Assess your organization across 7 dimensions — from AI Strategy to AI Data — and get a prioritized action plan based on Gartner's AI Roadmap at a Glance framework.
7
Dimensions
5
Maturity Stages
~5
Minutes
🧭
AI Strategy
💰
AI Value
🏛
AI Organization
👥
AI People & Culture
⚖️
AI Governance
⚙️
AI Engineering
🗄
AI Data
Based on the Gartner AI Roadmap at a Glance framework (2025). Not affiliated with Gartner.
AI Strategy1 of 7
🧭
AI Strategy
Select the highest maturity stage that accurately describes your organization today. Choose based on what is fully in place, not aspirational.
1
Initiating
We have articulated an initial AI vision and are beginning to assess our current AI maturity.
Defines AI vision · Measures AI maturity
2
Exploring
We actively analyze external AI trends and have formally launched our AI strategy with leadership alignment.
Analyzes external trends · Initiates the AI strategy
3
Scaling
We communicate our AI strategy organization-wide and have set measurable adoption goals in our AI roadmap.
Communicates AI strategy · Sets adoption goals for AI roadmap
4
Optimizing
We manage a prioritized AI portfolio and track AI strategy success with defined KPIs and governance checkpoints.
Identifies AI portfolio priorities · Measures AI strategy success
5
Leading
We run a continuous, structured process to refine our AI strategy based on market signals, portfolio outcomes, and emerging trends.
Establishes process to refine AI strategy continuously
AI Value2 of 7
💰
AI Value
How mature is your organization at identifying, tracking, and scaling the business value of AI initiatives?
1
Initiating
We have identified initial AI use cases and defined how we will measure the value they should create.
Prioritizes initial use cases · Defines value metrics
2
Exploring
We are actively running AI pilots and tracking the value they generate against our defined metrics.
Runs initial AI pilots · Tracks value of initial use cases
3
Scaling
We have a formal portfolio prioritization process for AI and have introduced product management practices for AI initiatives.
Establishes portfolio prioritization process · Introduces product management
4
Optimizing
We have adopted AI FinOps practices to manage AI spend, and have launched at least one production AI product generating measurable business value.
Implements AI FinOps · Launches initial AI product
5
Leading
We have an automated AI value monitoring system and manage a mature portfolio of AI products, each with defined ownership and ROI tracking.
AI value monitoring system · Established AI product portfolio
AI Organization3 of 7
🏛
AI Organization
How well is your organization structured to enable, govern, and scale AI — including dedicated roles, teams, and external partnerships?
1
Initiating
We have created an AI resourcing plan and an AI community of practice is forming to share knowledge and build capability.
AI resourcing plan · AI community of practice
2
Exploring
We have appointed a dedicated AI leader and set up an initial AI team or Center of Excellence with clear mandate and budget.
AI leader appointed · Initial AI team / Center of Excellence
3
Scaling
We have defined our AI target operating model and formed initial external AI partnerships (vendors, academia, or ecosystem partners).
Establishes AI target operating model · Forms external AI partnerships
4
Optimizing
We have a structured process to identify, onboard, evaluate, and manage AI partnerships at scale across the organization.
Process to manage AI partnerships at scale
5
Leading
AI organizational capability is embedded and continuously improved — our CoE, operating model, and partnership network are actively evolved based on outcomes.
Continuously optimized AI organization model
AI People & Culture4 of 7
👥
AI People & Culture
How prepared are your people for AI — in terms of skills, mindset, and organizational change management?
1
Initiating
We have an initial AI workforce plan and a process to review how roles and jobs will be redesigned as AI is adopted.
AI workforce plan · Role & job redesign review
2
Exploring
We have an AI change management plan in place and have launched initial AI awareness campaigns to build broad understanding.
AI change management plan · AI awareness campaigns
3
Scaling
We have a formal process to evaluate AI's impact on the workforce and have launched a structured AI literacy program.
Evaluates AI workforce impact · AI literacy program launched
4
Optimizing
We have a network of defined AI business champions driving adoption, and we actively monitor employee readiness for AI across the organization.
Business champions for AI literacy · Employee readiness monitoring
5
Leading
AI literacy is embedded in our organizational culture — champions, readiness tracking, and upskilling are continuously driven as part of how we operate.
AI-native culture with continuous upskilling and readiness loops
AI Governance5 of 7
⚖️
AI Governance
How mature is your framework for governing AI responsibly — covering risk, ethics, policy, accountability, and compliance?
1
Initiating
We have identified top AI risks with mitigation approaches and established initial AI policies covering our primary use cases.
Identifies AI risks & mitigation · Defines initial AI policies
2
Exploring
We have established AI ethical principles and gained organizational buy-in for our overall AI governance approach.
Establishes AI ethical principles · Gains buy-in for governance
3
Scaling
We have set enforcement processes for AI governance and clearly defined decision rights and accountability for AI across the organization.
Sets enforcement processes · Defines AI decision rights
4
Optimizing
We have a cross-functional AI governance board and have defined our target governance operating model covering all critical AI domains.
Cross-functional AI governance board · Target governance operating model
5
Leading
We leverage AI literacy programs to embed governance understanding, and are piloting AI governance tooling to automate and monitor compliance.
AI literacy for governance · AI governance tooling piloted
AI Engineering6 of 7
⚙️
AI Engineering
How mature are your technical capabilities for building, deploying, and operating AI solutions — from architecture to MLOps?
1
Initiating
We have established a build vs. buy framework for AI and selected vendors for our initial AI use cases.
Build vs. buy framework · Vendor selection for initial use cases
2
Exploring
We have set up a sandbox environment for AI experimentation and developed a reusable library of design patterns for AI development.
AI sandbox environment · Library of design patterns
3
Scaling
We have defined our AI reference architecture and a clear AI vendor and application strategy to guide all major technical decisions.
AI reference architecture · AI vendor & application strategy
4
Optimizing
We have established MLOps/ModelOps practices for reliable AI deployment and set up an AI observability system to monitor model performance in production.
MLOps / ModelOps practice · AI observability system
5
Leading
AI UI/UX best practices are embedded in our product development process, and we have dedicated AI platform engineering to scale and support all AI initiatives.
AI UI/UX best practices embedded · AI platform engineering
AI Data7 of 7
🗄
AI Data
How mature are your data capabilities to enable, fuel, and sustain AI — from readiness and quality to governance and observability?
1
Initiating
We have assessed data readiness for our initial AI use cases and have implemented a data readiness plan to address identified gaps.
Data readiness assessment · Data readiness plan
2
Exploring
We have built data analytics capabilities specifically for AI and are building organizational buy-in to evolve our data capabilities further.
Builds data analytics for AI · Buy-in to evolve data capabilities
3
Scaling
We have extended data governance to support AI workloads and are actively evolving our data capabilities to meet growing AI demand.
Extends data governance for AI · Evolves data capabilities
4
Optimizing
We have established an AI data quality framework and adapted our metadata practices to support AI discovery, lineage, and reuse.
AI data quality framework · Metadata practices for AI
5
Leading
We have implemented end-to-end data observability for AI, enabling proactive detection of data drift, quality issues, and pipeline failures.
Data observability for AI fully implemented
Your AI Roadmap Assessment
Based on the Gartner AI Roadmap at a Glance framework