AI Readiness

We have not considered any changes to our organizational structure with regards to AI.

We are in the early planning stages to determine if and how our organizational structure should change.

We have designed new organizational constructs to succeed in the AI era.

We have implemented changes to our organizational structure with new teams, roles, or processes for the AI era.

We have not established any formal governance mechanisms for AI.

We use our standard governance approaches to manage AI plans and usage.

We have established and communicated formal guidelines on the usage of AI tools.

We proactively manage AI usage at the enterprise level, including the allocation of resources and funding and the selection of specific AI tools.

No centralized data management strategy or policies. Data is stored in disparate systems with limited documentation, and without clear ownership/accountability. Data pipelines are mostly manual and built for one-off use cases.

Some departments have begun organizing and cataloging data and assuming data ownership. Basic data management practices exist, but they are siloed and reactive. Some data pipeline automation exists.

There is a defined data management strategy or framework. Data is cataloged and data ownership, accountability, quality, security, and access controls are actively managed. Data pipelines are reliable, reusable, and integrated into structured workflows.

Data is treated as a strategic asset, managed through an enterprise-wide, automated framework. Governance is embedded into business and IT processes and real-time monitoring and quality enforcement ensure data is trustworthy and AI-ready. Data engineering pipelines are fully automated and scalable.

Data is fragmented, inconsistent, and often unavailable for AI use.

Some efforts are in place to improve data quality and labeling, but they are manual and inconsistent.

Clean, labeled data is available for most AI use cases, with standard processes in place.

High-quality, labeled data is consistently available and maintained through automated, scalable pipelines.

Business processes are not streamlined or standardized and lack documentation. Integration of systems, data, and operations is difficult, and balancing standardization vs. customization poses a significant challenge.

Have begun to integrate our systems, data, and operations. Our primary goal is to enable cross-functional visibility and actionable insights across the enterprise.

Currently focused on improving existing processes, typically through automation and streamlining of operations. Our primary goal is efficiency – doing what we already do but better.

Focusing on business model transformation – reimagining operations, products, and services using digital and AI-native approaches. Have begun to leverage AI for strategic advantage.

We have not yet adjusted our hiring or training plans and have not changed current employee roles and responsibilities.

We are developing strategies to address role changes in response to AI, including plans for reskilling current employees and hiring for new AI-focused roles.

We are in the beginning stages of reskilling employees and shifting job responsibilities to align with AI advancements and have begun hiring for AI-centric positions.

We have fully implemented new AI-based roles and completed initiatives to upskill/reskill our workforce for AI integration.

We do not use any predictive models, NLP for data extraction, computer vision or other traditional AI technologies.

We are developing strategies to use traditional AI solutions.

We have implemented traditional AI for select use cases.

We have broadly deployed traditional AI across many parts of our business.

We do not use any GenAI tools or solutions that include Agentic AI.

We are developing strategies to use GenAI and/or Agentic AI

We have implemented GenAI/Agentic AI for select use cases

We have broadly deployed GenAI/Agentic AI across many parts of our business.

We do not have measures in place to prevent or detect AI-related incidents or address potential AI hallucinations.

We are working on a plan to detect and address AI-related incidents, hallucinations, and inaccuracies.

We have controls in place to prevent or detect AI-related incidents.

We have documented and implemented risk mitigation measures.

We do not have a formal cybersecurity owner or a documented cybersecurity policy.

We have formally assigned cybersecurity ownership, accompanied by a documented escalation process to report issues.

We have a documented cybersecurity policy that is regularly reviewed and updated.

Cybersecurity policy is well communicated, and employees are aware of the cybersecurity risks associated with AI and their responsibilities.