AI-Powered Profits: Unlock Revenue While Safeguarding Your Data
he past few months at Silky Insights have been thrilling, as our deployed client products have not only maintained their reliability but also exceeded many of our internal usage goals, with nearly 5 million hits on the horison.
But what has really excited us is that we are starting to see our forward-thinking clients reap the benefits of their AI-enabled products, not just in the most obvious way, but also our clients are now starting to monetise the very architecture they built for AI models, transforming the value of their data into new revenue streams. By offering new value-added services, they're not only enhancing their client relationships but also tapping into previously uncharted business opportunities.
However, as AI opens new doors, it also introduces unique risks. Monetising data, particularly when it's being used in AI contexts, requires a clear understanding of how the data is used, managed, and safeguarded. While there are many well-established principles for sharing data, there are also distinct considerations when AI is involved. Here’s what you need to know:
1. Maintain AI-Specific Data Sovereignty
When it comes to sharing data in an AI-driven world, data sovereignty takes on a nuanced role. It’s not just about where your data is stored, but how it’s used within AI models. For example, ensure your data isn’t being used to train third-party models unless explicitly agreed upon. Instead, your data should only be used to analyse and generate insights. There’s a big difference between a model using your data for one-time processing versus your data becoming part of the model’s training set, where the AI could continually benefit from your proprietary information.
2. Realise AI-Driven Value, and Get Fair Compensation
AI amplifies the value of data, allowing businesses to unlock insights, optimise operations, or offer predictive services. But with this enhanced value comes greater risks, especially in terms of intellectual property. If you're offering additional value through your data (e.g., predictive models, AI-driven analytics), make sure you’re compensated for it, particularly if the third party intends to use AI models to derive new business outcomes from your data. Ensure agreements reflect the value you’re providing, while acknowledging the additional risks of AI-based exploitation.
3. Define Clear AI Use Cases and Handling Protocols
One of the most critical steps when monetizing data for AI is defining what the AI model can and cannot do with your data. Be specific about how AI will interact with your data. Some key questions to consider:
Will the AI simply analyse your data, or is it allowed to retain knowledge from it?
Are third-party AI models allowed to continuously access or use your data, or will access be time-limited?
What AI tools or platforms will be used, and are they sufficiently secure and transparent in their usage of data?
By having tight control over how the AI interacts with your data, you can avoid risks such as the unintentional training of models on your proprietary datasets, which could inadvertently diminish your competitive edge.
4. Audit and Track AI Data Usage
Regular auditing and tracking of how your data is used in AI environments is essential. AI models tend to evolve, and as such, so might the ways your data is applied. It’s important to establish clear tracking mechanisms that show how your data was processed by the AI, what insights were derived, and whether those uses align with the original agreement. This transparency ensures your data isn’t being repurposed or improperly leveraged.
5. Ensure Secure AI Data Destruction
In AI-driven data monetisation, you also need to focus on secure data destruction and removal protocols, especially once the engagement concludes. For AI models, this could mean not only ensuring that your data is removed from storage but also confirming that it has not been embedded into the model’s training. A thorough removal process guarantees that no long-term or residual benefits from your data continue after your agreement ends.
Other Considerations (Key Headlines)
Data Ownership & Licensing: Be specific about AI-specific rights over data use.
Compliance with Data Regulations: Ensure that AI-based data sharing adheres to local and global laws.
Data Anonymisation & Masking: Ensure anonymisation especially when AI models analyse data at scale.
Third-Party Risk Management: Vet AI platforms for security, ethics, and transparency.
AI Considerations Add Unique Value
We have now seen that proactive AI strategies don’t just help deliver better outcomes but unlock entirely new ways to monetise your data. By understanding the complexities of AI-driven data sharing—whether that’s avoiding unwanted model training or defining AI use cases with precision—your organisation can securely open new revenue streams while ensuring your intellectual property remains protected.
If you’re looking at how your AI-enabled data can offer value to others, now’s the time to explore how best to structure that opportunity while safeguarding your assets.