Funding AI When It's Not the "Top Priority" – A Māori Reality
Introduction
While completing my interviews for my research on ethical AI and Indigenous storytelling, speaking with both Indigenous and non-Indigenous tech leaders, one thing keeps coming through clearly and consistently.
AI is not at the top of the priority list.
And that makes complete sense.
Our communities are already dealing with immediate, pressing challenges: economic pressure, housing and health inequities, education gaps, and the urgent need to support whānau into stable, meaningful work and training. (MBIE, 2024a; MBIE, 2024b) Against that backdrop, "let's fund an AI project" can sound like a luxury, or even a distraction from what really matters.
But here's the tension I've been sitting with throughout my research:
AI is reshaping the world whether we engage with it or not. And if Indigenous communities aren't part of designing and leading that conversation, we risk being left further behind—especially our rangatahi. (Matihiko, 2025; Native Digital, 2024).
So the real question becomes:
How do we design and fund Indigenous-led AI projects that support core community priorities rather than compete with them?
The Reality on the Ground
Let me be clear about what I'm observing in Aotearoa:
Māori are facing a complex employment landscape. The Māori Employment Action Plan identifies persistent employment inequities and ongoing barriers to high-value work (MBIE, 2024b; Toi Tū Te Waiora, 2024). At the same time, Māori representation in the tech workforce sits around 4–5%, despite Māori making up 17% of the population (Native Digital, 2024).
That's a risk, but it's also a massive opportunity if we act strategically now.
However, when iwi and hapū are stretched thin, managing immediate crises, supporting families through economic hardship, addressing health disparities, ensuring young people stay engaged in education, and asking them to care about AI can feel like asking them to solve tomorrow's problem while today is still burning.
This is a fair position. And any project that doesn't respect this reality deserves to fail.
Three Principles for Ethical AI Funding in Indigenous Communities
From my research and practice, a few things are becoming clear about how to fund and support Indigenous-led AI projects without creating new burdens:
1. AI Projects Must Be Tied to Real-World Outcomes for Rangatahi
If an AI or digital project doesn't create clearer pathways into skills, jobs, or enterprise for our young people, it's fair for iwi and hapū to ask: "Why bother?"
Done well, Indigenous-led AI projects can sit alongside Māori employment and digital strategies, growing Māori participation in the tech sector rather than just building tools for others to use. (MBIE, 2024c; MBIE, 2024d; Matihiko, 2025).
This means:
Creating internships and apprenticeships in digital and AI roles for rangatahi
Building local capability so knowledge and power stay within the community
Connecting project outcomes to economic resilience and whānau wellbeing
Measuring success not just by technical metrics, but by how many rangatahi have gained skills, confidence, and opportunity
Without this link to real opportunity, AI projects can feel extractive, another thing outsiders are asking the community to participate in, without a clear return.
2. Funding Needs to Recognise the Reality on the Ground
Funders and tech partners often come with timelines, deliverables, and expectations shaped by corporate or academic logic. But they can't expect AI to be "the" priority for communities facing immediate social and economic challenges.
Support has to look like:
Resourcing local capability. Hire rangatahi, kaiako, and kaimahi from the community. Pay them fairly. Invest in their development.
Aligning with existing kaupapa. Don't ask communities to create new priorities around AI. Show how AI can serve employment plans, digital inclusion strategies, and education pathways already underway. (MBIE, 2024a; MBIE, 2024b)
Accepting that relationship-building takes time. Co-design, consultation, and community validation can't be rushed to fit a funder's reporting cycle. Communities need time to build trust, and funders need to respect that.
Being flexible. If the community's needs shift or if they decide the project isn't serving their kaupapa, good funders adapt, not push harder.
This is harder than a standard funding model, but it's the only way to build projects that communities actually want to maintain and grow in the long term.
3. AI Should Be Framed as Infrastructure for the Future, Not a Shiny Add-On
Just like we once had to invest in basic internet access and devices, we now need to equip our young people to live and lead in an AI-shaped world—whether we like it or not.
The reality is: AI is not going away. It's embedding itself into hiring, education, healthcare, language systems, and cultural tools. If Māori communities ignore it, we risk rangatahi being passive users at the edge of the system, rather than designers, leaders, and decision-makers inside it. (Radio New Zealand, 2025; MBIE, 2024d).
But framing it as "infrastructure" is different from framing it as "an exciting new opportunity." It's saying:
"We need to prepare our young people for a world where AI is present. We need to make sure they're not left behind. And we need to do this in a way that protects our data, our stories, and our rights."
This is protective work, not adventurous work. And that changes the conversation.
What This Looks Like in Practice
So what does responsible, community-first AI funding actually look like? Here are some real examples of what can work:
Model 1: Iwi-Led Tech Enterprises
Some iwi are building their own digital or AI capabilities, with external funding supporting infrastructure and training rather than dictating the agenda. The power and decisions stay with the iwi. (Native Digital, 2024).
Model 2: Co-Designed Projects with Clear Community Benefit
When external funders or researchers want to partner, they do so on the community's terms with profit-sharing, data control, and genuine co-leadership. Examples include Te Hiku Media's approach to language technology.
Model 3: Youth Pathways First
Rather than building an AI tool and hoping rangatahi will learn from it, start by identifying what skills and opportunities young people actually need, then design technology as one support tool among many.
A Reframed Question for Funders
For iwi, hapū and whānau, it's absolutely right that economic resilience, health, housing, and immediate whānau wellbeing come first.
The role of AI projects and the funding behind them should be to:
Support those priorities, not compete with them
Create new pathways, especially for rangatahi, into skilled, valued work
Protect data, stories, and language while building skills and confidence
Build local leadership and control, not dependence
So when we ask communities to consider AI and digital innovation, maybe the better kōrero is:
"How can this help your people, your kaupapa, and your rangatahi in 5–10 years… without taking away from what you're already carrying today?"
If you're a funder, tech leader, or educator, that's the kind of partnership conversation that respects where iwi and hapū are really at. It acknowledges their reality, respects their priorities, and frames technology as support not salvation.
Final Reflection
There's a tendency in the tech and innovation world to move fast, build first, and ask questions later. But for Indigenous communities, the pace needs to be different.
We move at the speed of relationship. We decide based on community wellbeing, not quarterly metrics. And we say "no" when something doesn't serve our kaupapa even if it's exciting, well-funded, or "the future."
That's not resistance to innovation. That's wisdom.
Suppose we can bring that same wisdom to how we fund and lead AI projects in our communities. In that case, we might actually build something that serves rangatahi, protects our taonga, and strengthens whānau rather than adding to the burden leaders are already carrying.
References
Ministry of Business, Innovation and Employment. (2024a). Māori employment action plan. https://www.mbie.govt.nz/business-and-employment/employment-and-skills/employment-strategy/maori-employment-action-plan
Ministry of Business, Innovation and Employment. (2024b). Te mahere whai mahi Māori – Māori employment action plan – Summary (English). https://www.mbie.govt.nz/dmsdocument/18741-te-mahere-whai-mahi-maori-maori-employment-action-plan-summary-english
Matihiko. (2025). Māori in digitech futures report. https://matihiko.nz/wp-content/uploads/2025/08/Maori-in-DigiTech-Futures-Report.pdf
Ministry of Business, Innovation and Employment. (2024c). Focus area: Enriching Māori inclusion and enterprise. https://www.mbie.govt.nz/business-and-employment/economic-growth/digital-policy/digital-technologies-industry-transformation-plan-2/focus-area-enriching-maori-inclusion-and-enterprise
Ministry of Business, Innovation and Employment. (2024d). Digital technologies industry transformation plan. https://www.mbie.govt.nz/assets/digital-technologies-industry-transformation-plan.pdf
Ministry of Business, Innovation and Employment. (2024e). Strategic framework. https://www.mbie.govt.nz/business-and-employment/economic-growth/digital-policy/digital-technologies-industry-transformation-plan-2/strategic-framework
Native Digital. (2024). Toi Hangarau 2024: Māori tech sector reaches new milestone. https://www.nativedigital.co.nz/blog/article/toi-hangarau-2024-maori-tech-sector-reaches-n
Radio New Zealand. (2025, August 12). AI is peeling back the layers of 'low-value' work. https://www.rnz.co.nz/news/on-the-inside/569813/ai-is-peeling-back-the-layers-of-low-value-work-nz-may-be-well-placed-to-adapt
Toi Tū Te Waiora. (2024). Te mahere whai mahi Māori. https://toitutewaiora.nz/wp-content/uploads/2023/07/32-v2.0-Te-Mahere-Whai-Mahi-Maori.pdf