Ch 14. Looking Back to Move Forward
Learn what to do after funding is awarded.
Abstract
Chapter 14 examines grant writing beyond proposal development, emphasizing that receiving an award marks the beginning of new rhetorical, ethical, and managerial responsibilities. Revisiting the Rhetorical Thinking Model, the chapter considers how purpose, audience, community, and strategy continue to shape decision-making during implementation, reporting, evaluation, and sustainability. It explores shifting funding priorities, including merit-based and diversity-focused frames, and emphasizes the need for ethical rhetorical adaptation as grant writers respond to changing political, institutional, and funding environments. The chapter also examines the expanding role of artificial intelligence and automation in proposal development, review, compliance, reporting, and funding decisions. While these technologies offer opportunities for efficiency and access, they also introduce concerns about bias, confidentiality, transparency, and the erosion of human judgment. Ultimately, the chapter frames sustainable grant writing as an iterative practice grounded in relationships, stewardship, reflection, capacity-building, and continuous learning. Looking back at outcomes, failures, and changing conditions becomes a strategy for improving future practice and creating lasting community impact.
Revisiting the Rhetorical Thinking Model
Purpose, Audience, Community, and Strategy
Lessons from Across the Grant Lifecycle
Merit-Based and DEI Funding Rhetorical Framing
The Grant World’s Relationship with AI
Opportunities and Risks
Human Judgment in an Automated Era
Automation and the Grant-Making Process
AI-Assisted Proposal Review
Predictive Analytics and Funding Decisions
Agentic AI and Administration
Ethical Concerns and Bias
The Future of Grant Writing
New Forms of Collaboration
Data-Driven Funding Environments
Thriving in Uncertainty
Sustaining Relationships, Trust, and Stewardship
Vehicles for Social Change
Planning Sustainability through Capacity
Continuous Learning with Feedback Loops
Final Reflections
Summary