Your Value Proposition Was Written for Humans. Rewrite It for Machines.

"Elevate your everyday with our premium, small-batch, thoughtfully sourced blend — crafted for those who demand more."

An AI agent reads that and learns almost nothing useful. It can't confirm what theproduct is, what it does, who it's for, or how it compares to alternatives. Itmoves on.

This is the value proposition problem hiding inside agentic commerce. Brands spentyears perfecting copy that speaks to human emotion, aspiration, and identity.Now they're competing in a channel where the buyer is software — and softwaredoesn't care about aspiration. It cares about specificity.

"Answer Engine Optimization is becomingessential as discovery shifts to AI platforms." — commercetools

What an agent actually reads

When an agent evaluates your product, it's extracting a handful of signals: whatcategory does this fit, what are the primary attributes, what's the price point, what's the quality signal (reviews), and does it match the buyer'sstated criteria. Everything else is noise.

Your brand story is noise to an agent. Your founder's journey is noise. Theadjectives you've carefully chosen to evoke a feeling — premium, artisanal,elevated, effortless — are noise. The agent isn't looking for a feeling. It'slooking for a match.

That doesn't mean brand storytelling is dead. It means it lives in a different layerof the purchase experience — the human-facing layer. The machine-facing layerneeds to be rebuilt from scratch, and most brands haven't started.

The formula for a machine-readable value prop

Avalue proposition that works for AI commerce answers four questions in thefirst two sentences: What is it? What does it do? Who is it for? What makes itthe better option?

Comparethese two descriptions for the same product:

Version A: "Experience the transformative power of our premium collagen formula —designed to help you feel your best from the inside out."

Version B: "Marine collagen peptides, 10g per serving, hydrolyzed for absorption,unflavored. Formulated for joint support and skin elasticity in adults 30+.Third-party tested, NSF certified."

Version A wins a brand awareness contest. Version B wins an agent recommendation. Thebrands that figure out how to have both — a version A that lives in brandmedia, a version B that lives in feeds and PDPs — have the right architecturefor 2026.

→ Creative& Content  —  agencyfiveeighty.com/creative-and-content

→ Agent-ProofingYour SKU  — agencyfiveeighty.com/agent-proof-sku-checklist 

The rewrite exercise

Take your three best-selling products. Pull the current product description. Pasteit into a document and ask: if a machine was extracting product attributes fromthis copy, what would it find? Run that test honestly.

Then rewrite the first two sentences to answer: what exactly is this, what does itspecifically do, who is the primary buyer, and what's the credible differentiator (certification, ingredient source, third-party test, clinicalbacking). Keep the brand voice in the rest of the description. The first two sentences are the machine layer. Everything after is human.

This exercise takes about an hour per product. The impact on agent discoverabilitycompounds from day one.

Where brand voice still lives

Brand story telling matters for human shoppers who are browsing, comparing, anddeciding. It matters in CTV campaigns that build category consideration. Itmatters in social commerce where the feed is entertainment first. It matters ininfluencer content where the human telling the story is part of the productexperience.

Wha tit doesn't do is get you recommended by an agent to a shopper who asked for"the best magnesium glycinate for sleep under $30." That job belongs to clean data, clear attributes, and a value proposition built for machines.

FiveEighty writes for both readers. It's not a tradeoff — it's a brief with twoaudiences, each requiring a different craft.