A social impact program is only as strong as the experience of everyone in it.
Your employees need to submit a matching gift and get a response before they lose interest. Your nonprofit partners need to resolve a question about a disbursement without waiting days for a reply. Your administrators need to manage a high-volume campaign without being buried in tickets.
When any part of that breaks down, trust erodes in the program, in the platform and in the company behind it.
That is the part of AI in social impact that rarely gets talked about. Everyone covers what AI does in product. Fewer people talk about what it does to the support experience: the part your users actually feel every day.
Here is what we did at Benevity, what we learned and what it means for your program.
Why we started: the numbers that told us something had to change
Benevity's support team handles three very different audiences: employees making donations and volunteering, social impact or grant administrators running programs and the nonprofit community that receives disbursements. To give some context, this means we serve about 1,000 companies and their 7.7M employees who partake in giving and volunteering, as well as the 2.4M nonprofits in our network. In 2024, this translated to more than 350,000 support tickets, across time zones, languages and urgency levels.
We set a high bar for our support experience, and we saw a clear opportunity to get there faster. With a goal of responding to every ticket within one business day and a customer satisfaction target of 90%, we had a concrete north star to work toward. AI became a key part of how we planned to close that gap.
Part of what made speed a priority was something we are genuinely proud of: Benevity’s culture of internal growth. In a single 12-month window, 19% of our support team advanced within the organization, a testament to the talent we attract and develop. That momentum also meant we were regularly welcoming new agents, with a six-to-eight week ramp before they were fully productive. To maintain quality at that pace, we needed smarter systems, not just more headcount. That’s when we started looking seriously at AI.
What we built: AI as a complement, not a replacement
The instinct when you first explore AI in support is to go straight to self-service bots. We deliberately did the opposite. We started with the agents.
Before we touched the external experience, we focused on what would make our own team faster, smarter and less burned out. Three internal capabilities changed everything.
Sentiment analysis: When a ticket comes in that needs extra care, our system now identifies sentiment so these tickets are automatically escalated to the right operations leader immediately. We stopped relying on a human to spot distress in a sea of volume. The result: escalated users are now actioned 58% faster than they were before.
Intent analysis and skills-based routing: AI categorizes incoming tickets and matches them to the agent best equipped to answer. Less time triaging. Faster resolution. And for new agents, the structure means they start answering real tickets in week three of training instead of week eight.
Language tools for agents: At the click of a button, an agent can make a response more formal, more friendly or more complete. Suggested articles and similar tickets surface relevant knowledge automatically, so agents spend less time searching and more time crafting effective responses.
Together, these internal changes produced a measurable shift: agents are saving one to five minutes per ticket on average, which translates to roughly a 20% efficiency gain across the team.
What changed for your employees, nonprofit partners and administrators
The internal improvements made what came next possible.
Once our agents were working faster and with more confidence, we turned to the external experience. For the nonprofit community, we introduced Georgie, an AI-assisted bot that answers questions based on our help center content, the way a knowledgeable human would. Before Georgie, nonprofits navigating questions about eligibility, disbursements or account setup waited for a human agent. Now, the answers they need most are available immediately.
One thing we learned building Georgie: the quality of the bot is entirely dependent on the quality of the content behind it. We used the Gunning Fog Index, a readability test, to audit every article in our help center. Content should score at an eighth-grade reading level or below to be genuinely usable. Ours did not always meet that bar. So, we rewrote it. That content work is invisible to end users, but it is what makes the self-service experience feel intelligent rather than frustrating.
Grace and Genie extended the same capability to client administrators and employee end users, bringing AI-assisted support to every audience we serve.
The results across all three audiences, within six months of implementation:
- 80% of tickets now receive a meaningful first response within one business day
- CSAT improved to 80% (with 90% as our ongoing goal)
- Escalated users actioned 58% faster than non-escalated tickets
- New agents contributing by week three, not week six to eight
What this means for social impact leaders choosing a platform
Support quality is a trust signal.
When an employee cannot get an issue resolved, they stop participating. When a nonprofit cannot reach someone about a payment, they lose confidence in the pipeline. When a program administrator is waiting for answers during a high-volume campaign, the program suffers.
AI in support is not a cost play. Done right, it is a quality play. Most important, the people who feel it most are the ones your program needs to drive participation and impact.
At Benevity, being AI-native goes beyond what the product does. It is about how we work: how we code, build, how we operate and how we show up for the people your program depends on. Applying AI to support is that same philosophy in action.
The AI tools do not replace the judgment, empathy and expertise of our agents. They handle the administrative layer — searching, routing, adjusting tone — so agents can focus on the conversations that actually need them.
That is what AI-native looks like in practice — not a feature list, but a fundamentally different way of working.
Frequently asked questions
How does Benevity use AI in its support operations? Benevity applies AI across three areas of support: internal agent tools (sentiment analysis, intent-based routing, language assistance and knowledge suggestions), self-service bots for nonprofit partners and employees, and automation for common request types. These capabilities work together to reduce response times, surface the right help at the right moment and allow agents to focus on complex or sensitive cases.
How does Benevity's AI support benefit nonprofit partners? Benevity's AI-powered support bot for the nonprofit community provides immediate answers to common questions about eligibility, disbursements and account management — based on a regularly updated, human-reviewed help center. Nonprofits no longer need to wait for an agent response for the most frequently asked questions. For anything more complex, a human agent remains available and is routed based on the nature of the request.
Does AI in support replace Benevity's human agents? No. Benevity's approach is explicit: AI handles the high-volume, repetitive work so agents can focus on what needs a human — complex issues, sensitive situations, escalations and relationship-building. Sentiment analysis, for example, identifies tickets that need extra care and surfaces them to a human immediately. AI is a tool for the team, not a substitute for it.
What results has Benevity seen from its AI support implementation? Within the first six months of implementation, Benevity improved the number of tickets resolved within one business day to 80%, improved customer satisfaction to 80% and reduced response time for escalated users by 58%. Agents report saving one to five minutes per ticket on average (roughly a 20% efficiency improvement) and new agents begin handling live tickets in week three of onboarding rather than week six to eight.
How does Benevity ensure its AI support is accurate and trustworthy? Benevity's self-service AI is built on human-written, regularly reviewed help center content that is assessed for readability using the Gunning Fog Index, targeting an eighth-grade reading level or below. Agents have full visibility into AI-suggested responses and can accept, modify or override any suggestion. All AI outputs are reviewed for quality, and the team maintains active change management to ensure adoption and ongoing refinement.
What is Benevity's broader AI strategy for client support? Support AI at Benevity is one part of a broader AI-native approach that includes AI in Product Development Lifecycle (AI-DLC), AI capabilities embedded in grants management and matching workflows and responsible AI governance including a Chief AI Officer and a published Responsible AI Policy. The goal across all of these is the same: reduce the administrative burden on the people running programs so they can focus on impact.
Benevity powers social impact programs for leading companies around the world — from employee giving and volunteering to grants management and nonprofit disbursements.
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