# Your Next Customer Might Be An Agent. Here's How To Build Your Brand For Both Humans And AI
AI agents are becoming commerce gatekeepers. Within the next few years, autonomous purchasing systems will filter product recommendations, negotiate prices, and execute transactions without human intervention. Brands caught unprepared for this shift face a silent revenue loss: their products get deprioritized by algorithms that don't understand nuance, tone, or brand storytelling.
Toby Coulthard, writing for Crunchbase News, argues that startups cannot treat AI-ready branding as an afterthought. The window to embed these practices from day one is narrow. Companies building today must optimize for both human discovery and machine interpretation simultaneously.
The mechanics are straightforward but often overlooked. First, brands need to document their voice explicitly. A brand voice that resonates with humans through metaphor, humor, or cultural reference often confuses AI systems. Startups should create written voice guidelines that describe not just tone but also the core values and unique selling angles that differentiate them. This document becomes the reference layer for all future content decisions.
Product descriptions demand radical clarity. AI agents parse specifications, ingredients, materials, and return policies with mechanical precision. A beautiful but vague product page that charms human shoppers fails catastrophically when an agent's parser encounters it. Startups should write descriptions that pass both tests: compelling enough for human readers, structured enough for machine consumption. This means flagging key attributes upfront, using consistent terminology, and avoiding synonym sprawl that creates false distinctions for algorithms.
The third step is testing AI summarization. Coulthard recommends that founders regularly run their product copy through language models and check how these systems compress and represent their messaging. If ChatGPT or Claude misinterprets your brand's core benefit, an agent will too. This becomes a quality assurance practice, not an occasional audit. Startups should compare the AI summary against their intended positioning and iterate on copy until alignment holds.
Why does this matter now? AI agents are not vaporous sci-fi constructs anymore. Companies like OpenAI, Anthropic, and Mistral are shipping agentic products into production. Venture capital funds are backing agent infrastructure plays. Early adopters of enterprise procurement software will soon hand negotiation to autonomous systems. Consumer shopping will follow as adoption curves steepen.
The brands that win will be those that treated AI readiness as a competitive advantage rather than a compliance checkbox. Startups that build clear voice documentation, unambiguous product data, and machine-testable copy will see higher conversion rates when agents recommend their products. Competitors that ignore this shift will find themselves invisible to algorithmic buyers.
Coulthard emphasizes measuring success through conversion and revenue, not content volume. A startup with 100 beautifully written product pages that confuse AI agents will lose to a competitor with 50 pages that agents understand completely. The shift from human-centric metrics to outcome-centric metrics requires discipline. Startups should audit their analytics to understand whether AI traffic converts at parity with human traffic. If not, the gap signals a voice or description problem.
The practical timeline is urgent. Startups that implement these practices today build a structural moat around product discovery. Those that wait until agents dominate purchase flows will face expensive remediation. The best time to speak both human and machine was yesterday. The second best time is this quarter.