As AI agents like ChatGPT, Gemini, and Alexa+ begin making purchase decisions on behalf of consumers, traditional business intelligence is breaking down. When customers never visit your website or click your ads, how do you track market share shifts, understand competitive threats, or identify strategic opportunities? This shift to "agentic commerce" requires entirely new intelligence frameworks: tracking decision logs, agent reasoning, and prompt data rather than clicks and cookies. Organizations that adapt their competitive intelligence now will gain strategic advantage as AI-driven purchasing becomes mainstream.
Agentic commerce refers to AI agents making purchasing decisions and completing transactions autonomously on behalf of users, with minimal or no human intervention in the buying process.
When Amazon first launched Alexa, the promise was a tool that could take on the hassle of buying regular items. "Alexa, buy more toilet paper," and the toilet paper would arrive like magic.
In truth, this never caught on with the wider public, the capabilities were too limited.
With the modern capabilities of AI agents like ChatGPT, Gemini, and of course Amazon's own Alexa+, we are much closer to a world where a user tells their AI agent "Buy me a new pair of headphones for under $50, I need them by Friday."
The AI agent then compares your known likes and dislikes, its own product database, and the wider internet, to find the best pair of headphones with the price under budget that meets your delivery requirements.
This entire process will happen in seconds (or milliseconds), with no human-driven application or website browsing taking place.
Google recently launched their "Universal Commerce Protocol," which supports multiple retailers and enables direct product buying from inside Gemini. OpenAI have the "Agentic Commerce Protocol" with an integration with Shopify, and more will certainly follow.
As these protocols proliferate, the shift from human-driven to AI-driven purchasing accelerates dramatically.
As this agentic purchasing grows, traffic to product detail pages may drop precipitously, following early trends in clicks from search terms as "AI Overview" summaries replace detailed browsing.
Sales might stay the same, or rise, but website traffic will fall, and with it the accuracy of existing measurement will start to fail.
AI agents following URLs may strip tracking parameters (UTMs), use single-use virtual credit cards, or anonymized browsers, making it impossible to link a purchase back to a specific ad campaign.
Traditional competitive intelligence relies on observable touchpoints: website traffic patterns, search ranking changes, campaign spend estimates, and customer journey data. When an AI agent compresses the entire purchase journey into milliseconds of autonomous decision-making, these signals vanish.
Leadership teams lose visibility into:
In the past, we measured a consumer's age, location, and browsing history. In the future, that "persona" is defined by the inputs the human gives to their AI agent.
One person will have many different "agent personas," whether that's "Budget conscious" or "Fashion forward," depending on whether they're buying household staples or a new outfit.
To navigate this, brands must shift their focus toward new data sets:
Decision logs: Similar to how analysts currently examine web analytics, companies will require decision logs from agent providers to understand purchase intent. These logs reveal which products the agent evaluated, what criteria it prioritized, and where in the decision tree your product succeeded or failed.
Agent reasoning: Instead of relying solely on customer surveys, leadership teams can analyze AI reasoning to understand competitive positioning gaps (e.g., "Product rejected because the shipping window exceeded the user's requirement by 1 day"). This provides unprecedented clarity into why market share shifts occur.
Prompt visibility: Organizations need visibility into categorized prompt data to understand emerging consumer needs and determine strategic positioning for both human users and the AI agent layer. Understanding common prompt patterns reveals unmet market needs and competitive opportunities.
Understanding agentic commerce requires fundamentally different data collection infrastructure. Organizations need:
Opted-in behavioral panels that capture the complete user-to-agent interaction, not just transaction outcomes. This means observing the prompts consumers give AI agents, the alternatives agents consider, and the reasoning behind final selections.
Cross-platform tracking that follows behavior across ChatGPT, Gemini, Alexa+, and emerging AI shopping platforms. Agent recommendations vary significantly by platform, and competitive intelligence requires understanding where your products win or lose in each ecosystem.
Prompt-to-purchase linkage that reconstructs the decision journey from initial user request through agent evaluation to completed transaction. This reveals which product attributes, price points, and delivery requirements actually drive conversions versus what companies assume matters.
Decision reasoning visibility that decodes why agents recommend specific products. Without understanding the logic behind agent choices, organizations can't strategically optimize for selection.
RealityMine®'s behavioral intelligence technology provides exactly this infrastructure through opted-in, fully consented panel partners. We track prompts, agent interactions, and outcome events like purchases across major AI platforms, giving leadership teams the competitive intelligence they need to navigate the agentic commerce transition.
When the human is removed from the loop, we transition from Shopping to Logistics.
In a proactive world, brand discovery works fundamentally differently. If your fridge orders milk automatically, will the AI agent vary your purchase occasionally to provide variety, or default to the "Optimal Constant"?
Long-term, brands will fight to be the "Preferred Default" in the agent's initial configuration. It's no longer about winning the "moment of choice," but winning the "moment of installation."
This raises critical strategic questions for brand marketers:
The answers require measurement systems that track agent behavior, not just human behavior.
Is your leadership team still relying on traditional web analytics while AI agents make purchasing decisions without ever visiting your website? The competitive intelligence gap is widening as agentic commerce accelerates.
RealityMine® provides the behavioral intelligence infrastructure to decode agent decision-making and maintain strategic advantage in the agentic commerce era.
CTA: Contact us to discover how our technology reveals what AI agents are choosing and why.
Ewan is Chief Technology Officer at RealityMine, where he leads technology development. He oversees the engineering and infrastructure behind RealityMine's behavioral intelligence platform, which tracks consumer interactions across emerging AI technologies and digital platforms.
