Classification
AI Lifecycle Management
Overview
The Planning Stage is the foundational phase in the AI lifecycle, where organizations define the problem to be solved, articulate the mission and objectives, and determine the scope of the AI initiative. This stage involves critical decisions such as whether to build a custom AI solution or procure an existing one, and requires a thorough gap analysis to assess impact, feasibility, resources, risks, and alignment with business needs. The Planning Stage also includes stakeholder identification, initial risk assessment, and the establishment of success metrics. While this phase sets the direction for subsequent development, a key limitation is that early assumptions may later prove inaccurate, potentially necessitating significant course corrections. Additionally, insufficient stakeholder engagement or incomplete risk identification at this stage can undermine the effectiveness and safety of the entire AI project.
Governance Context
The Planning Stage is addressed in several AI governance frameworks, such as the NIST AI Risk Management Framework and the OECD AI Principles. These frameworks impose obligations like conducting a documented risk assessment and engaging stakeholders to identify potential impacts and mitigation strategies. For example, NIST RMF requires organizations to define intended use, context, and potential harms before model development, while ISO/IEC 42001:2023 mandates clear documentation of objectives and accountability structures. Controls at this stage include formal approval gates, documentation of decision rationales, and alignment checks with legal and ethical standards, such as GDPR's requirement for Data Protection Impact Assessments (DPIA) prior to deploying AI impacting individuals. Additional concrete obligations include: (1) Documenting and reviewing the business case and risk profile before proceeding to design or procurement, and (2) Ensuring that stakeholder engagement and impact assessments are formally recorded and revisited as the project evolves.
Ethical & Societal Implications
The Planning Stage is critical for embedding ethical considerations and societal values into AI projects from the outset. Decisions made here affect fairness, accountability, transparency, and potential societal impacts, such as bias or exclusion. Failure to adequately consider ethical risks or engage affected communities may result in harm, loss of trust, or regulatory non-compliance. Early-stage oversight helps ensure that AI systems align with human rights, legal requirements, and public expectations. Inadequate planning may also lead to the deployment of systems that perpetuate or amplify social inequalities.
Key Takeaways
The Planning Stage sets the foundation for successful and responsible AI projects.; Key activities include problem definition, objective setting, and gap analysis.; Governance frameworks mandate documented risk assessments and stakeholder engagement.; Early-stage oversights can lead to ethical, legal, or operational failures later.; Build vs. procure decisions should be informed by feasibility, risk, and alignment with objectives.; Thorough planning enhances transparency, accountability, and societal trust in AI systems.; Documenting and revisiting planning decisions is essential for project adaptability and compliance.