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 If opportunities are out of date, stages mean different things to different people or expected income dates are repeatedly moved forward, the analysis may sound convincing while being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:

1. Where are we against target?

2. What has changed since the previous report?

3. Why has performance changed?

4. What is the likely year end position and how confident are we?

5. What are the most material risks and opportunities?

6. 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.