How Nonprofits Can Save Time and Do More with AI

This article is written by Rich Berry, CEO of 425 Consulting Group and Co-Founder of Boyer & Ritter OTS—helping organizations get more value from their systems, data, and teams.

Nonprofits are being asked to do more than ever, often with the same or fewer resources. Budgets are tight, teams are lean, and reporting expectations continue to grow.

AI is one of the first technologies I’ve seen that can meaningfully help address that challenge. Not by replacing people, but by helping your team move faster, produce more consistent work, and spend less time on repetitive tasks that don’t directly advance your mission.

For many organizations, the opportunity is closer than it seems. Most nonprofits already have access to AI today. It’s built directly into tools they’re already using, including Word, Excel, Outlook, Teams, Power BI, and Dynamics 365.

As a Microsoft partner, many of the examples here come from Microsoft tools. We often see nonprofits get started here, but the broader idea applies regardless of platform. The advantage usually comes from starting with tools your team already knows, where your data already lives, and where security and permissions are already in place.

You don’t necessarily need to buy a new system or completely change how you work. Organizations find success by starting small, focusing on practical use cases, and building from there.

Start with What You Already Have

One of the biggest misconceptions about AI is that it requires a major investment or a complete shift in systems. In most cases, the opposite is true.

The fastest way to get value is to start with tools your team already uses every day. For many nonprofits, this means Microsoft 365. For others, it might be Google Workspace or another platform with built-in AI capabilities.

Either way, the advantage is the same:

• Your data is already there
• Your team already knows the tools
• Security and permissions are in place

That combination removes much of the friction that typically slows down adoption and makes it easier to build early momentum.

At a basic level, AI in these tools acts as an assistant. It helps to take work you’ve already started and make it faster, more consistent, and easier to complete.

What This Looks Like in Practice

In practice, this shows up in simple, everyday ways.

A rough set of meeting notes can become a clean, structured summary with clear action items. A draft document can be rewritten to sound more polished and consistent. A spreadsheet that would normally take time to clean and analyze can be organized and turned into something meaningful much more quickly.

Tools like Microsoft Copilot support this work directly within Word, Excel, Outlook, and Teams. They can help draft emails, highlight next steps, and bring structure to work that would otherwise be manual and time-consuming.

Individually, these improvements may seem small. Across a team, they add up quickly.

If you’re not sure where to start, a simple approach is to ask Copilot how it can help. It can guide you in structuring prompts and improving results over time.

Getting Consistent Results with AI

One of the biggest shifts I’m seeing is the move from experimenting with AI to using it more consistently across a team.

Most people start by asking one-off questions and hoping for a good result. That works, but it’s inconsistent. The real value comes when organizations begin to define how AI should be used for common tasks.

Instead of reinventing the wheel every time, teams create reusable prompts for things like grant writing, donor communications, meeting summaries, and financial analysis.

Over time, this becomes a small internal library that anyone on the team can use. It reduces training time, improves consistency, and gives people a reliable starting point rather than asking everyone to figure it out on their own.

Practical Examples That Save Time

These are the kinds of tasks where AI can make an immediate difference:

• Turning notes into board-ready content: Take a set of meeting notes, meeting transcripts, or a rough draft and turn it into a structured, professional document in minutes instead of hours.
• Cleaning up messy donation data: Standardize inconsistent spreadsheets, remove duplicates, and identify trends to focus fundraising efforts.
• Writing stronger grant applications: Organize unstructured content into a clear, well-written narrative with consistent tone and structure.
• Summarizing meetings and decisions: Capture the discussion, decisions, and next steps, making follow-up and communication easier.
• Drafting emails more quickly: Generate a strong starting point for donor or volunteer outreach that can be reviewed and personalized.

Individually, these improvements may not seem dramatic. Across a team, they can free up a meaningful amount of time.

Where AI Supports Finance Teams

For finance teams, the impact of AI goes beyond saving time. It improves accuracy, strengthens internal controls, and supports audit readiness.

In practice, that often looks like:

• Automating invoice processing: Reduce manual entry by capturing, coding, and routing invoices more efficiently.
• Matching transactions and flagging discrepancies: Identify mismatches and unusual activity more quickly, improving accuracy and reducing risk.
• Improving collections and communication: Send reminders, predict late payments, and keep accounts receivable moving.
• Increasing visibility into financial data: Surface trends, highlight risks, and provide a clearer picture of financial performance for leadership.

Across accounts payable, this means fewer errors and better control. Across accounts receivable, it leads to faster collections and clearer communication.

Individually, these improvements save time. Together, they create more consistent processes, stronger oversight, and a more reliable financial foundation.

Expanding the Impact on Automation and Reporting

As teams become more comfortable using AI in their daily work, the next step is automation.

This is where tools like Power Automate, Power BI, and Power Apps come into play. They allow organizations to reduce repetitive processes and surface insights from data more consistently, without requiring large development efforts.

For example, a donor acknowledgment email can be triggered automatically after a contribution is received. Reports can highlight trends or outliers without requiring manual analysis. Simple applications can streamline tasks like volunteer coordination or expense tracking.

These improvements build on the tools teams already use but extend their impact in a meaningful way.

A Few Practical Guidelines for Using AI

AI adoption doesn’t need to be complicated, but it does need to be intentional.

A few simple guidelines to keep in mind:

• Stay within systems you already trust: Use AI within platforms like Microsoft 365 where your data, security, and permissions are already managed.
• Be mindful of sensitive information: Avoid entering confidential data such as Social Security numbers, banking details, or health information.
• Maintain strong access controls: Continue using multi-factor authentication and regularly review permissions and access levels.
• Review outputs before using them: AI can accelerate your work, but it’s not perfect. Human judgment is still essential.

These practices help ensure AI is used in a way that is both effective and responsible.

A Simple Way to Get Started

You don’t need a large rollout plan. Start small and build from there.

A simple four-week approach can help you begin:

• Week 1: Use AI for note-taking and basic tasks. Identify a few areas where it could save time and create a handful of standard prompts.
• Week 2: Create a consistent template for something like grant writing or recurring communications.
• Week 3: Use AI to analyze financial or donor data and identify trends or gaps.
• Week 4: Review what worked, gather feedback, and identify what to automate or expand next.

Small wins build momentum quickly and help teams build confidence using AI in everyday work.

AI is not about replacing your team. It’s about helping them operate at a higher level with the resources you already have.

For nonprofits, this means less time on repetitive tasks, more consistent output, better visibility into operations, and more time focused on the mission itself.

The organizations that get the most value from AI are not the ones that try to do everything at once. They are the ones who take a practical approach, start with what they already have, and build from there.

As organizations start putting these ideas into practice, having the right support can make a difference. At OTS, we work with nonprofits to apply these ideas practically, helping teams identify quick wins and get more value from the tools they already use.