Like many charities, Prostate Cancer UK has ambitious plans for growth.
The challenge is one that will be familiar to every finance leader. Demand for services continues to increase, expectations continue to rise and financial pressures show little sign of easing. Yet, simply adding more people is rarely a sustainable answer.
Futureproofing finance is no longer just about managing budgets and controls. It is increasingly about helping organisations find ways to scale impact without increasing costs at the same rate.
For me, that is where the conversation around AI becomes interesting.
Too often AI is presented as a transformational technology capable of changing everything overnight. In reality, the most valuable opportunities are often much smaller and much more practical.
The question we have been asking at Prostate Cancer UK is not: “How can AI replace people?”
It is: “How can AI help us create capacity so we can continue to grow without continually increasing overheads?”
That is a very different conversation.
Moving beyond individual productivity
Like many organisations, our AI journey started with individual productivity.
We recognised early that colleagues were already experimenting with AI to draft content, analyse information and reduce time spent on repetitive tasks. Rather than trying to prevent adoption, we focused on governing it properly through training, approved tooling and clear guidance.
Today, 92% of our workforce has access to a secure AI licence and, in the last month alone, 94.4% of licensed users actively used it. The significance of those figures is not the technology itself. It is that AI has moved from individual experimentation to an accepted and governed way of working across the organisation.
That was an important first step, but it is not where the long-term value sits.
The bigger opportunity is using AI to support teams and organisational workflows, helping charities increase capacity without needing equivalent growth in headcount.
Start with the problem, not the technology
One of the biggest mistakes organisations make with AI is becoming fascinated by the technology before understanding the problem they are trying to solve.
We have deliberately taken the opposite approach.
One example is our Finance AI Triage proof of concept. Working alongside Toca.io and supported by our internal development team, we are exploring whether AI can help classify and route incoming finance enquiries within a busy finance mailbox that processes hundreds of emails each day across multiple routing categories.
Another example is DAREN AI, where we are introducing AI capabilities into our existing DAREN CRM platform, developed in partnership with Fingertip Media and used by our specialist nurses and support teams supporting men affected by prostate cancer. Rather than replacing existing processes, the objective is to provide trusted, current and sourced information when it is needed, helping colleagues spend less time searching and more time supporting service users.
Although these initiatives operate in different parts of the organisation, they are both trying to answer the same question: How do we create more capacity without continually increasing cost?
That question feels increasingly relevant as charities seek to grow services while maintaining financial discipline.
The knowledge is the asset
One lesson emerged very quickly: the AI itself was rarely the problem.
Whenever we encountered issues, they were usually linked to the quality, ownership or currency of the information underneath it. Out-of-date content, duplicated documents, broken links and unclear ownership presented greater risks than the technology itself.
Whether the risk is a misclassified finance query or inaccurate information being surfaced to a colleague, organisations remain accountable for the outcomes. That makes governance, oversight and auditability as important as the technology itself.
As a result, we increasingly view the knowledge base as the strategic asset rather than the model. Models will evolve. Platforms will change.
However, a trusted, governed and centrally-managed body of organisational knowledge retains its value regardless of which technology sits above it.
That insight has significantly changed how we think about investment.
The long-term value is not simply the AI capability. It is creating a foundation of trusted information that can support future services, future processes and future technologies.
For finance leaders, that reframes the business case. The asset that compounds over time is not the model itself. It is the knowledge, governance and organisational capability that sits behind it.
The technology was the easy part
Perhaps the biggest surprise from our pilots was that the technology itself was often the easiest part.
With a strong internal technology team and experienced delivery partners, moving from concept to working solution happened relatively quickly. The more challenging conversations were about ownership, assurance and governance.
What happens when the AI is wrong? How would we know? Who is accountable? What controls exist to prevent issues becoming real-world problems?
These questions ultimately proved far more important than model selection or technical architecture.
The experience reinforced an important lesson.
AI maturity is not reached when a large language model produces the right answer. AI maturity is reached when an organisation understands how to govern the wrong answer.
For finance leaders, this should sound familiar. Financial controls are not designed around everything working perfectly. They exist because mistakes happen.
AI is no different.
Trust before scale
This lesson became particularly important in DAREN AI.
Success is not defined by whether the technology can generate a plausible response.
Success is defined by whether our nurses trust the information being presented and feel confident using it as part of their day-to-day work.
That is why we have deliberately focused on governance, guardrails and measurement before deployment.
Rather than claiming savings upfront, we are building the evidence first.
We are measuring how processes operate today, how AI affects drafting and review activities, and whether it genuinely creates capacity while maintaining quality, confidence and appropriate oversight. The aim is to make decisions based on evidence rather than enthusiasm.
For me, that discipline is important.
It is easy to become excited by what AI might achieve.
It is much harder, and ultimately much more valuable, to demonstrate what it has actually achieved.
Scaling impact, not overhead
The biggest lesson from our AI journey so far is that futureproofing finance is not primarily a technology challenge. It is a capacity challenge.
The opportunity is to remove friction, help teams focus on higher-value activities and create room for growth without continually increasing operational cost.
Some AI initiatives will succeed. Some will evolve. Others may never progress beyond experimentation.
That is exactly what a proof of concept is designed to determine.
Ultimately, success should not be measured by how much AI an organisation deploys.
It should be measured by whether it enables the organisation to deliver more impact, more sustainably, with the resources available.
And if AI can help charities achieve that, then the opportunity is not simply technological – it is strategic.
Gerardo Del Guercio is head of technology & solutions at Prostate Cancer UK
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