This post is part of our “Smarter Fundraising” series based on a fireside chat featuring Katie Dudley (Partnerships & Business Development, Dataro) and Jono Walker (CTO, Walkerscott & Head of Product, Klevr Fundraising). Below you’ll find the blog summary and highlights – scroll down to watch the short video clip.
If you’re a not-for-profit executive, chances are you’re already mapping out your 2026 and next three-to-five year strategy. Donor expectations and fundraising technology have already shifted dramatically in the past year, with AI, data-driven personalisation, and dedicated all-in-one platforms moving from ideas into real tools.
Looking ahead to 2026 and beyond, the pace of change isn’t likely to slow down. The real question is whether your organisation chooses to factor these shifts into its strategy now, or risks falling further behind while others move ahead.
This isn’t about chasing buzzwords. It’s about recognising the tech challenges that could stall growth, and the opportunities that can set you apart if you prepare early.
Challenge 1: Donor expectations for hyper-personalisation
Opportunity: Make every interaction feel donor-first
Donors are already used to Netflix-style experiences: the more they engage, the smarter the recommendations get. Increasingly, they’ll expect the same from you.
If your communications still rely on basic segmentation – “monthly donors,” “mid-tier givers” – you risk higher churn. Donor retention rates in many markets already sit around 40–45%. Without personalisation at scale, you’re leaving lifetime value on the table.
The key is to go beyond transactions. Preferences, engagement behaviours, and interaction history all provide signals that can make donor journeys feel relevant and personal. AI tools can help interpret these signals and surface likely upgrades, lapses, or new major-gift prospects, but only if that data is being tracked and maintained.
Where to start now: Audit your donor journeys.
- Are you still sending every supporter the same newsletter?
- Do you capture channel preferences, event attendance, or areas of interest?
- Could you pilot predictive models that use these signals to create precision audiences?
This builds on our earlier discussion of fit-for-purpose fundraising technology, the right CRM and AI tools are what make personalisation realistic at scale.
Purpose-built fundraising AI, like Dataro, already helps organisations analyse donor behaviours and build tailored outreach strategies from this kind of enriched data.
Challenge 2: Fragmented data and siloed systems
Opportunity: Build a unified data ecosystem
Fundraising, finance, marketing, impact reporting – too often, each team has its own silo. That makes it difficult to see the full donor picture, let alone act on it.
Nonprofits that thrive in the coming years are likely to be the ones that connect their systems into a single ecosystem. The payoff? Faster insights, reduced compliance risks, and teams working from the same donor truth.
Where to start now: Prioritise integrations when renewing tech.
If your CRM doesn’t talk to your finance / ERP or marketing systems, build that requirement into your next RFP.
Consider choosing an all-in-one donor management platform like Klevr Fundraising, which integrates tightly with Microsoft Dynamics 365 and other core systems like FMIS.
Challenge 3: Data quality and trust
Opportunity: Clean and enrich your data in preparation for AI
AI is only as good as the data it learns from. Many nonprofits still live with legacy CRMs riddled with duplicates, patchy contact info, and inconsistent entry. That’s not just an inconvenience, it undermines donor trust.
Equally important is capturing the right kinds of information: donor preferences, engagement behaviour, event attendance, and communication history. This is the context that turns raw transactions into meaningful donor signals. Without it, even the smartest predictive model won’t know how to personalise effectively.
If your data isn’t accurate, connected, and enriched with these insights, your AI investments may misfire and your personalisation efforts could feel irrelevant.
Where to start now: Run a data hygiene project this year.
- Audit your donor and donations data with fit-for-purpose reporting.
- Deduplicate donor records and consolidate recurring gift histories.
- Start tracking donor preferences and engagement consistently (e.g. channels, topics, event participation).
- Continue to use data audit reports to highlight and fix data inconsistencies.
- Consider using AI to provide recommendations to fix data inconsistencies.
Challenge 4: The rise of AI agents
Opportunity: Treat AI as a new team member
Dashboards are already common. The next step is AI agents, virtual assistants embedded in your systems. Think of them as colleagues who never tire; researching prospects, flagging opportunities, optimising campaigns across channels.
Organisations that haven’t prepared their data and culture for this shift may find it harder to catch up.
Where to start now: Encourage your team to test safe AI features in existing platforms.
For example, let AI draft variations of your next appeal email, then have staff refine the tone. Build confidence through small experiments, not big-bang rollouts.
Challenge 5: Organisational culture and change management
Opportunity: Foster a culture of experimentation
Technology often moves faster than people. Many fundraisers still rely on “gut feel” or “what worked ten years ago.” AI flips that on its head by helping you act on real-time donor signals.
The gap between organisations that experiment and those that cling to old ways is already visible, and it’s only likely to grow.
Where to start now: Build “test and learn” into your strategy.
- Set aside a small portion of your budget for pilots.
- Try A/B testing subject lines, or experimenting with SMS vs. email follow-ups.
- Measure results, celebrate wins, and normalise iteration.
The Bottom Line: Don’t wait for 2026 to arrive
Writing your next strategic plan means making bets on the future. The nonprofits that thrive are likely to be those that:
- Personalise at scale, rather than segment crudely
- Connect their systems into unified data ecosystems
- Invest in clean, trustworthy, and enriched donor data
- Treat AI agents as part of their team
- Build a culture that’s ready to experiment
The gap between nonprofits that act on these trends and those that don’t is already starting to show. The good news? You don’t need to solve it all today. But you do need to start.