Artificial intelligence (AI) is transforming industries faster than ever before. Today, the question is no longer whether companies should use AI, but how effectively they can implement it. Many organizations have launched pilot projects or integrated machine learning into selected processes, yet few have a clear understanding of their AI maturity. Without this insight, there is a risk that the innovations they showcase may not deliver real value.
Assessing an organization’s readiness to leverage AI is crucial for future success. Equally important is identifying gaps, risks, and opportunities that could shape the company’s future in the AI era. In this blog, we will explore the key areas that define organizational AI maturity and share practical steps to take it to the next level.
Author
Alexander Zagnetko
Manager, Process Organization and Improvement
To move beyond experimentation and toward enterprise-wide impact, organizations must evaluate AI performance across the following six areas:
Strategy & Governance
AI maturity starts at the top. Is there a clear AI vision aligned with business goals? Are roles, responsibilities, and ethical guardrails well-defined? Mature organizations embed AI into their strategic planning and establish governance frameworks that ensure responsible innovation.
Data & Infrastructure
AI is only as good as the data it learns from. High-maturity organizations invest in data quality, accessibility, and governance. They also build scalable infrastructure-cloud platforms, data lakes, and secure pipelines-that support AI at scale.
Talent & Culture
AI success depends on people. Do you have the right mix of data scientists, engineers, and domain experts? Are employees AI-literate and empowered to innovate? A culture that embraces experimentation and continuous learning is a hallmark of AI maturity.
AI Development & Deployment
From model development to monitoring, mature organizations adopt robust MLOps practices. They ensure models are explainable, fair, and regularly audited for bias. Automation and lifecycle management are key to scaling AI safely and efficiently.
Use Cases & Impact
Are your AI initiatives delivering measurable value? Mature organizations prioritize high-impact use cases, track ROI, and scale successful pilots across business units. AI is not just a tech initiative-it’s a business transformation lever.
Risk & Compliance
With great power comes great responsibility. AI maturity includes proactive risk identification, regulatory compliance (think: EU AI Act), and incident response protocols. Organizations must anticipate ethical, operational, and reputational risks before they materialize.
In a world where AI is reshaping competitive dynamics, maturity is a differentiator. Organizations that treat AI as a core capability - not a side project - will lead in innovation, resilience, and trust. The question isn’t just “Are we using AI?” It’s “Are we using it wisely, responsibly, and at scale?”
Why a structured AI maturity assessment?
Amid rapid progress in machine learning, generative and agentic AI, many enterprises struggle to move beyond proofs‑of‑concept into robust, governed, and economically meaningful deployments. AI maturity assessment creates a shared vocabulary for where an organization stands, why gaps exist, and what it would take-organizationally, technically, and culturally-to close them. The KPMG AI Maturity Assessment (AIMA) framework is designed precisely for this – to understand current AI maturity capabilities, identify gaps and determine the strategic roadmap for data‑driven decision making.
The AIMA was developed based on insights from hundreds of real-world use cases, being more than just a collection of leading practices. It pairs high‑level capabilities with granular questions and evidence prompts, ensuring that scores are anchored in observable practices and artifacts rather than aspirations. A coordinator role, sign‑off authority, and pre/post‑assessment reflections increase the rigor and repeatability of the process.
The AIMA provides a structured way to diagnose current strengths and gaps across six pillars: Vision & Strategy; Technology & Tooling; Data Management; Processes; Risk, Governance & Ethics; and People & Culture. Each pillar contains a set of questions scored on a 1–5 scale, representing four levels of maturity:
- Elementary: Initial interest and exploration
- Emerging: Hopeful adoption with foundational capabilities
- Experienced: Operational integration and value generation
- Established: Strategic alignment and competitive advantage
The Six Pillars of AIMA
Evaluates whether the organization’s AI ambition is explicit, aligned with business goals, and measured through defensible KPIs. It also considers adoption, agility, partnerships, investment discipline, and leadership incentives.
Selected high-maturity attributes:
- Integrated strategy with explicit links to long-term business goals
- Well-defined KPIs tied to AI outcomes
- Clear scaling plans for data/user growth
- Enterprise-wide AI adoption
- Leadership incentives aligned with AI value realization
Assesses the breadth, standardization, interoperability, validation, support, and maintainability of AI technologies, including cloud posture, training, innovation, reference architectures, and LLM management.
Selected high-maturity attributes:
- Standardized, interoperable tech stack with reference architectures
- Advanced testing, SLAs, and proactive maintenance
- Cloud strategy optimized for AI needs
- Firm-wide LLM management policies
- Culture of experimentation with safe sandboxes and production pathways
Covers data strategy, governance, stewardship, operations, catalogues, discovery, transformation, validation, automation, hosting, model training, data quality, and incident management.
Selected high-maturity attributes:
- Actively followed and optimized data strategy
- Real-time tracking and rich metadata in catalogues
- Automated data quality controls
- Hybrid cloud fluency with clear integration patterns
Evaluates how AI work is executed-from project prioritization to funding, agile practices, model lifecycle, cybersecurity, and change management.
Selected high-maturity attributes:
- Portfolio-based prioritization aligned with strategy
- Repeatable model lifecycle patterns
- Enterprise agile practices applied to AI
- Integrated cybersecurity and change management
Focuses on AI policies, ethical standards, compliance, bias controls, privacy, human oversight, transparency, stakeholder engagement, and governance authorities.
Selected high-maturity attributes:
- Comprehensive, auditable AI policy integrated into workflows
- Proactive compliance with regional expertise
- Responsible AI controls with human-in-the-loop safeguards
- Transparent KPI frameworks including ethical metrics
Pillar 06 - People & Culture
Measures talent availability, upskilling, collaboration, stakeholder buy-in, diversity, innovation incentives, data literacy, and transparency.
Selected high-maturity attributes:
- Systematic upskilling with internal learning paths
- Strong cross-functional collaboration rituals
- Inclusive teams and design practices
- Incentives and psychological safety for experimentation
More than a diagnostic
The KPMG AI Maturity Assessment is more than a diagnostic - it is a blueprint for organizational learning. A high‑credibility roadmap links each low‑scoring item to a concrete initiative, an owner, time‑bound milestones, and measurable outcomes.
By assessing six pillars with anchored rubrics, capturing evidence, and rolling scores into a transparent maturity profile, leaders can turn fragmented initiatives into a cohesive, governed, and ethically grounded AI program. The assessment’s value grows with repetition: as governance bodies consume results, as roadmaps close gaps, and as culture catches up with technology.
Done well, AIMA becomes the organization’s single source of truth for AI capability, risk posture, and readiness to scale.
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