Brand Health Tracking with LLM Equity (Part 3)
What Is An AI Trust Infrastructure?
In the second blog of our three-part series, we discussed the benefits of tracking brand health to form brand strategies that help improve how AI describe and surface your brand. But aside from understanding the dimensions of brand health and the metrics from which brand messaging can be measured, there is another layer that you would need to consider when building your brand strategies. Sure, your brand is now being represented in AI search results and recommendations, but have you set up your brand to not just catch attention but also gain consumer trust?
We’re in the early days of the AI-driven shopping with brands experimenting on how to best connect with customers and compete in this new landscape. While impressive and promising, consumers are approaching this emerging new shopping experience not without caution and circumspection. PwC’s 2025 Future of Consumer Shopping Survey has 64% of its respondents expressed that it would help them trust AI assistants to shop in their behalf if at least one safeguard is in place. These safeguards include but are not limited to approving all purchases before completion, money-back guarantees, turning off access anytime as well as setting strict spending limits.
This echoes back to the early stages of e-commerce with customers exercising prudence when providing credit card information on websites. The implementation of safeguards like SSL encryption and fraud protection subsequently enabled e-commerce to gain consumer confidence and scale for mass adoption.
Once AI-assisted shopping starts to scale, brands that have incorporated an AI trust infrastructure in their strategies would most likely thrive and surface better than those that don’t. But an AI trust infrastructure goes beyond just implementing safeguards for purchases.
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Building An AI Trust Infrastructure
There are at least a couple of things that could go wrong with AI assistants making your purchases. It could overspend or make unexpected or unauthorized decisions. It could buy the wrong selection because it misinterpreted products. That misunderstanding could be a result of outdated or inaccurate product information, or even an instance of AI hallucination when it had to guess because it has inadequate or misaligned data to work with.
While safeguards like spending limits and final customer approval could circumvent the abovementioned situations, what about for errors it commits that a customer is unable to fix because they don’t know what went wrong or how to resolve it? Now these are just a few examples of how consumer trust could be broken, but from these challenges a brand can base on and build their AI trust infrastructure.
Nowadays, product content are mostly structured to capture human attention and rank favorably with search engine optimization (SEO); with the rise of AI agent shopping, content needs to be just as friendly with generative engine optimization (GEO) by including product data optimized in a machine-readable format. In other words, brand content should start speaking to both customer and AI, with consumer terms mapped into specific attributes to help improve precise product matches.
Brands would also need to constantly monitor the accuracy of their product information and how they show up in AI search results to make corrections or adjustments whenever necessary.
Expanding into the concept of purchasing safeguards, perhaps an even greater degree of trust can be earned if consumers understand the scope of delegating to AI assistants through a clear, accessible and easily configurable presentation of the AI-assisted shopping process. In addition to limits and conditions on the purchasing decisions AI is allowed to make, this could include requiring customer approval under certain parameters, mapping and tracing every decision and action the AI makes throughout the shopping process, as well as the abilities to dispute and/or reverse results. Brands can also explore the option to collaborate with popular AI platforms to extend their suite of purchasing controls and safeguards to customers who prefer to shop in those third-party platforms over purchasing directly at their website.
There is also the question about how sensitive customer data is protected. In the coming age of AI-assisted shopping, this won’t be limited to just payment details but also include contextual data such as preferences, constraints, and intent. Understanding how that data is used, remembered, or protected could help customers make that leap into delegating shopping to an AI agent. This includes what data is being shared and who or which other platforms or companies it’s being shared with.
Brands can offer options to minimize the data being retained or limit the amount of time that information is kept, or even present the choice for guest or one-time shopping where no transaction details are ultimately stored. Customer should feel empowered when it comes to their privacy choices by being presented with clear, visible and configurable options.
And despite the gradual transition to an automated shopping experience, brands shouldn’t forget the value of being able to reach a human representative, especially when things escalate. Customers could feel lost, powerless and frustrated in a situation that could’ve been salvaged with intervention by another human.
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The Future of Brand Health Tracking
The concept of brand health has been around for more than just three decades but how it’s being tracked moving forward is being rewritten. Just as Generative AI has caught the world’s attention and fascination, LLM equity is quickly gaining steamed across various industries in just these last few years. While AI has a democratizing effect of leveling the field for players of all sizes, companies who are able to understand and leverage brand health tracking with LLM equity would likely emerge as leaders in their sectors.
Brands might not have full control over how they’re described or surfaced by AI, but they could strongly influence how they’re represented by developing coherent and consistent brand messaging reinforced by consumer-earned content built on trust and loyalty.
