Grantmaking Strategies Evolve Amidst Volatility and AI Rise

Yesterday, I shared a thought: Hard work is not the problem. Access to the right knowledge is. But the real question is what can we do about it? I’ve been thinking about practical ways this gap can actually be addressed, especially at the grassroots level. Here are a few ideas that stand out to me: 1. Use AI to localize and simplify knowledge One powerful thing about AI is its ability to communicate in different languages and formats. Imagine simple, relatable content explaining: - what investment really means - how to manage money better - how climate affects farming decisions …translated into Yoruba, Igbo, Hausa, and delivered in a way that feels familiar. Not complex. Not technical. Just clear and usable. Because the goal is not just to inform it’s to make people understand. 2. Grassroots financial literacy programs We need more community-based learning spaces where people are taught: - how to save - how to invest - how to grow income sustainably Not in theory, but in ways that connect to their daily realities. 3. Equipping farmers with practical climate knowledge Farmers don’t just need tools they need information they can act on. - When to plant - What changing weather patterns mean - How to adapt using simple technology Bridging this gap can directly improve productivity and stability. But here’s the most important thing: Solutions don’t fail because they don’t exist. They fail because they are not accessible. If we can focus on making knowledge: simple, local, and practical, we can start turning effort into real, sustainable progress. Because people are already doing the work. They just need the right understanding to make it count. What do you think which of these solutions is most realistic to implement first?

Trump is putting the fox in the henhouse, filling his Council of Advisors on Science and Technology with the same tech executives getting rich off the AI boom. The people profiting from rapid AI expansion are now being positioned as trusted voices on the public policy that will govern it. This is oligarchic capture dressed up as expertise. And it is happening alongside a broader weakening of independent science and public-interest protections. When an administration is willing to sideline scientific integrity and roll back environmental guardrails, it isn’t hard to see what follows: more power for corporate actors, less accountability to the public, and more pressure on communities to absorb the costs. And let’s be clear, these are not abstract costs- it’s water use, energy burden, and the steady erosion of democratic control over what gets built, where, and for whom. That is why the push for a moratorium on new data centers matters. This is not a fringe position, despite what Big Tech and monied actors would have us believe.  It is a democratic response to an industry trying to lock in infrastructure with enormous social and environmental consequences before the public has any real say. The pressure now coming from Bernie Sanders and AOC did not appear out of thin air. It is the result of ordinary people refusing to let their communities be sacrificed for AI profits. Philanthropy, you’re up! Every day I hear from or about grassroots groups, nascent coalitions, and frontline leaders navigating these fights with little to no investments for the organizing, litigation, and capacity-building work that is required. The equation is so lopsided. If funders are willing to invest in AI uptake and adoption, I hope that the sector will soon be equally up for investing in the communities resisting its harms and defending the conditions that democracy actually depends on. 

This week I'm in Washington for the International Monetary Fund and The World Bank Group Spring Meetings, representing CAGS as a CSO Parliamentarian alongside my colleague Michael A. Trudeau, who is joining both in his capacity as a CAGS board member and as Principal of Valexis Global, a private sector partner of CAGS. The work we're doing at the intersection of AI, data science, and grand strategy has direct application to some of the conversations happening in these rooms this week, and that's exactly why being here matters. The question of how multilateral institutions can use better data and better analytical tools to know whether development investments are actually working is one CAGS is built to help answer. If the intersection of AI, development finance, and national strategy is your space, we'd love to find time to connect.

ENDS. WAYS. MEANS. How do we know when development finance is actually working? That is the question we're bringing into the International Monetary Fund and The World Bank Group Spring Meetings this week, participating as a CSO Parliamentarian. Walking into rooms with finance ministers, central bank governors, and development practitioners is a reminder of how much genuine effort is directed at the world's hardest problems. The agenda, spanning debt sustainability, digital agriculture, energy access, and post-conflict reconstruction, reflects the ambition of what this community is trying to do. And yet the structural challenge persists. The instruments of development finance are designed to disburse capital, but the feedback loops that would tell you whether that capital is producing durable outcomes remain underdeveloped. The World Bank's IDA21 replenishment came in at a record $100 billion. Knowing whether it's working is just as important as raising it. AI and data science are helping us ask deeper, richer questions here. Researchers have mapped AI's potential across the UN's Sustainable Development Goals and demonstrated that machine learning applied to satellite imagery and mobile data can generate high-resolution poverty estimates at a fraction of traditional cost. Genuinely useful capabilities. But the honest version includes a hard constraint: AI is only as good as the data it runs on. In fragile states and post-conflict environments, that data is often incomplete, inconsistent, or absent. Garbage in, garbage out is not a technical footnote. It is the central challenge, and solving it is as much a governance problem as a technical one. That is where grand strategy thinking has something to contribute, and where the development and national security communities rarely build frameworks together. At CAGS we're supporting those efforts and looking forward to finding new friends this week to help answer the question we started with. ENDS. WAYS. MEANS. is a weekly series from the Center for the Application of Grand Strategy (CAGS). Find out more at www.cagsai.org #GrandStrategy #HumanSecurity #DevelopmentFinance #Statecraft #AIPolicy #ArtificialIntelligence #DataScience #GeoPolitics #StrategicStudies #CAGS #PolicyAnalysis #NationalSecurityPolicy #SpringMeetings2026

The Shift from Data Strategy to Decision Strategy: When Insight Outpaces Human Capacity We’ve spent the last decade trying to solve for a lack of insight. Now a different problem is emerging. What happens when an organization generates more insight than it can handle and more importantly, more complexity than it can operationalize? At first, it feels like progress. More data. More models. More precision. But beneath the surface, the operating model starts to strain. Not because of volume alone but because the complexity of the insight exceeds the organization’s ability to absorb it. You begin to see the symptoms: • Decisions slow down despite better information • Leaders hesitate because outputs are harder to interpret and explain • Teams quietly simplify or ignore insights to make them usable • A small group of experts becomes the bottleneck between models and action And over time, a dangerous gap forms: The organization knows more than it can act on. In wealth management, this goes beyond productivity. It becomes a governance issue. If an insight cannot be clearly translated into a client-specific recommendation and cannot be explained in terms of suitability, risk, and client outcome then its sophistication is not an advantage, it is a liability. This is where many AI initiatives will struggle. Not because the technology isn’t powerful but because the organization isn’t designed to operationalize complexity at scale. The next phase of transformation will not be about generating more insight. It will be about: • Constraining insight to what is decision-relevant • Translating complexity into clear, accountable actions • Embedding intelligence directly into advisor workflows • Ensuring every recommendation can be explained, defended, and evidenced Because ultimately: The firms that win will not be those that know the most They will be the ones that can act with clarity on what they alreaady have #AI #WealthManagement #DataStrategy #Leadership #DigitalTransformation #RegTech #AgenticAI https://lnkd.in/gZmUxAnt

We have just published our 2025 Annual Report, capturing our journey through a year of major milestones and strategic transition in a challenging landscape. In 2025, we successfully assessed 2,000 of the world’s most influential companies in a single cycle, with results launched in early 2026. Alongside this, we continued to advance our impact by supporting climate transition planning, driving collective action on ethical AI, and strengthening global expectations on corporate accountability. This progress came in an exceptionally challenging context, marked by major disruptions in the global funding landscape and reduced development and philanthropic support. In response, we adapted our strategy, moving towards an Integrated Transition Planning approach across climate, nature, social issues, and AI, while restructuring internally to become more agile and resilient. Download the full report here: https://lnkd.in/eCd_v9WY

Yesterday I had the opportunity to attend the 2026 Sentinel Business Forum in Raleigh, NC, as a guest of Brian Miller—appreciate the invitation and the insight. The forum brought together strong perspectives on economic trends, legislative changes, and risk management shaping today’s business environment. What stood out most to me was a consistent theme across every presenter: Artificial Intelligence is no longer a future concept—it’s a present-day business reality. From accounting and legal considerations to workforce strategy and risk mitigation, AI is actively reshaping how companies operate, make decisions, and compete. The message was clear: those who embrace it thoughtfully will have a distinct advantage. In commercial real estate, we’re already seeing this shift—better data, faster analysis, smarter site selection, and more informed investment decisions. The pace is only accelerating. It’s an exciting time, but also one that requires intention, learning, and adaptation. Grateful to be in rooms like this where forward-thinking conversations are happening. #CommercialRealEstate #ArtificialIntelligence #BusinessStrategy #Leadership #EconomicTrends

Most organizations in the development sector are doing extraordinary work. But here's a question, What if the funding gap isn't about impact - but about how that impact is communicated, documented, and presented to the world? I've been sitting with three questions lately: 1. What if AI could read your organization's fundability the way a funder does and tell you what they see before you even apply? Not a checklist. Not a consultant. A mirror that shows you the gaps you can't see yourself. 2. What if compliance documentation- the thing that kills most small CSOs in due diligence, could be generated in minutes instead of months? Policies, frameworks, theories of change. The paperwork that separates funded organizations from unfunded ones. What if that barrier was no longer about capacity? 3. What if your field data- messy, scattered, collected on paper could be turned into a compelling impact narrative that funders actually read? I don't have all the answers. But I've been building something that starts to explore them. If any of these questions resonate with your work — I'd love to hear your thoughts in the comments. What's the biggest operational challenge your organization faces that you wish technology could solve? Try out getfundri.com - In under 60 seconds, Fundri analyzes your organization across 6 key parameters and gives you: ✦ A fundability score showing where you stand ✦ A clear overall assessment of your current position ✦ The top funding gaps holding you back ✦ Recommended funders matched to your profile ✦ Quick wins you can act on this week No consultants. No waiting. Just clear, actionable intelligence — free We want to continue to explore how best we can leverage these AI tools and make improvements along the way. #development #NGOs #CSOs #globalmajority #Aid

AI tools offer nonprofits practical ways to save time, enhance communication, and extend mission impact—when implemented responsibly and with clear guardrails. Link to full article in comments.

🔔 (new project!) What does it mean for deliberation to be really, really excellent? How well are we able to achieve those ideals currently, and where do we fall short? What work is needed to fill those gaps? And how should we prioritise that work? To answer those questions, we've built a *Democratic Capabilities Gap Map* — a handcrafted database of high-level desiderata, low-level goals, capabilities, open research problems, related work, and "product" gaps for increasing our capacity to do deliberation well. There is an increasing amount of enthusiasm, effort, and funding pouring in to the deliberative democracy ecosystem, and we hope the gap map can help ensure those resources accumulate into durable democratic infrastructure — deliberative processes that are increasingly compelling, democratic, widely available, and able to handle the many high stakes governance decisions of the coming years. ➞ democracy build dot org (link in the first comment) This is a living resource; please do submit contributions and we'll weave them in! Many contributors acknowledged on the website, but in particular huge credit due to Kyle Redman, Quan Ze Chen, Eloïse Gabadou Santiago, Oliver Smith, Flynn Devine and Aviv Ovadya at the AI & Democracy Foundation for making this happen.