AI Maturity Assessment | Qubitly Ventures

01How does your organisation currently approach AI strategy?

ANo formal AI strategy; AI not on the leadership agenda BLeadership aware of AI but no formal strategy or roadmap exists CA documented AI strategy exists with prioritised business use cases DAI strategy embedded in corporate roadmap with C-suite sponsorship EAI drives our competitive differentiation; we have dedicated AI leadership (e.g., CAIO)

02What is the status of your dedicated AI budget?

ANo dedicated AI budget exists BSmall ad-hoc budget for isolated experiments only CFormal, annually approved AI budget with defined projects DMulti-year AI investment roadmap with a growing dedicated budget EAI is treated as core R&D; board-level budget oversight in place

03How engaged is your leadership team with AI outcomes?

ALeadership has limited awareness of AI capabilities or results BA few internal champions exist; broader leadership not engaged CLeadership receives regular AI updates and approves key investments DAI features on every leadership agenda; executive accountability in place ELeadership drives AI ambition; AI outcomes tied to executive performance

01How would you describe the quality and accessibility of your organisation's data?

AData is siloed across systems and largely inaccessible for AI use BSome data is accessible but quality is inconsistent and pipelines are fragile CData pipelines exist for core business areas; quality is actively improving DUnified data platform; governed and accessible across the organisation EReal-time data mesh or fabric; data treated as an enterprise strategic asset

02What is the maturity of your cloud and AI infrastructure?

APrimarily on-premise; no cloud AI services in use BSome cloud migration underway; exploring AI services from providers CUsing cloud AI services for specific pilot use cases DHybrid or cloud-native architecture optimised for AI; MLOps tooling deployed EFully cloud-native; automated scaling, CI/CD for models, multi-cloud flexibility

03How mature is your data governance and compliance posture?

ANo formal data governance programme exists BBasic data policies exist but are not consistently enforced CData governance framework in place with assigned data owners DStrong governance with automated quality monitoring and access controls EEnterprise governance enables AI at scale; real-time quality and compliance controls

01Which best describes your current AI and ML adoption level?

ANo AI or ML applications in production BExperimenting with off-the-shelf AI tools or vendor APIs CSome custom ML models in production for specific business functions DMultiple AI applications across the organisation; exploring LLMs and GenAI EComprehensive AI portfolio: predictive, generative, and agentic AI in production

02How is your organisation leveraging Large Language Models (LLMs) and Generative AI?

ANot yet exploring LLMs or Generative AI BIndividual employees informally using ChatGPT or Copilot CPiloting 1–2 structured GenAI use cases (document summarisation, code generation) DMultiple GenAI applications deployed; RAG systems and fine-tuned models in use EGenAI embedded in core workflows; AI agents autonomously executing business processes

03What is your AI agent and autonomous workflow maturity?

ANot considering AI agents or autonomous workflow automation BResearching multi-agent frameworks; assessing feasibility CRunning a POC for an AI agent in a specific business function DOne or more AI agents in production; working on orchestration and scaling EMulti-agent systems autonomously handling complex, cross-functional workflows

01How would you describe AI literacy across your organisation?

AVery low; most employees have no exposure to AI concepts BA few AI champions exist; broader awareness is limited and informal CAI awareness training has reached a significant portion of the workforce DAI-literate workforce; functional teams actively using AI tools in daily work EAI-first culture; every employee understands their role in an AI-enabled organisation

02What is the state of your internal AI talent and capability?

ANo dedicated data science or AI resources internally B1–2 data analysts; AI work is predominantly outsourced CSmall data science or AI team (3–10 people) building use cases DEstablished AI Centre of Excellence with multi-disciplinary teams ELarge in-house AI engineering organisation; talent is a key competitive asset

03How does your organisation manage change related to AI adoption?

ANo change management for AI; resistance is common BAd-hoc communication about AI; limited structured adoption support CStructured change programme in place for major AI deployments DChange management embedded in every AI project; adoption tracked and reported EAI adoption is self-reinforcing; employees actively drive demand for new capabilities

01How does your organisation approach AI risk management?

ANo AI-specific risk framework; risks not formally assessed BAware of AI risks but no formal process or controls in place CAI risk assessed project-by-project with documented mitigations DEnterprise AI risk framework with defined controls, monitoring, and oversight EContinuous AI risk monitoring; automated guardrails and red-team testing embedded

02How do you address AI security threats such as prompt injection?

ASecurity considerations not applied to our AI systems BAware of risks; reviewing options but no controls deployed CBasic input and output filtering applied to AI systems in pilot phase DPrompt injection shields, RBAC, and DevSecOps practices in production EZero-trust AI security architecture; continuous adversarial testing and audit trails

03How do you approach responsible AI, fairness, and transparency?

ANo policies on ethics, fairness, or responsible AI BHigh-level ethical principles discussed but not enforced or operationalised CResponsible AI guidelines drafted and applied to new AI projects DAI ethics committee in place; bias testing conducted pre-deployment EResponsible AI embedded in design; model cards and transparency reports published

01How many AI use cases does your organisation currently have in production?

AZero — no AI use cases in production B1–2 use cases at pilot stage; limited to a single team or function C3–7 use cases in active production across one or two business units D8–20 use cases deployed across multiple functions; actively measuring ROI E20+ AI use cases pervasive across the enterprise; AI is core infrastructure

02What measurable business impact has your AI delivered?

ANo measurable business impact from AI yet BCan demonstrate early value from isolated pilots (time saved, errors reduced) C10–25% efficiency improvement in specific, measurable business processes DSignificant, quantified revenue impact or cost reduction across departments EAI is a primary revenue driver and key competitive differentiator in the market

03How does your organisation scale AI from pilot to production?

ANo process for scaling; pilots rarely reach production BSome pilots moved to production ad-hoc with significant manual effort CDefined process for moving AI from pilot to production with clear gates DRepeatable, rapid scaling of AI solutions; standardised deployment frameworks ESelf-serve AI platform; business units independently deploy and scale AI capabilities

AI Maturity
Assessment

Tell us about your organisation

Strategy & Vision

Data & Infrastructure

Technology & AI Capabilities

Talent & Culture

Governance & Security

Business Impact & ROI

Analysing your responses…

Your AI Maturity Report

Overall AI Maturity Score

Capability Radar

Ready to accelerate your AI journey?

AI MaturityAssessment