Private AI requires a fundamentally different and well prepared foundation.

The ITQ Private AI Readiness Assessment Service helps organizations understand whether they are ready for Private AI and what it will take to design, build, and operate it safely, compliantly, and at scale.

Requesting an assessment

The challenge

Organizations exploring Private AI often struggle with unclear definitions, fragmented decision making, and a lack of operational readiness. Typical challenges include uncertainty around what Private AI means for their specific environment, concerns about data sovereignty and regulatory compliance, limited insight into suitable platform and deployment models, skills gaps in AI Ops and MLOps, and unclear ownership across teams.

Without proper readiness, Private AI initiatives risk becoming expensive, slow to deliver, or difficult to control.

How this assessment helps

The Private AI Readiness Assessment is a structured evaluation of your organization’s preparedness to adopt Private AI. Using ITQ’s proven frameworks and assessment approach, we assess readiness across business, technology, security, data, and operations, and translate insights into a clear and actionable path forward.

You gain a realistic view of where you stand today and what is required to move forward with confidence.

Key benefits

Clear insight into your current readiness for Private AI

Early identification of security, compliance, and governance risks

Architectural direction tailored to your environment and ambitions

Improved operational confidence across AI Ops and cloud native teams

Faster and safer adoption by avoiding redesign and rework later on

What we assess

The assessment covers all critical dimensions required for a successful Private AI foundation.

Strategy and ambition alignment

We align on why Private AI is being considered, define internal and external AI ambitions, and clarify success criteria and non negotiables such as sovereignty, isolation, and control.

Security, compliance and data sovereignty

We assess regulatory and jurisdictional constraints, data classification and access models, security zoning and tenant isolation requirements, as well as auditability and observability expectations.

Platform and infrastructure readiness

We evaluate your current infrastructure including virtual machines, Kubernetes and GPU availability, datacenter readiness, platform options, and integration with identity, networking, and monitoring systems.

Data and AI platform readiness

We assess data availability and quality, access patterns, model lifecycle requirements, and readiness for architectures such as RAG and agent based AI.

Skills, operations and governance

We evaluate DevOps and cloud native maturity, AI Ops and MLOps capabilities, organizational ownership, operating models, and cultural readiness for AI enabled workflows.

Readiness dimensions

Readiness is evaluated across the following domains:

Business and strategy
Security and data sovereignty
Infrastructure and platform
Data and AI architecture
Skills and operations
Governance and ownership

What the assessment includes

Executive and technical workshops
Security and compliance deep dives
Infrastructure and platform analysis
Skills and operating model assessment
Gap analysis and risk identification

Deliverables

The assessment results in a comprehensive Private AI Readiness Assessment report, including a readiness scorecard per domain and a clear overview of key risks and gaps. You receive architectural direction tailored to your environment, along with an actionable roadmap that guides your organisation step by step towards a secure and scalable Private AI foundation.

What’s Next?

Based on the outcomes, ITQ can support you with next steps such as Private AI platform design, AI proof of concept initiatives, use case assessments, Private AI foundation and implementation, and managed AI and platform services.

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