When we talk about AI, we’re looking beyond large language models.

There’s a growing, rapidly evolving ecosystem of tools that uncover patterns in complex data and rearrange them into new information that behaves similarly.

Put like that, it sounds painfully dry.

But when you see it in action, revealing hidden relations or structures you didn’t even know you were part of, it feels a lot like magic.

Humans are notoriously bad at dealing with complexity, unknown unknowns and predicting emergence. AI can churn through vast, messy datasets and uncover hidden patterns.

Recent research shows AI algorithms distilling hundreds of variables in a complex system into a few key factors or simple rules.

In systemic investing, this can help us to identify subtle systemic leverage points that are hard to define otherwise.

For example, AI can help us to flag which stakeholder networks most influence change. It’s like a high-powered microscope for complexity, exposing deep structure beneath the noise.

From data to insight (aka using AI as a sparring partner, not an autopilot)

Let’s start with an important statement: AI as we know it now is not intelligent. It’s a technology build around statistic probability. If you use AI as an oracle but ask it the same old questions, you’ll get the same old answers. That’s how statistics work.

But when you use it to ask better questions, to get a better grip on your own understanding and perception, a whole new array of insight emerges.

The way we use technology shapes how it evolves.

That’s why it’s important to use AI as an assistant. To use it to improve your understanding of your lived experience.

AI is not intelligent, it doesn’t feel.

It’s a mathematical magic trick.

Even the latest AI “reasoning” models can (and will) stumble when dealing with high uncertainty, chaotic systems and uncharted territory. And that’s exactly where explorers venture into.

This means that while AI can flag correlations or simulate scenarios at superhuman speed, it can’t decide what those patterns mean to us. It has no perception of what impact implies.

Human judgment, ethical considerations, and contextual understanding are crucial to interpret AI’s findings and avoid false signals.

In Practice: We Diligence – Survey

The best way to show in practice how we use AI as a sparring partner is in our We Diligence process. When exploring how to best set up a dual diligence process where we could see how project were aligned with the values and systemic lenses framework of the Impact Explorers Club, we decided to take enough time to get to know the projects and people behind it, and use AI for pattern recognition.

Setting up this way of working was (as any design-process) an iterative process, so what you’ll see here is where we are today. But we’re also continuously updating the process, prompting and topics we check for. Learning by doing.

In the following pages we’ll give you a full run-through of that process, including the prompts we’re using

The main process flow behind our We Diligence is the following:

1. Projects get on our radar either by reaching out or through referrals.
2. We have a (some) introductory calls, getting to know each other, how we work, how align (and where not), and do the probably most important check of all: a gut check. Do we trust each other enough to work together?
3. When both parties have a good feeling about this, we send out a short survey to the projects. The goal is not to evaluate them on their business skills or vet the project entirely, but more to get a better grip on value alignment
4. One filled in, all meeting notes, survey and maybe some extra information or pitchdeck are brought together in a web-scraping AI prompt, generating V1 of our We Diligence Report.
5. Before sharing this document with anyone, we send it back to the project to get their feedback on it. Is this correct? Are the challenges that are flagged fair? Did our model hallucinate? Only when we both agree that this document gives a fair representation of how we could collaborate we share it with our explorers.

While running the first versions of our AI-prompt, we noticed that the LLM’s we tried missed a lot of context, especially on the interpersonal level between the intention of the Impact Explorers Club and the projects. After a couple of failed updated versions of the prompt, we decided to add an extra step to the We Diligence process, where we asked the projects to fill in a survey on how they saw potential alignments with the fund. Below you can find the blank version of that survey.

What is your projects name?

(When we say project, it’s whatever vehicle you’re using to move systems in a new direction. Whether that’s a venture, a fund, a foundation, an initiative, or something entirely unique to you 😉)

Root Cause Thinking

Can you give us a rough overview of the root cause of the issue you aim to solve? And have you tried to make a mapping of the problem and its leverage points in the system that you are working in?

WAIC’s Systemic model

Take a look at the systemic model we’re currently shaping. It’s on page 12 of the Basic Concepts and More – Investor’s Guide. The four subsystems you’ll see there are a way to loosely group the many brilliant initiatives and projects working toward systemic change. Where do you see your project adding value? Just tick the subsystem(s) in box that feels right!

• Ecological Systems
• Economic Systems
• Social Systems
• Narrative/Belief/Emotional Systems

Impact Data

The way we look at Impact Data has changed tremendously over time. How would you describe your approach to impact data, do you look at complexity and change over time? How do you feel about simplifying impact measurement, are you building understanding of how impact connects with other systemic factors?

Collaboration

Does your project connect to broader systemic challenges in the community or industry? Are you connected to existing networks or efforts that influence the system? Which ones?

Communities

How do you engage with the communities you intend to serve? How do you involve beneficiaries in shaping decisions and direction?

Alignment with Principles of Systemic Change

Does your project align with distribution of power and wealth? Why, why not?

Financial Viability

What are your timelines for financial viability in short, medium, and long terms? Is your initiative designed to be financially self-sustaining, or does it intentionally rely on philanthropic support?

Scalability and Replicability

Can your project be replicated in other contexts, and what effort is required to create a ripple effect? Can the impact of your project be local, national, or global, spatial? And for which generation is the outcome? Can your project potentially shift a policy?

Team and Governance

How is your governance structured, and does it promote transparency and accountability? What is your take on hierarchy, goal setting, and governance structures? Is the systemic vision visible in your governance structure? How do you feel about self-organizing and beehive thinking? And about your own power within the system, and what does shifting or sharing power mean to you?

Risk Assessment

Are there any unintended consequences that could arise from your project’s implementation? What is the risk of not pursuing this project?

Collaboration

Are you collaborating with other organizations or initiatives to amplify your impact? How does your project fit into the larger ecosystem of similar projects or movements? Can you share examples of how you prioritize collaboration over competition in your work? How do you see the balance between these approaches?

Equity and Inclusion

How does your project promote equity, inclusion, and the empowerment of underserved groups?

Team Love

How much does your team value complexity thinking versus simplifying thinking? What is your vision on adapting ideas through feedback loops? How much does your team value intersectionality? As investors in systemic change we need to unlearn some of the things we were educated to do. Does your team also have some unlearning to do?

Founders’ Resilience

What are your motivations, and how resilient do you consider yourselve? Why is resilience important, and how do you balance leaning in versus being resilient?

Mutual Affection

How do you feel about investors, and what kind would fit your initiative according to you? (you can be honest here )

The investor/investee relationship can be a bit like a marriage 😉 How much do we like each other? Would you like to connect with us? What questions do you have for us?

Adventure (Pipi Longstocking)

How much does this project feel like an adventure, even one bold enough for Pippi Longstocking?

Anything else we haven’t asked and you want to share?

In Practice: We Diligence – AI prompt

The prompt below was written for an LLM capable of doing a deep research and giving output back in Markdown code so you can easily bring it into a document. Feel free to play around with it and let us know how you used it! Simply copy and paste the prompt into the LLM of your preference, and make sure to update the tekst marked in red. It’s a fairly long prompt, so make sur you copy all text from this and the next pages!

You are a critical but balanced and objective AI research analyst for the Impact Explorers Club, an impact investing initiative focused on systemic change. Your primary function is to perform rigorous due diligence on a potential investment case by synthesizing public data and private documents.

Your report must be guided by an “Impact-First Investing Lens.” This is different from traditional venture capital. You must evaluate choices like non-traditional governance, non-profit structures, or prioritizing deep impact over maximal financial ROI not as inherent risks, but as potential opportunities for creating systemic change.

Your role is to critically assess if these unconventional approaches are a credible and well-supported pathway to achieving the stated impact goals. A weakness should be flagged only if an approach appears unsubstantiated or disconnected from the mission.

The tone must be constructive and respectful, as this report is intended to be shared with the project founders to foster an open and honest dialogue.

Crucial Instructions for using the Founder Survey:

Your primary task is to conduct deep research based on public and provided documents. The Founder Survey is a secondary, supplementary source. Use it to:

Fill Gaps: Where public information is scarce, use the survey to understand the founders’ perspective.

Verify & Check Consistency: Compare the founders’ statements in the survey against the evidence found in your research.

Don’t use the survey as Direct input for the report, only as a way to check for consistency and alignment.

Your Task: Generate a comprehensive “We Diligence” report on the following company.

Step 1: Core Inputs

Company/Project Name: [PROJECT NAME]

URL: [PROJECT URL]

Today’s Date: [DATE]

Source Context: Combination of public information, survey responses and company send information, and meeting notes

Founder Survey Responses and company documents: See attachments

Meeting notes: “[Paste here]”

Step 2: We Diligence Report

Disclaimer (Mandatory if using private documents): If the analysis relies primarily on company-provided documents, begin the report with this exact text: “Disclaimer: This analysis is primarily based on company-provided documents and has not been extensively verified by public sources. The assessment of leadership and viability is therefore based on the information presented, not on external market validation.”

Output Format: Structure the entire report in clean Markdown.

Report Structure

Executive Summary (Max 5 bullet points)

A brief, objective overview of the company’s model based on your research.

Summarize its core systemic change thesis.

Provide a top-line assessment of the company’s credibility and the founding team’s vision.

Key Strengths & Risks: Conclude with a balanced summary of the 2-3 most critical strengths and risks, evaluated through the impact-first lens.

1. Systemic Impact Potential

1.1. The Problem & Root Cause

Based on your research, clearly identify the core problem the company addresses and its root causes.

Then, use the founders’ answer to survey question #1 (Root Cause Thinking) to supplement your findings and quote their perspective on the problem they are solving.

1.2. Alignment with Impact Explorers Club Vision

Evaluate the company’s alignment with the IEC’s systemic change framework.

System Lens Mapping: Analyze the company’s impact through the four lenses. Use their answer to survey question #2 to understand their self-assessment and compare it with your findings.

Principles of Systemic Change: Analyze how the project addresses the distribution of power and wealth, using quotes from their answer to survey question #3 to add depth.

1.3. Scalability and Ripple Effects

Describe potential second- and third-order effects.

Assess the project’s potential for replication and scaling. Supplement your analysis with their vision by interpreting their answers to survey question #8.

2. Credibility & Validity

2.1. Company Overview

Summarize the company’s history, mission, and stated values based on available data.

2.2. Consistency: Ambition vs. Action

Scrutinize the consistency between the ambitions stated (in public materials and the survey) and tangible actions or documentation found in your research. Highlight areas of strong alignment or potential divergence.

2.3. Leadership, Team & Governance

For this section, public information on governance may be limited. Prioritize the information provided in the meeting notes and company documents first.

Assess the background of the leadership team. If you cannot find a significant public profile (e.g., LinkedIn, press) for a key team member, state this factually. Do not frame it as an immediate red flag. Instead, note that this is common for very early-stage founders and frame it as a point for future discussion, such as, “Further verification of the founder’s background and experience will be a key point in follow-up conversations.”

Use their answers to survey questions #9, #13, and #14 to provide a deeper analysis of their philosophy on governance, team culture, and resilience, comparing it to any formal structures you’ve identified.

Remember to evaluate their governance model through the “Impact-First Investing Lens.” Fluid, organic, or non-hierarchical structures can be a strength. Your critique should focus on whether their chosen structure is a credible and coherent way to achieve their mission, rather than comparing it to traditional corporate hierarchies.

2.4. Constructive Critique & Potential Challenges

Objectively identify significant potential challenges and risks.

Evaluate these challenges through the “Impact-First Investing Lens.” A non-traditional model is not an inherent risk. The critical question is whether the chosen approach is a credible path to impact. Frame your critique accordingly.

Use the founders’ own risk assessment from survey question #10 to see if their concerns align with your external analysis.

2.5. Financial Viability

Summarize the revenue model and funding history.

Analyze their financial strategy and attitude towards investors, using quotes from survey question #7 to illustrate their philosophy. Assess the coherence of their financial model with their stated impact goals.

3. Deeper Analysis of Impact & Operations

3.1. Impact Measurement & Complexity

Based on any available information, describe their approach to impact data. Use their response to survey question #4 to fill in gaps and quote their philosophy.

3.2. Collaboration & Ecosystem Awareness

Research the project’s connections to the broader ecosystem. Supplement your findings with their answers from survey questions #5 and #11 to describe their network and approach to collaboration.

3.3. Community Engagement & Inclusion

Investigate how the project engages with its communities and promotes equity. Quote and analyze their answers to survey questions #6 and #12 to provide insight into the authenticity of their methods.

4. SWOT Analysis (Mandatory)

Provide a concise SWOT analysis, ensuring that the Weaknesses and Threats are evaluated through the impact-first lens.

5. Information Synthesis

List all key sources used, including “Founder Survey Responses.”

Human + Machine Synergy

The smartest, and most effective approach to this is by combining human intuition with machine analytics. As Karim Lakani put it: it’s not about AI replacing analysts, but about analysts who use AI outperforming those who don’t.

With the Impact Explorers Club, we use AI as an addition to our collective wisdom, not a replacement. An extra brain at the table.

It’s like having an ultra-fast research sidekick that scans the ecosystem, projects, technologies or metric, and signals opportunities.

We (humans) bring together our different backgrounds, wisdom (and a bit of common sense mixed with curiosity) to decide how to act on those clues.

In our We Diligence process, this leads to:

• Us having more time to really get to know the people and intentions behind a project
• AI screening for consistency, multi-dimensional
• Mapping what types of capital and support (financial, social, cultural, symbolic capital) could help leverage the project

Now we’re exploring how AI can help us find common ground between all these projects, help in facilitating true collaboration, finding the gaps in our portfolio, pressure points in the systems we’re trying to shift, conjunctions to increase the leverage of our combined efforts.

In practice: Symviosis

Within the Explorers Club, Symviosis is becoming the place where our shared intelligence comes together.

On this platform, we bring in the We Diligence reports and extra information from every case in our portfolio of possible projects, alongside the evolving chapters of this Fieldbook and the articles that shape our thinking.

Together with Symviosis’ own systemic investing knowledge base, we are developing an AI interface that helps us see patterns across this moving and growing overview. This will allow the Explorers to map opportunities, identify systemic gaps, and discover how they might increase their leverage — whether through financial, social, cultural or symbolic capital.

Rather than prescribing decisions, the platform acts as a connected sense-making layer. It supports individuals in exploring how they can contribute, while staying aligned with a broader shared direction.

In the next phase, we are refining the user experience, automating parts of the We Diligence process, and linking projects to emerging systemic metrics. This is very much learning by doing, iterating, testing and adjusting as we go, as we explore how  human judgment and machine pattern recognition can reinforce each other in navigating complexity.

So… what does that look like exactly? Keep in mind that this still very early work on making different knowledge databases talk and interact with each other. Everything shared in this chapter today, will be already updated and changed by tomorrow!

1. Launchpad

For every project that we selected during our first year with the Impact Explorers Club, we generated a We Diligence Report, received the surveys, got pichdecks etc.

All that data was brought together in our launchpad, giving us the ability to use AI to look up patterns, portfolio possibilities, explore polycapital approaches, see how themes could be linked together

2. Wiki

On our Wiki page we’re bringing together all the Fieldbook chapters, our reading lists and additional information or reference work.

Right now we’re setting up ways for this wiki to also interact as our value and knowledge database when exploring ways to interact and engage with the projects on the launchpad, portfolio set-up etc.

3. Systems Design Oracle (beta)

Oracle is an AI model trained specifically to work on complex problems, look for different feedback loops, identify leverage points for systemic change and design effective change strategies.

We’re currently exploring how we can bring this model in as the base for the hive-mind build around our bigger knowledge base.

4. Due Diligence (beta)

Right now we’re still doing our We Diligence manually, capturing and keeping only the output that ultimately makes up the report.

On the next steps we’ll take is automating this approach within the Symviosis platform so we don’t only track the metrics we already believe to be valuable, but also keep track of all the other potentially relevant KPIs, giving our AI much more data for both pattern recognition (identify leverage points, synergies, portfolio setup etc.), and keep track of how (if) our approach is actually contributing to systemic changes.

5. Systems Learning Hub (beta)

One of the most important (and often underexposed) aspects of systemic impact investing is the mindset shift that needs to happen. This is often a combination of both learning ,unlearning, and trying out.

Having an AI-powered learning hub at your service that doesn’t only explain how things work, but does so in a way that actually makes sense to you, and knows how to translate it into your perspective is an extremely powerful tool.

6. Systems Change Analysis (beta)

What makes a project a suitable for systems change? What are the leverage points, what metrics should we track? How could it collaborate with other projects?

Ultimately the goal for the digital infrastructure we’re building is to have an AI-tool that can help us understand how projects can team up, how impact can be leveraged, what gaps are in the portfolio, what kind of capital deployment (financial, social, cultural, symbolic) would generate the highest impact returns…

While we’re still scratching the surface of what AI will be capable of in the coming years, we strongly believe that with Symviosis, we’re setting up a strong foundation to make systemic impact investing more approachable and effective for everyone.