Collection
30 free guides, frameworks, and implementation resources
Microsoft Learn
For: CIOs, transformation leaders, enterprise architects, and AI program sponsors.
Why explore: A structured AI strategy that identifies the business outcomes being pursued, the types of AI capabilities required, and the organizational conditions needed for execution.
Microsoft Learn
For: Enterprise AI program managers and cross-functional implementation teams.
Why explore: A practical process for transforming broad strategic priorities into an actionable initiative portfolio.
Google Cloud
For: Leaders assessing enterprise-wide AI readiness and transformation priorities.
Why explore: A shared vocabulary for discussing present capabilities, desired future capabilities, and the gaps between them.
Stanford Digital Economy Lab
For: Strategy executives, innovation leaders, researchers, and transformation sponsors.
Why explore: Evidence-informed questions about the relationship between AI systems, organizational design, human participation, and execution.
Microsoft Learn
For: Investment committees, transformation offices, and enterprise AI portfolio managers.
Why explore: A repeatable approach for comparing competing opportunities rather than prioritizing whichever proposal has the most enthusiastic sponsor.
Microsoft Learn
For: AI program leaders, HR capability teams, and technology decision-makers.
Why explore: A practical view of which initiatives are realistic given existing capabilities.
Google Cloud
For: Enterprise capability architects and transformation offices.
Why explore: An organized capability-building agenda rather than an isolated list of technology purchases.
Microsoft Learn
For: Platform engineering teams, enterprise architects, and infrastructure leads.
Why explore: A checklist of the operating conditions required to support enterprise AI workloads.
Microsoft Learn
For: CIO organizations, operating-model designers, and transformation leaders.
Why explore: Explicit ownership of implementation, security, operational support, and ongoing performance.
Google AI for Developers
For: AI engineering teams, product teams, and technical risk specialists.
Why explore: A clearer understanding of the capabilities required for responsible application development and evaluation.
National Institute of Standards and Technology
For: Enterprise risk managers, AI governance teams, auditors, and program owners.
Why explore: A structured set of risk-management activities that can be adapted to individual applications.
NIST
For: Responsible AI leads, risk specialists, architects, and security reviewers.
Why explore: A more specific risk vocabulary for generative AI initiatives.
Microsoft Learn
For: Governance offices, security leaders, compliance teams, and enterprise architects.
Why explore: A model for operationalizing AI policies through repeatable processes and controls.
OWASP GenAI Security Project
For: Application security teams, AI engineers, security architects, and technical risk managers.
Why explore: A security-focused review framework for LLM-enabled enterprise applications.
OWASP GenAI Security Project
For: Teams developing autonomous or semi-autonomous workflows.
Why explore: A practical foundation for agent-specific threat modeling.
World Economic Forum and Capgemini
For: Enterprise AI leaders, agent platform owners, governance teams, and operating-model designers.
Why explore: A way to treat AI agent authority as an explicit organizational decision.
Microsoft Learn
For: Organizations moving from AI assistants to integrated AI agents.
Why explore: A structured view of the additional operating-model requirements introduced by agent-based systems.
Microsoft Learn
For: Enterprise architects, chief information security officers, and AI platform teams.
Why explore: A model for separating centralized enterprise standards from application-specific responsibilities.
Microsoft Learn
For: Transformation leaders, enterprise architects, and operational governance teams.
Why explore: A structured approach to defining central and local ownership.
Microsoft Learn
For: AI operations teams, IT service owners, and enterprise program offices.
Why explore: A method for preventing the ownership gap that often appears between project completion and routine operations.
Microsoft Learn
For: Enterprise platform teams and AI solution architects.
Why explore: A deployment planning structure that covers more than the AI model itself.
Microsoft Learn
For: Security engineers and teams deploying connected AI applications.
Why explore: A baseline for securing the applications and integrations surrounding an AI model.
OWASP
For: AI security architects, red teams, and agent platform engineers.
Why explore: An evolving collection of threat and control references for agentic systems.
Google AI for Developers
For: AI application developers, evaluation engineers, and quality-assurance teams.
Why explore: A structured approach to turning desired behavior and safety expectations into repeatable evaluation exercises.
OWASP
For: Delivery teams preparing a generative AI application for production.
Why explore: A security release checklist grounded in recognized categories of LLM application risk.
NIST
For: AI governance teams, evaluation specialists, and operational managers.
Why explore: A method for selecting measurements aligned with the risks and intended use of an AI system.
NIST
For: AI risk owners, operations leads, and governance committees.
Why explore: A disciplined process for turning observed evidence into decisions about the system.
Microsoft Learn
For: AI operations teams, FinOps practitioners, and application owners.
Why explore: A structure for continuously managing the resources and processes on which AI systems depend.
NIST
For: Product owners, governance teams, and enterprise risk specialists.
Why explore: A framework for detecting changes in context that may affect expected value or risk.
NIST
For: Enterprise AI councils, risk committees, and transformation program leaders.
Why explore: A mechanism for turning individual deployment lessons into enterprise-wide standards.
