The six dimensions of AI maturity: your roadmap to transformation

This is evident even in cautious markets. Swiss companies, despite high interest in AI, lag behind international peers in infrastructure readiness and strategic deployment, leaving significant potential untapped. ⁽⁴⁾

These barriers signal a need for strategic clarity. The path forward begins with an honest assessment.

By evaluating your organisation's AI maturity across critical dimensions, you can identify capability gaps, prioritise investments, and chart a realistic roadmap to value creation. Deloitte's AI Maturity Framework provides the diagnostic lens, helping you understand where you stand today and the concrete steps required to advance your AI capabilities tomorrow.

Evaluate where you stand across the six dimensions of Deloitte’s AI maturity framework

Deloitte’s AI Maturity Framework is built on six dimensions:

1. Strategy - The organisation's AI vision, goals, and alignment with business objectives.
2. People - Talent models, workforce strategy and workforce enablement for AI usage.
3. Process – Mechanisms for identifying, prioritising and tracking value of AI use cases, alongside governance structures for AI implementation and operations.
4. Risk & Ethics - Security, compliance, and ethical safeguards across AI systems.
5. Data - Data governance, quality management, and making data ready to support AI initiatives.
6. Technology - Platforms, tools, and technical infrastructure enabling Data & AI deployment.

Each dimension is interdependent. A mature strategy without the right talent will stall. Advanced technology without proper governance creates risk. Data quality without clear processes leads to wasted resources. True AI maturity requires balanced development across all six dimensions.

Below, we examine each dimension in detail, outlining the critical success factors that guide successful AI integration within an organisation. 

1. Strategy: setting your AI North Star

To establish a foundation for successful AI transformation, an organisation must define a clear AI vision and goals that are aligned with business objectives and supported by leadership commitment.
 

Success factors

• Business-embedded vision: A clear, enterprise-wide AI vision aligned with business strategy drives measurable, long-term returns. The most successful organisations embed AI into their core business strategy, not as a separate technology initiative. This alignment ensures that AI investments directly support revenue growth, cost optimisation, and competitive advantage.
• Strategic use case portfolio: A realistic, multi-year roadmap with clear sequencing and investment philosophy prevents fragmented spending. Strategic growth criteria and aligned budgets ensure resources flow to the highest-impact initiatives. The roadmap should balance quick wins (6-12 months) with transformational initiatives (2-3 years), maintaining momentum while building organisational capability.
• Leadership backing : Backing from the C-suite is essential. Visible, sustained commitment from senior leadership helps to overcome organisational resistance, secures funding, and signals organisational commitment. Without executive backing, AI initiatives struggle to secure resources, overcome organisational silos and achieve cross-functional alignment.
• Organisational setup: Organisational structure and governance directly impact how AI initiatives are prioritised, funded, and executed. Unclear ownership, fragmented decision-making, and misaligned incentives create bottlenecks and duplicate efforts. Mature organisations establish clear AI governance structures, whether through a Centre of Excellence, embedded AI teams, or hybrid models, that provide oversight, accountability, and cross-functional coordination.

2. People: building your AI-ready workforce

To ensure optimal adoption, an organisation should focus on building the talent, skills, and capabilities needed to implement and scale AI that best fits its structure and culture.

Success factors

• Upskilling and AI fluency: AI adoption requires diverse skills, from data scientists and machine learning engineers to business analysts, domain experts, and change managers. Without targeted upskilling, organisations face talent gaps, slow project delivery, and an inability to scale.
• Change management and cultural readiness: AI adoption fundamentally changes how work gets done, requiring shifts in mindset, processes, and decision-making. Without proactive change management, organisations face resistance and a low level of adoption and therefore fail to realise the benefits of AI. The workforce needs to be prepared for evolution of its role, addressing fears about automation and building confidence in AI-driven decision-making.
• Performance management: Traditional performance metrics may not align with AI-driven ways of working, creating misaligned incentives and behaviours. Performance management systems must evolve to reward collaboration, data-driven decision-making, continuous learning, and responsible AI use.
  

3. Process: turning ideas into impact

To ensure that AI use cases generate value and solve business pain points, an organisation must set up the mechanisms and frameworks to identify, prioritise, and measure AI use cases value as well as the required governance to realise them.

Success factors

• Business value: A multitude of use cases exist across functions; not all are worth pursuing. Mature organisations prioritise initiatives that address critical business challenges, improve efficiency, or create competitive advantage. All use cases should be aligned with the organisation’s strategic priorities and measured against clear ROI targets.
• AI lifecycle governance: Consistent governance ensures proper oversight, accountability, and control throughout the use-case lifecycle. Clear approval gates, monitoring, and escalation procedures strengthen the path to value. Governance should balance speed with rigour, enabling rapid experimentation while maintaining control over deployment.
• Feasibility and cost: Evaluating technical feasibility and total cost of ownership is critical before committing resources. Organisations should assess whether they have the required technical infrastructure, tools, and expertise to implement and sustain the solution. A comprehensive business case should reflect expected costs and benefits, tracked throughout implementation to ensure accountability and learning.
  

4. Risk and ethics: responsible AI at scale

To ensure responsible AI practices, an organisation must implement comprehensive policies and governance frameworks that address security, regulatory compliance, and ethical considerations across all AI initiatives.

Success factors

• Risk and ethical requirements: AI introduces multiple risks: regulatory (GDPR, the AI Act, sector-specific regulations), ethical (bias, fairness, transparency), security (vulnerabilities to cyberattacks, potential misuse, data protection, and system integrity) and legal (liability, intellectual property). Organisations must clearly understand which risks and requirements apply to their context and ensure appropriate mitigation.
• End-to-end ownership: AI risks and ethics are multi-disciplinary and cannot be owned by a single function. Mature organisations define clear ownership across risk categories, data governance, model governance, compliance, ethics, and ensure proper governance and accountability.
• Technical guardrails: AI risk management requires concrete technical controls across the lifecycle. Without guardrails embedded in systems, governance remains aspirational. This is where many organisations struggle: translating policy into enforced technical practice.

5. Data: the foundation of AI success

To power AI effectively an organisation must establish robust governance systems and infrastructure to ensure data quality, security, accessibility and compliance throughout the data lifecycle.

Success factors

• Data product strategy: Strategically select and prioritize data assets that directly support priority AI use-cases and business value. Define how data can be reused and shared across the organization to maximize ROI and avoid siloed investments.
  
• Data quality & AI readiness:  Document business context and metadata to enable powerful AI use-cases and support "talk to data" LLM scenarios. High data quality is essential for AI success. Define clear standards for data readiness, ensuring data is clean, well-documented, structured for consumption by AI agents and users, and rich with business context and metadata. Standardized formats and quality gates accelerate time-to-value.
• Data sourcing and discovery:  Establish clear authoritative data sources with strong lineage and governance. Treat data as a product with clear ownership and SLAs. Expose AI-ready assets through a discoverable marketplace where agents, developers and users can find, understand, and consume well-governed data products.

6. Technology: enabling scalable AI operations

To enable scalable and reliable AI operations an organisation needs to select, implement, and maintain AI platforms and tools that support secure and efficient deployment.

Success factors

• AI platform and tools strategy: Platform and tool choices directly impact team productivity, model quality, and organisational scalability. Define a clear strategy balancing build/buy/reuse decisions multi-vendor approach, and sovereignty requirements.
• Scalable infrastructure: AI workloads are computationally intensive. Without scalable infrastructure, organisations face bottlenecks during training, cannot handle production traffic spikes, and struggle to support multiple concurrent projects.
• ML & LLM Ops and automation: Manual AI development processes are error-prone, slow, and don't scale. MLOps and LLMOps automation ensures continuous testing, deployment, monitoring, and updates without human intervention.
• Agentic orchestration: Define a clear target architecture for how AI agents, models, and data systems interact. Establish orchestration patterns that enable agents to work autonomously, integrate with business processes, and scale across use-cases without creating new silos.
• Integration and delivery: Model value is only realised when integrated into business processes and accessible to end-users. Design integration architecture that connects AI outputs to workflows, ensures adoption, and drives measurable business impact.

What you can do

AI maturity is not a destination, it's a journey. Organisations that systematically assess and advance their capabilities across the six dimensions we highlight are better positioned to unlock AI's transformative potential and achieve measurable business impact.

Understanding where your organisation stands today is the critical first step. Whether you're just beginning your AI journey or looking to scale existing initiatives, a comprehensive maturity assessment can reveal capability gaps, enable investments to be prioritised, and chart a clear path forward.