Robson and Mitchell blog

AI for MG Fundraisers #6: Q2 Reforecast Test Reality, Not Spreadsheets

sarah@robsonandmitchell.co.uk (Sarah Robson) — Fri, 11 Sep 2026 13:35:07 GMT

24/08/2026

Your Q2 Reforecast Is Not Just a Spreadsheet Exercise

By Q2, assumptions need to meet reality. For many charities and purpose-led organisations, this is the point in the year when the original budget begins to be scrutinised. Some income has landed, some has slipped, some opportunities remain plausible, but are less certain than they appeared at the start of the year.

The temptation is to treat the reforecast as a financial exercise: update the numbers, re-phase income, rebalance the spreadsheet and move on. That misses the point.

A credible reforecast should do something more important. It should establish what is financially true now, what is still genuinely deliverable, and which organisational conditions will determine whether the revised plan succeeds or fails.

A reforecast should test reality, not protect optimism

The purpose of reforecasting is not to make the numbers look more comfortable. It's to give leadership a clearer view of risk. That means asking harder questions:

Which income assumptions are genuinely supported by evidence?
Which opportunities have weakened?
Where are renewals or major gifts exposed?
Which operational constraints are affecting delivery?
What decisions are still outstanding?
What action is realistically possible within the remainder of the year?

If those questions are not addressed, a revised forecast may look more credible while still resting on the same unsupported assumptions.

The four lenses every reforecast should use

At Robson & Mitchell, we would test a reforecast through four connected lenses.

1. Financial Reality

Start with what has actually happened. Look at year-to-date performance, current commitments, cash, restricted and unrestricted income, liabilities and the assumptions sitting behind the remaining forecast. The key question is simple:

What is financially true now?

Not what was expected in January. Not what leadership hopes will happen. What is supported by current evidence.

2. Leadership & Governance

Income performance is often affected by decisions outside the fundraising team. A major opportunity may depend on CEO involvement. A proposal may be waiting for approval. A trustee introduction may have stalled. A proposition may need a decision that has been repeatedly deferred. A reforecast should therefore ask:

Which decisions are required?
Who owns them?
Where is ambiguity slowing progress?
Where are leadership behaviour or governance dynamics affecting performance?

A financially plausible plan can still fail if the organisation cannot make the decisions required to deliver it.

3. Operational Capacity

A revised target is only meaningful if the organisation has the capacity to achieve it. That means examining staffing, vacancies, portfolio load, delivery dependencies, bottlenecks and the practical ability of teams to execute the plan. There is little value in retaining income in a forecast if the operational conditions required to secure it do not exist. The question is:

Can the organisation realistically deliver the revised plan?

4. Income Performance

Finally, test the income itself. How much is genuinely secure? Which gifts or renewals are confirmed? Which remain exposed? What evidence supports the remaining forecast? Where is confidence based on donor behaviour, and where is it based on assumption? This is where optimism most often needs to be challenged.

Where AI can help

AI can be extremely useful in a reforecasting process, but only if its role is clearly defined. It can help teams:

interrogate data and assumptions
identify inconsistencies and anomalies
structure variance analysis
classify and summarise risk
compare scenarios
test consistency
expose missing evidence
draft management and board narratives

Used well, AI can act as an analytical challenger. But it should not become an automated decision-maker. It should not approve a forecast, decide whether income is genuinely secure, infer donor intent, invent explanations for variance or create false precision. The rule is straightforward:

If the evidence is missing, AI should identify the gap, not fill it.

Start with the evidence

The quality of any analysis depends on the quality of the input. A robust Q2 review should draw from more than just the fundraising pipeline alone. It should include financial, income, operational and leadership evidence. That might include:

approved budget
year-to-date actuals
current forecast
cash position and liabilities
restricted and unrestricted income
fundraising pipeline
confirmed gifts and pledges
renewal dates
donor correspondence
staffing and vacancy information
delivery constraints
known bottlenecks
outstanding decisions
approval dependencies
material strategic assumptions

This broader view matters because high-value income rarely underperforms for one reason alone. The cause may sit in the pipeline, but it may just as easily sit in leadership behaviour, operational capacity, decision-making or proposition strength.

The real output is better decision-making

The strongest reforecast is not the one with the most sophisticated spreadsheet. It's the one that gives leadership a clear view of:

what is secure
what is exposed
what has changed
what is causing the variance
what can still be influenced
what decisions need to be made now

That's the difference between financial reporting and active performance management. A reforecast should reduce ambiguity. It should make risk visible. And it should leave the organisation with a plan that is not simply financially possible, but operationally and organisationally deliverable. Because by Q2, the objective is no longer to defend the original plan. It's to understand reality clearly enough to make better decisions.

For the full AI for Major Gifts Fundraisers #6: Reforecasting, Budgeting and Planning toolkit click here to apply the four-lens approach to your own Q2 reforecast.

AI for Major Gifts Fundraisers #5: Turning fundraising data into better decisions

sarah@robsonandmitchell.co.uk (Sarah Robson) — Fri, 11 Sep 2026 13:31:37 GMT

04/08/2026

How AI can support strategic leadership

Most fundraising reports are very good at telling us what has already happened. They can show income received, meetings held, proposals submitted and activity completed. All of this matters, but when the figures reach a senior leadership team or Board, the questions are usually rather different:

Why has performance changed? Are we confident in the forecast? Where is the greatest risk? What should we do next and what support does the fundraising team need?

For me, this is where AI starts to become genuinely interesting. Its greatest value may not be in writing more polished reports. It is in helping fundraising teams interrogate their own performance, challenge assumptions and turn their data into clearer decisions.

Moving beyond activity reporting

AI can help us explore what sits behind the numbers. For example:

Is income plateauing because the pipeline is too small or because prospects are not progressing?
Is the team carrying too many unqualified opportunities which are not being developed?
Is the forecast dependent on a few high risk gifts?
Are fundraisers constrained by portfolio size, internal decision-making or limited access to senior leaders?
Which intervention is most likely to improve performance?

AI cannot answer them reliably without context, but it can help leaders examine the available evidence, recognise patterns and consider alternative explanations. Used well, it becomes a useful thinking partner (not a replacement for an experienced fundraising leader).

Better analysis starts with better data

AI cannot compensate for an unreliable pipeline. If opportunities are out of date, stages mean different things to different people or expected income dates are repeatedly moved forward, the analysis may sound convincing while being fundamentally flawed. Before using AI to assess performance, teams need:

Clear income targets and timeframes.
Consistent pipeline stage definitions.
Accurate opportunity values and expected dates.
Current dated next actions.
Historic conversion rates and cultivation times.
Information about portfolio size and fundraiser capacity.
Relevant organisational context and constraints.

This links directly to the previous toolkit in this series on pipeline management. Better leadership reporting begins with a pipeline that genuinely reflects what is happening. If information is incomplete, ask AI to identify what is missing, not fill the gaps with assumptions.

Measure what explains future performance

Major gifts teams can easily become over reliant on activity measures: the number of meetings held, approaches made or proposals submitted. These measures can be useful, but activity alone does not tell us whether future income is secure.

Leadership reporting should also consider:

Qualified pipeline coverage against future targets.
Conversion between stages.
Time spent in each stage.
Forecast accuracy.
Donor retention and uplifts.
Opportunities without a dated next action.
Income concentrated in a small number of donors or prospects.
Portfolio size and relationship coverage.

AI can bring these measures together and help identify the questions leadership should be asking. But the fundraising team still needs to determine whether the interpretation makes sense in the context of real donor relationships.

Use AI as a challenger, not a cheerleader

One of the most valuable applications is asking AI to challenge a fundraising plan rather than simply validate it. You could ask:

What are the dependencies for this strategy to succeed?
Which assumptions are least supported by evidence?
Is the income target realistic given the current pipeline, conversion rates and capacity?
What would happen if one or two major opportunities were delayed?
What alternative explanations might account for current performance?
What additional evidence should we gather before investing?

This can help reveal where a strategy relies on optimism, untested assumptions or a small number of opportunities. The aim is not to allow AI to decide whether the strategy is right. It is to improve the quality of the conversation before a decision is made.

Run a premortem before the plan fails

A premortem asks the team to imagine that the strategy has failed and then work backwards to understand why. For example:

"Imagine it is two years from now and our major gifts strategy has failed to achieve its objectives. Based on the plan and anonymised data provided, identify the five most plausible reasons for failure. For each, identify the early warning signs, potential impact and preventative action."

AI can help explore risks connected to pipeline strength, donor concentration, fundraiser capacity, leadership involvement, governance, proposition development and donor experience. The team can then assess which risks are credible, agree early-warning indicators and assign preventative actions.

Used in this way, AI becomes a constructive challenger helping leaders surface potential problems while there is still time to address them.

Make Board reporting more useful

Trustees rarely need more fundraising detail. They need a clearer understanding of what matters. A useful Board report should answer six questions:

Where are we against target?
What has changed since the previous report?
Why has performance changed?
What is the likely year end position and how confident are we?
What are the most material risks and opportunities?
What decision or support is required from trustees?

AI can help turn anonymised fundraising data into a concise executive summary or one-page Board paper. It can explain specialist terms, highlight important changes and structure the narrative around risk, action and decisions.

However, the final paper still needs human review. Calculations should be checked, unsupported conclusions challenged and forecasts clearly distinguished from confirmed income. AI should never be allowed to infer donor motivation or present assumptions as facts.

Keep judgement and accountability with people

There are some important safeguards. Identifiable donor and prospect information should not be entered into public AI tools. Teams should use organisation approved systems, anonymise data and follow their internal information/governance policies.

Every analysis should distinguish between:

What the data shows.
What AI has inferred.
What remains an assumption.
What information is missing.
How confident the conclusion should be.

Ultimately, an accountable fundraising leader must own the interpretation and the recommendation. AI can make reports shorter, reveal patterns and sharpen leadership conversations. What it cannot do is understand every nuance of a donor relationship, organisational culture or strategic choice.

The opportunity is not to automate leadership. It is to give leaders better tools with which to think.

Our latest AI for Major Gifts Fundraisers toolkit #5: Strategic Leadership and Reporting includes practical prompts for analysing performance, challenging strategy, running a premortem and creating clearer leadership and Board briefings.

Click here to request your free copy of the toolkit.

AI for MG Fundraisers #4: Using AI to Strengthen Pipeline Management

sarah@robsonandmitchell.co.uk (Sarah Robson) — Fri, 11 Sep 2026 13:26:31 GMT

22/07/2626

Most major gifts pipelines do not fail because teams are not busy enough. They fail because the wrong relationships receive attention, difficult decisions are delayed, and optimistic assumptions remain unchallenged.

A prospect may have been sitting in cultivation for 18 months. A significant ask may keep moving into the next quarter. A relationship may appear active because meetings are taking place, even though there is no clear evidence of progress. The CRM records activity. It does not necessarily reveal movement.

This is where AI can be useful. Not as a replacement for the judgement of an experienced fundraiser, but as a tool for examining the pipeline more systematically and identifying where management attention is needed.

Moving from activity to decisions

The most useful application of AI in pipeline management is not producing another summary. It is turning existing information into decisions.

Which relationships need action now?
Which prospects are stuck or being over-cultivated?
Where is income at risk?
Is the forecast realistic?
Where are ownership, stewardship or next steps unclear?

These are management questions. They require evidence, judgement and, often, a willingness to challenge the story the organisation has been telling itself about the pipeline.

The AI for Major Gifts Fundraisers #4 - Pipeline Management Toolkit has been developed to help fundraising teams ask these questions more effectively. It includes practical prompts covering portfolio review, pipeline stages, forecasting, risk and short-term action planning.

For example: "Review this pipeline and identify the 10 relationships most likely to stall in the next 90 days. Explain why and recommend one management action for each."

This is more useful than asking AI to summarise the pipeline. It directs attention towards risk, evidence and action.

A large portfolio is not necessarily a strong portfolio

Many fundraisers manage more relationships than they can meaningfully progress. The result is often a crowded portfolio in which every prospect appears important, but relatively few receive the sustained attention required to move.

AI can help identify donors who have not been contacted recently, relationships that appear stuck and prospects whose potential is not matched by recent activity. It can also help examine whether a portfolio is too thin, too crowded or too dependent on a small number of opportunities.

A useful portfolio review might ask AI to group relationships into five categories: Increase investment. Maintain. Re-qualify. Pause. Deprioritise.

The categories are less important than the discipline behind them. For every recommendation, the fundraiser should ask:

What is the evidence?
What has changed?
What is the next meaningful action?
What would justify continuing to invest time?

AI can help structure this analysis. It cannot decide the value of a human relationship, understand every aspect of donor motivation or account for conversations that have never been recorded. The final judgement must remain with the fundraiser and their manager.

A stage is not evidence of progress

Pipeline stages can create a false sense of certainty. A prospect may be labelled "cultivation", but that tells us very little unless the organisation has clear definitions and exit criteria.

What has the donor expressed interest in? Has the potential value been tested? Is there a credible route to an ask? What evidence would justify moving the relationship to solicitation?

Without this clarity, prospects can remain in the same stage for months or years. Close dates move. Expected values remain unchanged. The forecast grows, but confidence does not. AI can help audit whether each prospect is in the right stage and identify what evidence is missing.

It can also examine where relationships are slowing down between qualification, cultivation and solicitation.But this may reveal a problem beyond the pipeline itself. A delayed ask might reflect fundraiser uncertainty. It might also reflect a weak proposition, slow internal decision-making, lack of CEO availability or disagreement about who owns the relationship. The symptom is a stalled prospect. The root cause may sit elsewhere in the organisation.

Forecasting should expose uncertainty, not hide it

A pipeline value is not a forecast. Adding together every potential gift creates a large number, but not necessarily a reliable view of future income. A more useful approach is to separate opportunities into: Committed. Probable. Possible. Stretch.

AI can help create a weighted forecast and test the assumptions behind each category. It can identify risk linked to delayed asks, weak next actions, donor concentration and over-optimistic close dates.

One of the strongest questions in the toolkit is: "Identify what would need to be true for each top opportunity to move from possible to probable."

That question changes the quality of the conversation. Instead of asking whether the fundraiser feels confident, it asks what evidence would increase confidence. A further meeting may not be enough.

The donor may need to confirm interest in a particular programme. The proposition may need approval. A trustee introduction may be required. The organisation may need to resolve a delivery concern before an ask can progress. A realistic forecast makes these dependencies visible.

Better pipeline meetings

Pipeline meetings often become activity-reporting sessions. Each fundraiser explains whom they met, which emails were sent and what is planned next. The meeting finishes with more information, but few decisions. AI can help redesign the agenda around:

Decisions
Blockers
Risk
Ownership
Next actions

This creates a different management conversation. Which opportunity requires leadership involvement? Which prospect should be re-qualified? Which ask date is no longer credible? What decision must be made this week to prevent further delay? The purpose of a pipeline meeting is not to prove that people are busy. It is to create movement and protect income.

The quality of the answer depends on the quality of the data

AI can analyse what is recorded. It cannot repair what is missing. Before using AI on pipeline information, teams need clear stage definitions, accurate contact histories, meaningful next actions, named relationship owners and sufficient information about gift history, capacity and relationship context.

They also need agreed rules covering privacy and sensitive donor data, with human review of every recommendation. Poor data does not always produce an obviously poor answer. It can produce a polished, confident and misleading one. That is the risk.

A management tool, not an automated fundraiser

AI will not build trust with a donor. It will not understand every political, emotional or organisational factor affecting a relationship. It will not make an uncomfortable decision on behalf of a fundraising leader. But it can make drift harder to ignore.

It can highlight inconsistencies, challenge assumptions and direct attention towards the relationships and decisions that matter most. The opportunity is not to automate major gift fundraising. It is to create better focus, more disciplined management and earlier intervention when income begins to move off course.

The Pipeline Management Toolkit is part of our AI for Major Gifts Fundraisers series. It provides practical prompts to help teams review portfolios, audit pipeline stages, test forecasts and build focused action plans—while keeping fundraiser judgement and donor context at the centre.

Get the Pipeline Management Toolkit

Ready to put this into practice? Click here to request the toolkit.

AI for Major Gifts Fundraisers #3: Don't Automate Trust

sarah@robsonandmitchell.co.uk (Sarah Robson) — Fri, 11 Sep 2026 13:23:22 GMT

13/07/2026

AI can strengthen major gifts fundraising, but it cannot replace judgement

Artificial intelligence is becoming increasingly useful in major gifts fundraising. It can help fundraisers prepare for meetings, organise prospect information, generate thoughtful questions, test objections, build cultivation plans and turn meeting notes into structured follow-up actions. Used well, it can improve preparation, consistency and speed.

But there is an important distinction. AI can support the work but it cannot own the relationship.

That matters because major gift fundraising is not simply a process of producing better briefs, asking better questions or writing better follow-up emails. It depends on trust, timing, judgement, emotional intelligence and organisational discipline.

The risk is not that fundraisers will use AI. The risk is that they will use it without enough judgement.

Where AI adds real value

AI is particularly helpful where fundraisers need structure. Before a donor meeting, it can help produce:

A concise briefing note
A suggested agenda
Questions designed to uncover motivations
Likely concerns or objections
Scenario plans for different meeting outcomes
Clear roles for the fundraiser, CEO or trustee

After the meeting, it can help turn rough notes into a usable CRM record, identify follow-up actions and clarify who needs to do what next. It can also support longer-term cultivation by helping fundraisers map stakeholders, plan engagement, identify stewardship opportunities and create realistic relationship timelines.

These are useful applications because they reduce administrative burden and create more space for strategic thinking. But only if the output is checked.

AI output is not evidence

One of the most significant risks is misplaced confidence. AI-generated content often sounds plausible, polished and authoritative. That does not mean it is accurate. In major gifts fundraising, that distinction is critical.

A donor briefing that includes an invented connection, an assumed motivation or an unsupported estimate of capacity can damage internal decision-making. In the worst cases, it can undermine trust with the donor.

Every AI-generated output should therefore be treated as a draft. Before it is used, fundraisers should:

Check factual claims against the original source
Separate known information from interpretation
Remove unsupported assumptions
Test whether the recommendation reflects the actual relationship
Review the tone and language
Apply professional and ethical judgement

The fundraiser remains accountable for the final output. Not the technology.

Data protection cannot be an afterthought

The practical convenience of AI can make it easy to paste more information into a system than is necessary. That creates risk. Fundraisers should not place sensitive personal information, private correspondence, unredacted meeting notes or unsupported internal opinions into public AI tools.

The principle should be simple: Use the minimum information necessary.

Where possible, anonymise the material. Replace names with role descriptions. Remove identifying detail. Avoid entering anything the donor would not reasonably expect to be processed in this way.

A useful test is:

Would you be comfortable explaining this use of information directly to the donor?

If the answer is no, stop.

AI cannot read the room

A donor meeting is not a scripted exercise.AI can suggest questions, talking points and possible objections. It cannot reliably interpret trust, hesitation, discomfort, power dynamics or the meaning of silence.

It cannot know whether the donor feels rushed. It cannot judge whether the meeting should move towards an ask or away from one. It cannot always see the organisational politics affecting the conversation. Those decisions still depend on the fundraiser's judgement.

The strongest use of AI is therefore not to script the meeting, but to improve the quality of preparation.

Use it to explore scenarios. Use it to challenge assumptions. Use it to identify gaps.

Then put the prompt away and listen.

Leadership preparation needs clarity, not volume

AI can also be valuable when preparing a CEO or trustee for a donor conversation. But senior leaders rarely need a long briefing pack, they need clarity. A strong preparation note should tell them:

Why the meeting matters
What the donor may care about
What their specific role is
What questions they should ask
What to avoid
What the desired next step is

The aim is not to give the CEO or trustee a script. It is to help them contribute with confidence and discipline. That is particularly important in organisations where leadership involvement is inconsistent, unclear or overly dependent on the fundraiser.

The most important moment comes after the meeting

Many donor relationships do not stall because the meeting went badly. They stall because nothing happens afterwards. The follow-up is vague. Ownership is unclear. The next move is not agreed. A leadership decision is delayed. The CRM is updated, but the relationship does not move. AI can help impose structure here, but the organisation must still make decisions. A useful post-meeting record should distinguish between:

What the donor actually said
What the fundraiser interpreted
What remains uncertain
What the agreed next step is
Who owns it
When it will happen
What leadership input is required

This is where better preparation becomes better performance. Without ownership and action, even a well-run meeting becomes another entry in the database.

The real skill is not prompting

There is growing interest in prompt libraries, templates and AI tools for fundraisers. They are useful. But they are not the main capability organisations need to build. The real skill is knowing:

What information to provide
What not to provide
What output to trust
What to challenge
What to ignore
When human judgement must override the tool

AI can improve the mechanics of major gifts fundraising. It cannot replace the relational, ethical and organisational conditions that allow major gifts to happen. Used well, it creates better preparation, better questions and more disciplined follow-up. Used poorly, it creates confident-looking material without the judgement required to use it safely.

The principle is straightforward:

Use AI to support better fundraising, not to automate trust.

Our AI for Major Gifts Fundraisers toolkit includes practical prompts and guidance for donor meetings, cultivation, briefing, scenario planning, follow-up and leadership preparation.

AI for MG Fundraisers #2: Why AI is Transforming Prospect Research

sarah@robsonandmitchell.co.uk (Sarah Robson) — Fri, 11 Sep 2026 13:05:42 GMT

07/07/2026

Prospect research has always been about helping fundraisers answer one simple question:

Who should we invest our time in?

The challenge is that answering this well can take hours. Reviewing company websites, annual reports, biographies, LinkedIn profiles, trustee registers, news articles and previous interactions is essential, but it is also time-consuming. For many fundraising teams operating with reduced capacity, that means valuable time is spent gathering information rather than using it.

This is where AI can make a genuine difference.

AI doesn't replace prospect research, it accelerates it

One of the biggest misconceptions is that AI replaces the role of the prospect researcher. In reality, it removes much of the repetitive work that sits around research.

AI can quickly summarise publicly available information, analyse annual reports, identify governance roles, prepare concise donor briefings and organise research into structured profiles. Tasks that previously took hours can often be completed in minutes. That doesn't remove the need for a skilled prospect researcher. It simply gives them more time to focus on the work that creates the greatest value.

From information gathering to insight generation

The real expertise of a prospect researcher has never been finding information. It is interpreting what that information means. Questions such as:

Does this person have genuine affinity with our cause?
What evidence suggests philanthropic intent?
Are there existing relationships we haven't recognised?
Is now the right time to engage?
What assumptions still need to be tested?

These require judgement, curiosity and experience. AI can organise the evidence. Humans decide what it means.

Better qualification leads to better fundraising

Most fundraising teams have far more prospects than they have capacity to manage. Qualification is therefore one of the highest-value activities in major gifts fundraising. Rather than asking AI, "Is this a good prospect?", a more useful approach is to ask:

What evidence supports financial capacity?
What indicators suggest genuine affinity?
What information is missing?
What assumptions am I making?
What should I verify before investing significant time?

These questions encourage critical thinking rather than blind acceptance of AI-generated answers.

AI should challenge your thinking, not replace it

One of the ways I use AI most often is not to generate answers, but to challenge my own assumptions. For example, I'll ask AI to distinguish between verified facts and assumptions, identify weaknesses in my assessment or suggest alternative interpretations of the evidence. This often surfaces questions I hadn't considered. Used in this way, AI becomes less like a search engine and more like a research assistant or critical friend.

The future of prospect research

As AI continues to evolve, I believe the role of prospect researchers will become even more valuable. Less time will be spent searching for information. More time will be spent interpreting evidence, identifying opportunities, advising fundraisers and supporting strategic decision-making. Technology will accelerate the process. Human judgement will continue to determine the outcome.

Final thoughts

The goal of prospect research has never been to collect the most information. It has always been to provide fundraisers with the confidence to invest their time in the right relationships. AI helps us do that more efficiently. Not by replacing expertise, but by giving us more time to apply it.

AI for Major Gifts Fundraisers is a series exploring practical ways AI can support fundraising without replacing the skills that matter most. This article accompanies my Prospect Research & Qualification toolkit, which includes practical prompts and frameworks for fundraisers and prospect researchers.

I'd love to hear how you're using AI in prospect research. Has it changed the way you prepare for donor conversations or qualify prospects?

AI for MG Fundraisers #1: Why Every MG Fundraiser Should Learn AI

sarah@robsonandmitchell.co.uk (Sarah Robson) — Fri, 11 Sep 2026 13:00:08 GMT

30/06/26

AI has become one of the most talked-about developments in fundraising. Yet much of the conversation has focused on whether AI will replace fundraisers. I don't think that's the right question.

The real question is whether fundraisers who learn to use AI will outperform those who don't.

Fundraising has changed

Across the charity sector, teams have become leaner. Administrative support has reduced, expectations have increased and fundraisers are expected to wear more hats than ever before.

A major gifts fundraiser today might spend their week researching prospects, preparing donor meetings, writing proposals, updating CRM systems, analysing portfolios, producing reports for trustees and, somewhere amongst all of that, building meaningful relationships with donors. It's little surprise that many feel stretched.

The challenge isn't a lack of commitment. It's a lack of capacity.

AI gives us something we desperately need: TIME

The biggest opportunity isn't writing donor emails. It's removing the repetitive work that stops fundraisers spending time with donors. AI can dramatically reduce the time needed to:

research prospects
prepare donor briefings
draft proposals
summarise meetings
review portfolios
analyse fundraising pipelines
produce reports

None of these activities disappear. They simply become faster. That gives fundraisers more time to do the work that only humans can do.

The human skills become even more valuable

No AI tool can build trust. It can't understand the emotion behind a transformational gift. It can't navigate complex organisational politics. It can't replace empathy, judgement or ethical decision-making. As AI becomes more capable, these human skills become even more valuable.

The biggest shift isn't writing, it's thinking

Many people use AI as a better search engine. Others use it as a copywriter. I've found the greatest value comes from using AI as a thinking partner. Some of the prompts I use most often ask AI to:

challenge my assumptions
identify risks I've overlooked
critique fundraising strategies
think like a major donor
suggest alternative approaches
highlight weaknesses in proposals

The result isn't that AI makes the decisions. It helps me make better ones.

The future belongs to fundraisers who combine both

Fundraisers who embrace AI won't succeed because they know the most prompts. They'll succeed because they use technology to remove low-value work, freeing up more time for meaningful conversations, stronger relationships and better strategic thinking.

In a sector facing increasing pressure to achieve more with fewer resources, that's an opportunity we can't afford to ignore.

I've put together practical guides of AI prompts for major gifts fundraisers, based on the ways I use AI in my own work. If you'd find it useful, I'd be delighted to share a copy.