Fund capacity, not tech: Building a business case for AI in charities (Part 3/4)

You have laid the strategic groundwork. In Part 2, we mapped the relational core identifying the vital web of trust that underpins your services and used the drudgery versus delight audit to decide what must remain human. However, even the best strategy needs a budget. Now you face the hurdle that stalls most charity innovation: building a robust AI business case that wins over skeptical Boards and secures the necessary resources.

For boards and funders, the acronym “AI” often triggers two immediate concerns: high costs and unmanageable risk. That scepticism is actually healthy. It is grounded in a history of over-promised and under-delivered change and innovation projects. Indeed industry reports suggest that up to 95% of corporate AI pilots fail to move beyond the experimental phase because they lack a clear path to value.

However, the greatest risk to the charity sector right now is the gradual erosion of capacity caused by burnout, administrative backlog, and rising demand. Therefore, to secure funding for your pilot, I suggest shifting away from asking for a technology budget and instead making the case for an investment in service resilience.

Step 1: The strategic reframe from "overhead" to "digital capability"

A common mistake charities make is burying tech costs in the "IT" or "Admin" budget lines. In a sector constantly under pressure to minimise overheads, these are the first budget lines to be scrutinised. To secure funding, try re-framing AI adoption as digital capability that augments frontline team capacity rather than a software purchase. Just make sure to work with your Finance Director to ensure this framing meets your charity's specific reporting standards.

The Old Frame (Overhead):

"We need £5,000 for a software license to automate email sorting."

The Investable Frame (Capacity):

"We are investing in a triage tool that releases 500 hours of caseworker time annually. This allows us to handle X% more crisis calls without increasing our headcount providing augmented capacity that handles high-volume admin so our frontline team can focus on complex care."

This aligns perfectly with your relational core strategy (Part 2). It proves to your Board that you are automating the process so the human can focus on the person.

Step 2: The triangulated charity AI business case

In my time leading charity innovation programmes, I found that pitching innovation rarely wins over a skeptical Board. Instead, address the three distinct needs in the room:

• Financial Prudence (The Treasurer)

• Mission Impact (The Chair/CEO)

• Governance Safety (The Risk Committee)

1. The efficiency case (financial prudence)

The logic: AI adoption in charities is often less about cutting costs but rather operational cost avoidance. It’s about meeting rising demand without a linear rise in payroll.

The evidence: Use your drudgery audit from Part 2.

The response: "This pilot costs £2,000. It automates booking administration that currently consumes 10 hours a week of our people's time (520 hours/year). If we had to hire staff to cover that time, it would cost £12,000. Effectively, this tool purchases capacity at £3.80 per hour, freeing up our technical staff and volunteers to return to front-line client work. Even if we only realise 50% of these gains due to the learning curve, the ROI remains significantly higher than manual processing."

2. The impact case (mission)

The logic: combing efficiency and effectiveness gives you a compelling narrative. Show how this moves the needle on strategic goals like equity, speed, or access.

The evidence: Link to the safe-to-automate tasks (green zone) which I will detail in the implementation framework in Part 4.

The response: "Currently, non-English speakers wait 3 days for triage. By using AI to support realtime translation, we can reduce that wait to 4 hours. This technology upgrade is an equity initiative that allows us to reach marginalised communities without increasing frontline burnout."

3. The governance case (safety)

The logic: Boards rightly fear "zombie projects". The initiatives that keep eating money with no clear end. A strong proposal demonstrates exactly how you will contain the risk.

The evidence: Reference the assurance trail and 3am stress test we will cover Part 4.

The response: "We are not asking for a full rollout. We are requesting an 8-week gated experiment. We have set pre-agreed success metrics (The 'Go/No-Go' criteria). If accuracy drops below X%, we decommission the tool immediately. The financial risk is capped at the cost of the pilot, but the organisational learning remains highly valuable."

The Triangulated charity AI business case cheat sheet

Dimension	Key focus	Evidence	Projected value
Efficiency	Unit cost reduction	Drudgery hours saved	Hours/year (redeployed to mission)
Impact	Mission outcomes	Equity & Access gains	Additional beneficiaries served
Governance	Risk containment	Gated pilot	Finance risk capped; Learning Assured

Step 3: Make the ask irresistible (templates)

If stakeholders have to hunt for the ROI, the default answer is "No." Below are practical templates you can adapt.

Template A: The internal board paper (executive summary)

Use this when asking for internal reserves to fund a pilot.

• Proposal: Pilot to increase [service area] capacity via automated workflow.

  
  

• Problem: Our team currently spends [X] hours/week on [drudgery task], creating a backlog of [Y] clients.

  
  

• Solution: A 10-week, ring-fenced pilot using [tool name] to automate this specific task under human supervision.

  
  

• Financial ask: £[X] (software) + £[Y] (training/implementation time) + £[Z] (Oversight & Audit Buffer). This ensures we have ring-fenced the human time required for the 'Human-on-the-Loop' safety checks defined in Part 4.

  
  

• The Learning Dividend: In the first 3 months, we expect a learning dividend rather than pure efficiency. This time will be used to refine the tool's accuracy. Post-pilot, we project a release of [X] hours annually for direct mission work. We project this will allow us to serve [X] additional beneficiaries per year with existing staff. In addition, we anticipate a reduction in staff 'drudgery' hours, will leading to lower burnout risk in high-pressure roles as measured by absence rates.

  
  

• Risk control: Success metrics will be reviewed at Week 5. If accuracy drops below [X%], the pilot stops.

Template B: The grant funder proposition

Use this when applying for capacity building or innovation grants. Funders love scalability and open source learning.

• Title: A scalable model to reduce unit costs in [Service Area].

  
  

• Pitch: We are seeking support to pilot an AI-assisted workflow that lowers the 'cost-per-beneficiary' in [Specific Service].

  
  

• Innovation: By automating low-risk administrative tasks, we aim to demonstrate how charities can break the link between increased demand and increased burnout.

  
  

• Sector Value: Beyond our immediate efficiency gains, we commit to open-sourcing our findings. We will publish our risk framework and implementation guide so other small charities can adopt this approach safely.

  
  

Step 4: Expect the sustainability question

Any savvy Trustee will ask: "If this works, how do we pay for it next year?" This financial sustainability is the backbone of any successful AI business case for charities. Unlike a laptop (a fixed capital cost), AI is often an on-going operational cost (subscription or usage).

Your sample answer:

"That is exactly why we are running a pilot. We need to establish the cost to serve. If the pilot proves that the tool helps us process a case for £0.50, whereas manual processing costs us £15.00 in staff time, the tool effectively pays for itself in capacity released. In future budgets, this will be categorised not as an IT overhead, but as a direct project cost proportional to the number of people we help."

Conclusion: Your charity's investable AI buisness case

Securing funding for AI isn’t about chasing hype; it is about demonstrating service stewardship. By mapping your relational core (Part 2) and building a triangulated business case (Part 3), you prove to your stakeholders that you are serious about maximizing every pound for your beneficiaries. But how do you actually run the pilot once the money is approved? In the final part of this series, Part 4, I share the "Map, Measure, Magnify" framework, a step-by-step guide to safe, disciplined AI implementation.

Part 1: Strategic Overview: Responsible AI Adoption for Charities

Part 2: The Relational Core: Keeping Humanity at the Center of Charity AI

Part 3: Securing Buy-in: Building the Business Case for Charity AI Funding

Part 4: Safety First: An Implementation Framework for Charity AI Innovation

By treating AI as a hypothesis to be tested rather than a solution to be installed, you ensure that technology serves your mission, not the other way around.

Authors note: I’m heading to UNESCO House in Paris this February for the IASEAI’26. I’m going as an attendee to listen in on the global conversation and, crucially, to see how these high-level AI standards translate (or don’t) to the messy, real-world reality of charity operations. I’ll be sharing my 'field notes' and what they actually mean for your teams on LinkedIn here.

Note: Examples are for illustrative purposes only; no official affiliation with the organisations or tools mentioned is claimed. AI systems can be unpredictable, so always keep personal or sensitive data out of third-party tools and ensure your implementation follows your own organisation’s data protection policies.