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AI as the Silent Partner in Platform Co-ops?

By Trebor Scholz

Introduction

One of the biggest challenges for platform cooperatives is securing adequate financing while maintaining their democratic governance structures. Unlike traditional tech startups, which can scale rapidly through venture capital investment, platform cooperatives prioritize shared ownership and long-term sustainability—a model that does not fit neatly into conventional financial frameworks.

As a result, platform co-ops often struggle to access investment at the scale required for growth, forcing them to rely on grants, member contributions, or impact-driven funds, which may be insufficient or inconsistent over time.

As a 2025/2026 ICDE Research Fellow and a professor in Mondragon’s MTA program, I explore how AI can unlock new financing models for platform cooperatives. Specifically, I will analyze how AI-powered financial models, impact assessment tools, and data governance frameworks can be leveraged to enhance investment opportunities without compromising cooperative principles.

This project will take an empirical and case-based approach, including interviews with cooperative leaders, financial experts, and policymakers in the Basque Country, as well as ethnographic research on existing financing strategies in cooperatives experimenting with AI. Given the growing importance of data governance in financial decision-making, this research will also examine the role of initiatives like the EU Data Governance Act in facilitating data-sharing frameworks that support cooperative financing models.

AI and Financing: Beyond Venture Capital

AI is transforming finance across industries, from credit risk analysis to automated investment strategies. However, its application to cooperative financial models remains largely unexplored. The challenge is that most AI-driven financial tools are built for investor-owned firms, optimizing for profit maximization rather than long-term sustainability and democratic governance.

One potential application of AI in cooperative finance is impact-based credit ratings, where AI models assess the financial health and social impact of cooperatives in real time, making it easier for them to access impact-driven investment funds. AI-driven credit scoring in Kenian SACCOs, which considers diverse member data, exemplifies this, reducing default risks and enhancing liquidity. 

A relevant example is the use of alternative data sources—such as utility payments, rental histories, and even social media activity—integrated through AI to enhance credit risk evaluation. This methodology has demonstrated improved default prediction accuracy by 20% to 30%, broadening credit access for organizations without traditional credit histories.

Another avenue is cooperative asset securitization, where AI aggregates financial data to enable bond issuance or pooled investment mechanisms that provide liquidity while preserving cooperative ownership structures. A concrete step toward this approach is demonstrated by Ricoh Latin America and Cognitive Experts, who collaborated to introduce AI solutions in credit unions (SOCAPs) in Latin America, automating credit pre-approval processes and significantly improving financial data management efficiency. This advancement could support future developments in asset securitization by improving data aggregation and facilitating the issuance of financial instruments while preserving cooperative ownership.

Additionally, AI could enable collective investment platforms designed specifically for cooperatives. Using smart contracts and predictive analytics, these platforms could enhance financial traceability, align funding opportunities with cooperative values, and provide investors with clearer risk assessments based on real-time cooperative performance data. For example, the integration of smart contracts and predictive analytics in decentralized finance (DeFi) platforms showcases how AI can automate financial agreements and provide real-time investment performance analytics, enhancing financial traceability and aligning investment strategies with cooperative values.

Initiatives like the Platform Coop Venture Builder are already exploring how AI can enhance financial sustainability for platform cooperatives, making it easier for them to secure investment. The Platform Coop Venture Builder is a cooperative venture-building initiative designed to foster digital cooperative entrepreneurship through research, incubation, and acceleration. It aims to strengthen the cooperative platform ecosystem by promoting collective ownership, democratic decision-making, and a community-focused approach to technology and investment, ensuring that the cooperative values remain central to digital transformations​.

However, a critical question remains: Are AI-driven financial mechanisms realistically viable in the cooperative sector? While READ-COOP and Transkribus demonstrate how cooperatives can engage with AI, their model is not easily replicable. READ-COOP, for example, secured €10.6 million in European funding, which raises questions about the feasibility of AI-powered financial tools at scale for cooperatives without access to such funding opportunities.

This research will critically evaluate these models to explore the potential of AI-driven financing within cooperatives, with a focus on the Mondragón network and similar ecosystems. The goal is to assess whether AI can contribute to financial resilience in cooperatives without leading to high-risk speculative investments or necessitating structural changes that conflict with cooperative governance principles.

Data Governance as a Foundation for AI-Driven Finance

For AI-powered financial mechanisms to function effectively, data governance must be a central consideration. AI-based financial assessments rely on structured, high-quality, and ethically governed data. The ability of cooperatives to demonstrate financial viability and social impact depends on their capacity to collect, manage, and share data while preserving democratic control.

This is where data governance frameworks such as the EU Data Governance Act and European Data Spaces become relevant. These initiatives aim to enable secure and ethical data sharing while ensuring that organizations—including cooperatives—can access shared data pools to improve financial decision-making.

Participatory data governance models can provide a cooperative alternative to the dominant data economy, where financial and operational data are controlled by centralized entities. Cases such as SalusCoop, a health data cooperative in Spain, illustrate how cooperative approaches to data governance can support collective resource management while maintaining transparency and ethical oversight. Additionally, cooperative data governance initiatives, such as those discussed in the Promise and Perils of Cooperative Data Centers, highlight essential foundations for data-driven financial sustainability. Similarly, the Enyorata Loviluku women’s group in Tanzania transformed into a data cooperative, enabling Maasai women to leverage their collective financial data to access formal banking services.

As part of this research, I have conducted semi-structured interviews with key stakeholders, including cooperative leaders, AI experts, and financial professionals, to explore the role of AI in cooperative finance and the governance challenges that arise in data-driven financial decision-making.

These interviews have underscored that without clear governance structures, AI-powered financial tools risk reinforcing existing power asymmetries and excluding smaller cooperatives from funding opportunities. Interviewees have highlighted the critical role of transparency, data control, and cooperative oversight in ensuring that AI-based financial assessments align with cooperative values.

Specifically, data governance was identified as a major concern when integrating AI into cooperative finance, as financial decisions based on opaque AI models may replicate biases or create barriers for cooperatives that do not fit standardized credit evaluation metrics. Many cooperatives lack access to shared financial data infrastructures, making it harder for them to demonstrate financial stability and investment readiness. 

A key part of this research will be to explore whether similar governance frameworks can be applied to AI-driven financial models, ensuring that transparency, member control, and ethical oversight remain central to cooperative investment strategies.

Regulatory Considerations: AI, Data, and Cooperative Finance

AI regulation and cooperative finance intersect in crucial ways. The EU AI Act, for instance, establishes compliance requirements for AI systems used in credit and financial decision-making, which means that cooperatives adopting AI-driven financial tools must navigate these legal frameworks carefully.

At the same time, the Data Governance Act promotes data-sharing mechanisms that could support cooperative financial initiatives, enabling them to collectively manage and leverage data for impact-driven investment. However, the challenge remains: how can cooperatives implement AI-driven financial models in a way that aligns with these regulatory frameworks without creating additional administrative burdens?

This research will assess whether regulatory frameworks support or hinder AI-powered financial solutions for cooperatives, particularly in the context of securitization models, impact investment, and alternative funding mechanisms.

Conclusion: AI and Data Governance as the Future of Cooperative Finance

For platform cooperatives, access to sustainable financing remains one of the most significant barriers to long-term resilience and scalability. AI presents both opportunities and challenges in addressing this issue. While AI-powered financial tools have the potential to unlock new investment pathways, enabling cooperatives to access impact-driven funding, asset securitization, and collective investment mechanisms, their feasibility and alignment with cooperative principles remain open questions.

This research aims to explore whether AI-driven financial tools can contribute to strengthening the financial sustainability of cooperatives without compromising their governance structures or leading to unintended risks such as increased financial speculation or loss of democratic control. The governance of data will be a critical aspect of this inquiry, as the integration of AI into financial decision-making depends on structured, transparent, and cooperative-led data governance models.

A key question this research seeks to answer is whether AI-powered financial mechanisms can be designed and implemented in a way that serves cooperatives, rather than forcing them to conform to existing financial logics that prioritize risk aversion over collective impact. Cases such as Enyorata Loviluku demonstrate that data-driven financial inclusion is possible when cooperatives retain control over their information, yet challenges remain in ensuring that these models can be adapted at scale and across different sectors.

Key Areas of Exploration in This Research:

  • How AI can be leveraged to support alternative financing mechanisms for cooperatives while maintaining democratic decision-making.
  • The role of data governance in ensuring fair and transparent AI-driven financial assessments, mitigating biases that could disadvantage cooperatives.
  • The regulatory landscape, including the EU AI Act and Data Governance Act, and how cooperatives can engage with these frameworks to ensure equitable access to AI-driven financial tools.

Rather than assuming that AI is an immediate solution to the financing challenges of cooperatives, this research will critically assess its applicability, identifying both opportunities and limitations. The aim is to develop insights that inform future cooperative strategies, ensuring that technology serves cooperative needs rather than reinforcing financial exclusion.

Now is the time to move beyond theoretical discussions and explore how AI and data governance can work together—not as obstacles, but as essential components of a financial ecosystem that aligns with cooperative values and long-term sustainability.