This section outlines approaches for establishing effective AI leadership within organizations of varying sizes and complexities. It provides guidance on defining leadership roles that establish appropriate authority and control mechanisms such as a dedicated Chief AI Officer or a committee-led initiative. Our goal is to outline required competencies and provide clear guidance on building and supporting organizational structures that enable effective AI implementation. This section emphasizes the importance of a flexible yet codified strategy, transparency, user rights, and company-wide awareness to ensure AI is an asset, not a risk.

This section focuses on providing organizations with frameworks for identifying, assessing, and mitigating AI-related risks. It offers comprehensive approaches to risk evaluation, covering both technical and organizational considerations. Our guidance aims to enable leaders to understand and address the full spectrum of risks, from data security to impacts in education and the workforce. Specifically for Generative AI, our guidance details twelve unique or exacerbated risk areas as identified by NIST AI 600-1. A few of these risk areas include configuration, data privacy, harmful bias, intellectual property, and strategies for assessment and mitigation.

This section focuses on how organizations can leverage existing policies or support the creation of policy to assess and mitigate risks associated with emerging technologies like generative AI. Our work highlights the importance of scrutinizing vendor acceptable use, terms of use, and privacy policies to identify compliance gaps and mitigate risks of unauthorized data ownership and use. We provide examples of both permissive and restrictive generative AI acceptable use policies. We encourage conducting gap analyses of existing IT, acceptable use, and data management policies and consider sector-specific regulations and laws.

In this section, we provide guidance on organizational training needs across all levels, offering frameworks for identifying required skills, mapping them against available resources, and creating clear development pathways for both leadership and employees. We provide frameworks for assessing training effectiveness and validating skill acquisition and certification. Our work includes modules on AI Fundamentals, Prompting, Introduction to AI Tools, Ethical and Responsible AI Implementation, Developing Governance Structures, AI Project Management, and AI in the workplace. Our goal is to help everyone responsibly explore use cases in their jobs and build an entrepreneurial AI mindset.

This section equips organizations with practical tools to evaluate their preparedness for AI implementation. The tools we outline will help organizations understand their current state and identify areas requiring attention before AI implementation. Among others, we provide guidance on data readiness evaluation tools, gap analysis, cultural readiness assessment and development, and planning techniques.

This section focuses on frameworks for evaluating and selecting appropriate AI tools and technologies. Our work includes developing evaluation criteria that consider technical capabilities, needed roles (e.g., Chief AI Officer, AI/Data Science Engineers), organizational fit, user needs, long-term viability, and integration requirements. We address support and maintenance considerations that include technical support, software updates, and Total Cost of Ownership (TCO).

This section provides practical approaches to understanding and measuring returns on AI investment. In this section, we outline methods for identifying quantitative (reduced operational costs, increased revenue) and qualitative benefits (improved decision-making, enhanced customer experience). Our work also provides frameworks for establishing meaningful metrics, approaches to calculating and tracking ROI over time, templates for benefit tracking, and guidelines for measuring indirect benefits.

In this section, we address the challenges of organizational change management in AI implementation and integration. We cover developing approaches for stakeholder engagement, communication planning, resistance management, implementation sequencing, and success planning. Our work includes AI readiness quizzes to assess team and leadership preparedness for AI adoption.

In this section, we provide guidance on anticipating and preparing for ongoing technological change. We provide frameworks for monitoring technological trends, planning for workforce evolution, and building organizational flexibility. Our work aims to help organizations maintain agility while making sound strategic decisions about their AI future.

This section focuses on strategies for publicizing and distributing the above content. We explore methods such as open-access platforms, partnerships with industry associations, targeted outreach, and leveraging conferences or webinars. Our work emphasizes creating a clear communication plan to highlight the above contents value and relevance to diverse organizational needs, facilitating its adoption.