Here are common challenges that we have found organizations experience in the Buy-In stage of their feedback loop:

Leadership skepticism or misaligned expectations

• Decision-makers may not see clear value from investing in new tools or processes for community feedback.

• Some leaders assume technology automatically improves data quality without understanding the need for human judgment alongside it.

• Others may be enthusiastic about AI but underestimate the time, training, and ethical care required to use it responsibly.

Staff capacity and change fatigue

• Teams are often stretched thin. Introducing new tools can feel like an added burden rather than support.

• Previous technology rollouts that did not deliver on their promises can create skepticism or resistance.

• Staff may lack confidence in their own digital literacy, making adoption feel risky.

Community trust and meaningful consent

• Communities being asked to participate in digitally assisted feedback processes may not fully understand how their data will be used, stored, or shared.

• In contexts where communities have historically been surveilled, exploited, or ignored, digital feedback tools can feel extractive.

• Organizations often struggle to explain complex data practices in accessible, community-friendly language.

Lack of a clear technology strategy

• Organizations jump to tools before establishing what problem they are trying to solve.

• Without a technology strategy, organizations risk duplicating efforts, selecting incompatible tools, or creating unsustainable systems.

Here are ways that we have found organizations using technology tools in the Buy-In stage of their feedback loop:

For Building Your Internal Case

Project management and planning tools

• Map out your buy-in strategy, assign owners, and track progress on internal stakeholder conversations. 

  • Examples: Asana, Trello, or Notion

  • Caution: A beautiful project plan doesn't convince skeptics if it doesn't address their actual concerns. Meet with stakeholders one-on-one first, then use the tool to document agreement.

• Build compelling internal presentations that make the case for tech-assisted feedback, visually communicating community needs, capacity gaps, and potential benefits. 

  • Examples: Google Slides or Canva

Research and landscape tools

• Explore what tools are being used by similar organizations in your sector and read honest reviews from nonprofits. 

  • Examples: NTEN or TechSoup

For Understanding Your Current State & Readiness

Quick readiness assessments

• Run a quick internal readiness assessment with staff or leadership before committing to a tool or approach. 

  • Examples: Google Forms or Microsoft Forms

• Document your current tech stack, costs, and what each tool is used for before deciding what to add or change. 

  • Examples: Google Sheets or Airtable

Long-term sustainability planning

• Before committing to a tool, document its long-term costs (licensing, training, support), the staff capacity needed to maintain it, and what happens if your organization's priorities or budget change.

  • Examples: Cost-tracking spreadsheets (Google Sheets, Airtable), capacity planning documents

  • Caution: A tool that looks great in year 1 may become a burden in year 3 if it requires ongoing technical expertise your team doesn't have or costs rise significantly so try to plan for the long term.

For Supporting Adoption & Change Management

Communication and collaboration tools

• Create a dedicated channel for your technology and feedback initiative so conversations are centralized and searchable. 

  • Examples: Slack or Microsoft Teams

• Record short explainer videos for staff or community partners who need a low-pressure introduction to a proposed new approach. 

  • Example: Loom

Here are ways that we have found organizations using AI tools in the Buy-In stage of their feedback loop:

For building the internal case

• • Use an AI assistant such as Claude or ChatGPT to help draft talking points, FAQs, or a one-pager explaining the rationale for tech-assisted feedback to skeptical stakeholders.

    • Caution: AI-generated talking points can sound generic or miss your organization's specific culture and concerns. Use them as a starting point, then revise with input from people who know your stakeholders well.

  • Ask AI to generate a list of potential objections to your proposal, then use those to prepare responses before presenting to leadership.

  • AI can help summarize research on the benefits and risks of AI in community feedback so that decision-makers have a grounded basis for discussion (not marketing hype). stakeholders.

For understanding readiness & tool selection

• • AI tools can help you build a simple staff readiness quiz, generating questions that assess digital literacy, data comfort, and attitudes toward new tools.

  • Use AI to help you compare different tools across criteria like cost, accessibility, language support, and data privacy before making a recommendation.

• Ask AI to brainstorm potential challenges that might come up during adoption: "What could go wrong when we roll out this feedback tool? What might staff resist? What accessibility barriers might we miss?"

  • Caution: AI tends to generate generic challenges. Talk to your actual staff and communities about what they're worried about because their concerns may be different. Use AI to supplement, not replace, real conversations.

For supporting transparent communication with communities

• Use an AI assistant to help draft consent forms in plain language, accessibility materials, or FAQs about data practices to help communities understand what you're proposing.

  • Caution: AI drafts can sound robotic or include too much jargon. Have a community member review before sharing. Personalize it.

• Use AI to simplify complex policy language, generate plain language summaries, or help translate materials into community languages as a first draft (always have human review).

  • Caution: Machine translation is helpful but not perfect. Professional human translation is more trustworthy for legal/sensitive content about data practices.

• Use accessibility tools to test that your consent forms and explanations are actually accessible to people using screen readers or other assistive technology.

  • Examples: Accessibility checkers (built into Google Docs), plain language testing tools, auto-captioning for any videos

  • Caution: Don't assume something is accessible just because you think it is. Test with actual community members who use accessibility tools.

Note: At the buy-in stage, AI should be used primarily to support internal thinking and communication, not yet to interact directly with communities. Keep human judgment at the center of decisions about whether, when, and how to proceed.

Consent and transparency from the start

• Community members should understand in accessible language what technology will be used, what data will be collected, and how it will be used before they are asked to participate.

• Consent is not a checkbox. It requires genuine understanding, real choice, and the ability to say no without penalty.

• Buy-in connection: Don't oversell what tech can do. Include limitations, costs, and required safeguards in your pitch so stakeholders aren't surprised later.

Power and participation

• Who was involved in deciding to adopt new technology? If communities were not consulted, consider whether there is still time to involve them before finalizing your approach.

• Organizations should be honest about whose interests are being served by a given technology, whether that is efficiency for the organization or genuine benefit to the community.

• Buy-in connection: Don't frame tech-assisted feedback as just an efficiency win for your organization. Include genuine benefits to communities (e.g., "faster response to feedback," "more accessible ways to participate").

Data sovereignty

• Even before data collection begins, consider who will own the data, who can access it, and what happens to it if the organization closes or changes focus.

• Avoid locking community data into proprietary platforms where it cannot be easily accessed, exported, or deleted.

• Buy-in connection: This is a strategic decision, not a technical one. Decide early so it shapes your technology choices, not the other way around.

Equity in tool selection

• The tools you choose embed assumptions about what technology people have access to, what languages they speak, and what digital skills they have. Scrutinize those assumptions early. 

• Buy-in connection: When building buy-in for a specific tool, be honest about who it will work well for and who it might exclude. Plan accessibility features, not as an afterthought.

For additional resources and support around ethical issues concerning AI, please view this AI & Listening Resources guide.