---
id: "concept-political-alignment-projects"
type: "concept"
source_timestamps: ["§ How Can Organizations Pick the Best Gen AI Projects?", "\\\"2. Choose projects that are practical", "quick wins", "and politically aligned.\\\""]
tags: ["office-politics", "budgeting", "project-management"]
related: ["action-align-cost-benefit-silos"]
part_of: "framework-gen-ai-project-selection"
definition: "The strategic selection of initiatives where the financial costs and the resulting business benefits are contained within the same departmental budget to avoid executive friction."
sources: ["spine"]
sourceVaultSlug: "hbr-seg-spine"
originDay: 1
articleStem: "hbr-cl-95-6-disciplines-genai"
sourceUrl: "https://hbr.org/2024/07/the-6-disciplines-companies-need-to-get-the-most-out-of-gen-ai"
sourceTitle: "The 6 Disciplines Companies Need to Get the Most Out of Gen AI"
---
# Political Alignment in Project Selection

Criterion #3 of the [[framework-gen-ai-project-selection|project-selection framework]] (paired with "practical, quick wins"). A critical, often overlooked factor in selecting Gen AI projects is **organizational politics** — specifically the **locus of costs versus benefits**.

The rule: select projects where **both the financial cost of implementation and the resulting business benefit reside within the same organizational unit**. If a project incurs costs in one department (e.g., Marketing) but delivers benefits to another (e.g., Customer Service), it will struggle to gain traction — few executives will prioritize spending their own budget to help another executive hit their KPIs. The concrete step is [[action-align-cost-benefit-silos]].

Enrichment nuance: this reflects standard change-management advice to align budgets and benefits to avoid political deadlock. **Counter-perspective:** some firms deliberately fund cross-unit projects (costs in one unit, benefits in another) using central budgets or top-down mandates — viable when governance is strong. Over-optimizing for political ease risks starving strategically critical but politically difficult projects like shared AI infrastructure or cross-unit data platforms.