Vega IT's AI Readiness Assessment

Adopting AI is not just a technological upgrade. It requires a fundamental transformation of your business. To truly unlock AI’s value, it’s essential to understand your organisation’s level of readiness and identify which areas need improvement. This clarity enables you to adopt AI effectively and turn its potential into measurable, tangible results. Take the test and find out more!

Strategy &
Vision
Use Case &
Value Realization
Data &
Integration
Infrastructure &
Tools
Governance &
Risk Management
Talent &
Skills
Culture &
Change Readiness
Processes &
Operational Maturity

Strategy & Vision Step 1 of 8

01. How clearly defined is your organization's AI vision and strategy?

We currently do not have an AI vision or strategy
We are discussing or drafting an initial AI vision, but it is not yet documented
We have a basic AI vision that guides some initiatives but lacks alignment with business goals
We have an AI vision and strategy for some departments, though it is not yet organization-wide
We have an organizational AI strategy with a clear vision for each department and well-defined overall goals.

02. AI initiatives are tied to business KPIs/OKRs with owners and timelines.

Strongly disagree

Strongly agree

03. Who is primarily responsible for AI strategy and execution within your organization?

There is no clear owner of AI initiatives
AI projects are managed by ad-hoc project owners
A functional leader such as the Head of Data or IT oversees AI efforts
A cross-functional steering group coordinates AI activities
An executive sponsor leads AI strategy through a structured program management office.

04. AI initiatives are tied to business KPIs/OKRs with owners and timelines.

Strongly disagree

Strongly agree

05. We maintain a prioritized AI use-case backlog with ROI estimates.

Strongly disagree

Strongly agree

06. What best describes the current stage of your organization’s AI initiatives?

We have not yet identified any AI initiatives
We have generated initial ideas but have not started development
We are building prototypes or proofs of concept
We have pilot AI projects with measurable metrics
We have scaled AI solutions in production and achieved measurable ROI.

07. We use a repeatable framework to qualify and select AI opportunities.

Strongly disagree

Strongly agree

08. How does your organization currently measure the success of AI initiatives?

We do not measure AI success
We measure activity or output metrics
We measure efficiency improvements such as time or cost savings
We link AI success to business KPIs such as revenue, customer satisfaction, or risk
We measure full ROI, including adoption and change management impact.

09. Our data is accurate, complete, and up-to-date enough to support confident decision-making.

Strongly disagree

Strongly agree

10. How would you describe your organization’s central data environment and the level of access teams have to shared, high-quality data?

We do not have a central data environment
We are planning or setting one up
We have a central data platform covering a few departments
Our data platform covers most business areas but is not fully integrated
We have an enterprise-wide data platform with governed access available to all teams.

11. Key data sources are integrated across functions.

Strongly disagree

Strongly agree

12. Data ownership and stewardship roles are defined and active.

Strongly disagree

Strongly agree

13. There are formal processes for correcting or escalating data quality issues.

Strongly disagree

Strongly agree

14. To what extent is your in-house data pre-processed, cleaned, and ready for AI projects?

We currently lack a defined process for preparing data for AI projects
Most data requires manual cleaning or transformation before it can be used for AI
Some datasets are clean and AI-ready, while others require significant preparation
Most of our data is pre-processed and suitable for AI use with minimal adjustments
All critical data is consistently pre-processed and AI-ready through our data pipelines

15. We use modern platforms for data analytics (e.g., Power BI, Tableau, Looker, etc.)

Strongly disagree

Strongly agree

16. Our IT systems can handle the computing and storage needs of AI solutions without major limitations.

Strongly disagree

Strongly agree

17. How would you describe your organization’s approach to launching, monitoring, and updating AI models across projects?

We do not have any AI tools or models currently deployed
We manage AI model deployment manually
We have a basic MLOps process for deployment and monitoring
We have an end-to-end MLOps process for consistent model delivery
We operate a full AI platform with automated monitoring, rollback, and self-service capabilities.

18. Our technology environment can easily scale to support new AI projects or increased demand.

Strongly disagree

Strongly agree

19. Cost visibility (FinOps) and optimization exist for AI workloads.

Strongly disagree

Strongly agree

20. An AI policy covers privacy, security, IP, and model usage.

Strongly disagree

Strongly agree

21. How would you describe how compliance (e.g., GDPR, ISO27001, or industry regulations) is embedded in your AI workflows?

Compliance is not addressed in AI workflows
We perform ad-hoc compliance reviews
We use checklists during specific project stages
We maintain continuous controls and perform regular audits
We have automated compliance checks and audit trails built into workflows.

22. Bias, safety, and explainability are considered in model lifecycle.

Strongly disagree

Strongly agree

23. Roles for accountability (risk/ethics review, model owner) are defined.

Strongly disagree

Strongly agree

24. How is your company prioritizing budget allocation between AI deployment and other technological initiatives?

We are unsure about our AI deployment budget priorities
AI deployment is important, but it depends on other technological initiatives being implemented first
AI deployment is important, but requires reducing spending on other initiatives to fund it
AI deployment is given equal priority alongside other technological initiatives
AI deployment is the highest priority for budget allocation

25. How would you describe your organization’s access to the AI and ML skills required to deliver projects successfully?

We do not have a defined approach to accessing AI talent
We rely mainly on external contractors or freelancers
We combine internal employees with external partners for AI projects
We have a clear plan for building internal AI capability through hiring and training
We have a mature internal AI team capable of leading most AI projects independently.

26. Business teams can identify AI opportunities and specify requirements.

Strongly disagree

Strongly agree

27. We have active upskilling programs (product, data, AI literacy).

Strongly disagree

Strongly agree

28. Cross-functional collaboration (Biz–Data–IT) is effective.

Strongly disagree

Strongly agree

29. Leaders visibly champion AI with resources and messaging.

Strongly disagree

Strongly agree

30. Teams are encouraged to experiment (guardrails and room for failure).

Strongly disagree

Strongly agree

31. We actively educate employees on AI’s potential, ethical risks, and responsible use.

Strongly disagree

Strongly agree

32. How much time could your team realistically allocate to support an AI pilot?

We currently have no available capacity to support an AI pilot
We could dedicate limited time occasionally, but it would impact other priorities
We could allocate some time each week, though support would remain part-time and ad-hoc
We could dedicate consistent weekly time from key team members without major disruption to ongoing work
We have dedicated resources or a defined team ready to actively support an AI pilot from start to finis

33. Change management (comms, training, adoption) is part of delivery.

Strongly disagree

Strongly agree

34. Our key business processes are documented, standardized, and consistently followed.

Strongly disagree

Strongly agree

35. We measure how efficiently and effectively our key business processes perform (e.g., time, quality, cost, or customer satisfaction).

Strongly disagree

Strongly agree

36. How would you describe the level of digitization and integration of your organization’s key business processes (e.g., ERP, CRM, workflow tools, RPA)?

Our processes are mostly manual and handled through paper or email
Some processes are digitized but remain disconnected
We use digital tools that are not fully integrated
Our core processes run through end-to-end digital workflows
We operate fully integrated, API-enabled systems across departments.

37. We continuously improve and optimize processes using data insights or automation.

Strongly disagree

Strongly agree

38. Our processes are designed with automation and AI-readiness in mind.

Strongly disagree

Strongly agree

39. There is a clear governance process for funding, prioritizing, and monitoring AI-related investments.

Strongly disagree

Strongly agree

Critical

Moderate

Advanced

Critical (≤ 2.5):

Your organization is at an early stage of AI readiness. Foundational capabilities and alignment are still developing, which may limit your ability to scale or extract tangible value from AI initiatives. The next step is to strengthen the areas with the lowest scores to establish a stable base for future growth. Focus on creating clarity, ownership, and foundational enablers before expanding AI adoption.

Moderate (> 2.5 - ≤ 3.5):

Your organization shows emerging AI maturity with several strong foundations in place. However, uneven progress across different areas may create friction in execution and slow measurable impact. Prioritize improving the pillars with the lowest scores to achieve greater consistency and readiness for enterprise-level deployment. By addressing these targeted gaps, your organization can move confidently toward sustained AI-driven value.

Advanced (> 3.5):

Your organization demonstrates strong AI maturity and is well-positioned to scale its initiatives. The key to continued success is to maintain balance across all areas and prevent weaknesses in lower-scoring pillars from constraining growth. Focus on refining governance, measurement, and continuous improvement to turn AI into a strategic, organization-wide capability. At this level, optimization and innovation become your main levers for competitive advantage.

Vega IT's AI Readiness Assessment

Moderate

Your company’s overall AI readiness assessment

Critical (≤ 2.5):

Moderate (> 2.5 - ≤ 3.5):

Advanced (> 3.5):

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