DAOs could hold the answer to better data governance policies

Automation, hybrid working models, the human cloud, the metaverse, and a more flexible and collaborative work environment are just a few of the trends shaping the 21st century office. Another such trend that isn’t getting the attention it deserves due to its innovative nature: decentralized autonomous organizations, or DAOs.

DAOs are businesses built entirely of coded rules and decisions. As the term suggests, autonomy means that the company runs almost entirely without human intervention, but can still serve as an extension of a traditional limited company. In other words, people, often aligned with a common interest and lacking a single leader, collectively own the company and collaborate within the project and its platform, often across borders. All administrative responsibilities for a DAO are in the capable hands of blockchain technology.

For example, if the DAO achieves a certain goal, smart contracts encoded in blockchain technology applications would execute a different policy themselves. Essentially, these smart contracts define the rules — set by DAO members and verifiable through the DAO blockchain — for automating the company’s operational process.

The future of these organizations looks so bright that companies are springing up to fuel their growth with a supporting infrastructure. An example is Utopia Labs, a company I invest in. Founded in late 2021, the organization is building an operating system to make DAOs even more efficient. Today’s AI-focused technology leaders have much to learn from this dynamic. Transparency, accountability, and efficiency in smart contracts can provide insights into the design and adoption of data governance policies for AI-driven systems.

The need for data governance of AI-driven systems

AI-driven systems have gone well beyond automating mundane, repetitive tasks with little to no supervision or guidance. Now companies are using data-driven AI models to accurately predict behaviors and shorten innovation cycles to bring new products and services to market faster without sacrificing quality. AI enables organizations to more effectively monitor systems for patterns and anomalies with uninterrupted attention space. Should a system detect an anomaly, a company can take immediate action and mitigate potential risks to operations.

More importantly, many companies are becoming more data-centric in their business models. This is where data governance policies play a crucial role, as data serves as the strategic heart for generating new business opportunities. Some companies simply repackage data and sell it, others use the information to provide guidance and guide improvements, and still others offer AI-driven data catalog solutions to inventory and understand data from disparate sources. No matter where a company operates, data is a commodity.

Therefore, data governance is an absolute necessity. And the unique structure of DAOs can be instructive in controlling AI-driven systems. Community decisions, not the motivations of central decision-making bodies, create incentives for DAOs. Likewise, the entire customer-enterprise relationship could be retooled to provide data governance.

DAOs: Building Better Data Governance Policies

By communicating with customers and engaging them in using personal data to create services, contributors understand how information flows through each node and process. Working together makes the relationship closer and more iterative. It also encourages the data collection process and enables three of the most important policies:

1. Consumer-oriented product development

If AI-driven systems continue to inform product development using the current tracking-based data model, consumers will be limited to choosing products that result from monitoring and interpreting their data and behavior. Conversely, DAO product decisions are user-driven from the start, allowing users to intuitively discern their own needs in a product and then make design decisions specific to those biases.

2. Continuous iteration

DAOs are constantly iterated. Contributors zoom in and out of project orbits, lending skills at a dizzying pace compared to traditional tenures. This accelerates the innovation cycle, continuously optimizing existing products or services with new capabilities as they emerge.

3. Democratization

In a DAO, contributors vote on the direction of projects, creating a feedback loop that currently doesn’t exist in AI-driven systems. Rather than simply refining complexity into simplicity by asking people to accept the internal decisions, DAOs bend the arc of data models toward community-centricity.

However, DAO policies are not without their pitfalls. Although distributed, DAOs can still be subject to bias, risk, and manipulation. Take the Maker platform for example. It uses a member voting DAO framework to drive protocol development. Anyone can invest in voting rights with MKR tokens, a decentralized exchange. However, those with the most MKR tokens invested have more influence as their votes are weighted more heavily. So there is the potential for “authority” in decision-making. While the community is still small, bad actors could destroy the still nascent governance structures.

We are in the first chapter of a story that winds its way through pitfalls and downsides on its way to the paradigm shift. Both DAOs and AI systems need to be audited and regulated in a way that enables their successful journey on this journey.

Dan Conner is a general partner at venture capital rise.