5 ways to use AI for better product descriptions and discovery

Customers cannot buy what they cannot find. They will not buy what they do not understand.

Every multichannel seller managing hundreds or thousands of SKUs across Amazon, eBay, Shopify, TikTok Shop, Google Shopping, and social commerce platforms faces the same challenge: each channel requires different content, formatted differently, optimized for different algorithms. Teams either copy generic descriptions everywhere—reducing visibility—or spend 45 minutes per product per platform—destroying budgets.

AI is rapidly becoming the default way retailers solve this. In one global study, 71% of consumers said they want generative AI integrated into their shopping experiences—and the demand is strongest among Gen Z and Millennials.And retailers aren’t investing on a whim. McKinsey estimates generative AI could unlock $240–$390B in annual economic value for retail (roughly 1.2–1.9 percentage points of margin impact across the industry).

Understanding AI’s role and limitations

AI will not solve this perfectly. Critical limitations include:

• Specification hallucination. AI invents details with confidence. It will claim products are “dishwasher safe” when they are not. Every output requires verification against source data.
• Generic output. AI generates technically accurate but uninspired copy: “This product features advanced technology for optimal performance.” Human review must inject differentiation and personality.
• Brand voice inconsistency. Approximately 20% of AI-generated descriptions will sound off-brand—too formal, too casual, or too generic—requiring correction.
• Research errors. AI pulls specs from outdated product versions, confuses compatible accessories, and states incorrect dimensions. Verification against manufacturer documentation is mandatory, especially for regulated products.

Despite these limitations, the upside is real when you implement it correctly. A practical example: Linearloop cites controlled tests where AI-generated product text increased conversion rate by up to 23.7%, alongside dramatic production speed improvements at scale.

1. Generate platform-specific content

Platform algorithms reward platform-specific optimization. Generic content renders products invisible. A 500-product catalog across three platforms requires 1,500 unique descriptions. At manual rates, this demands multiple full-time employees.

eBay has leaned hard into this problem. Since launching AI selling tools in 2023, 10M+ sellers have used them to create 200M+ listings, with ~500,000 AI-assisted listings per day.

That’s the scale advantage: the only way to keep content fresh across massive catalogs is automation + review.

Implementation

Start with centralized product data—one master source containing all specifications, features, and benefits. Tools like ChatGPT, Jasper, or Describely transform base data into platform-optimized variations:

• Amazon: Keyword-dense bullet points engineered for A9/A10 behavior. Benefits over features.
• eBay: Longer storytelling format optimized for browsing behavior and marketplace discovery.
• Google Shopping: Structured data with comprehensive specifications.
• TikTok Shop: Short, creator-style product descriptions optimized for video-first discovery, impulse purchase behavior, and TikTok’s recommendation algorithm.
• Social commerce: Short, punchy, visual-first content designed to stop scrolling.

Amazon prompt example

Create an Amazon product description for [wireless earbuds] with 5 bullet points under 200 characters each. Include these keywords: noise canceling, bluetooth 5.3, waterproof, 30-hour battery. Focus on benefits. Sound like you’re explaining to a friend why these are worth buying.
Rewrite this prompt completely for eBay, emphasizing story and use cases over keyword density.

Expected results

• Time per product: 10–15 minutes (down from 30–45 minutes)
• Search impressions improve within weeks as relevance signals strengthen
• Click-through rates increase as platforms recognize optimized content

2. Optimize for discovery while maintaining brand voice

Algorithms require keywords. Brands require human readability. Most companies choose one approach, resulting in either keyword-stuffed robotic copy or beautiful content that never ranks.

The opportunity is bigger than “SEO keywords” now: consumers increasingly expect AI-assisted discovery experiences. Capgemini found 71% want GenAI integrated into shopping, and the report describes a major shift in how people want to search and browse products.

Search optimization process

Phase 1: research
Use AI to identify:

• Keywords top competitors rank for
• Long-tail search terms with lower competition
• Platform-specific search patterns
• Gaps in current content

Phase 2: content creation with constraints
Modern AI tools can be trained on your brand voice. Upload existing descriptions, define tone parameters, and specify prohibited language.

Google Shopping prompt example
Write a Google Shopping description for [stainless steel water bottle] that includes these keywords: insulated, BPA-free, leak-proof, 24-hour cold. Write in [Brand Name]’s voice: friendly, sustainability-focused, conversational. Avoid corporate jargon. Sound like an expert friend giving advice.

SEO check: AI content can rank (if it’s helpful)

Google’s own guidance is clear: using generative AI content is fine as long as it meets Search Essentials and spam policies and isn’t “low effort / low originality / no added value” content at scale.

So the workflow isn’t “publish anything the model writes.” It’s “use AI to draft fast, then enforce quality and usefulness.”

Before and after comparison

Before (keyword-stuffed):

“Premium wireless Bluetooth earbuds with advanced noise cancellation technology ANC feature premium sound quality audio Bluetooth 5.3 connectivity wireless charging case waterproof IPX7 rating.”

After (AI-optimized with brand voice):

“Block out distractions with active noise cancellation that actually works—whether you’re on a crowded train or in a noisy office. These wireless earbuds deliver 30 hours of playtime, connect instantly via Bluetooth 5.3, and survive rain, sweat, and accidental coffee spills (IPX7 waterproof rating).”

Outcomes

• Content stays consistent across thousands of SKUs
• New team members generate on-brand descriptions immediately
• Budget 20–30% of time for quality control

3. Enrich product data to power search and recommendations

Incomplete product data creates two failures: search algorithms cannot surface products for relevant queries, and recommendation engines cannot match products with complementary items.

The product data problem

Manufacturer-provided content is frequently incomplete: missing specifications, absent attributes, impenetrable technical jargon. Manual research requires cross-referencing competitor listings and translating technical features into customer-friendly language.

AI can accelerate enrichment, but the highest-performing teams treat AI as an assistant—not the source of truth. The best practice is:

• Use AI to propose missing attributes and phrasing
• Validate against manufacturer data / internal PIM
• Store the final result in a single “source of truth”

Why this matters for discovery

Recommendation engines need structure. A/B-tested evidence supports this: BigCommerce rolled out AI-driven recommendations and reported 20%+ higher CTR than the prior system, and shoppers who engaged with AI recommendations saw 2×+ revenue during testing.

Implementation

Identify data gaps:

• Which attributes are missing?
• What does Amazon require that you are not providing?
• What about Google Shopping structured requirements?

Automated enrichment workflow:

1. Input basic product information (manufacturer, model, category)
2. AI suggests specs/attributes + customer-friendly phrasing
3. Humans verify (and correct) against source docs
4. Attributes are structured for platform requirements

Example: IP68 rating becomes both:

• “IP68 waterproof rating” (technical searchers)
• “waterproof up to 6 feet underwater” (general searchers)

Results

• Product onboarding time: 20–30 minutes (down from hours)
• Better visibility as attribute gaps fill
• Increased recommendation placements
• Verification time remains mandatory to protect trust

4. Turn reviews into conversion assets with AI summaries

Even when shoppers find your product, decision friction kills conversion—especially in categories with lots of reviews.

Retailers are already deploying AI on PDPs for this exact reason. Digital Commerce 360 reported major retailers using generative AI for product pages—including review summaries and content updates—specifically to improve product decision-making at scale.

And academic evidence backs the impact: research on AI-generated review summaries found they speed up purchase decision-making and reduce hesitation, with effects varying by product type and price.

Implementation

• Summarize reviews into “Pros / Cons / Best For / Watch Outs”
• Keep a link/expand to the raw reviews (don’t replace them)
• Label summaries as AI-generated to set expectations (and build trust)

Expected impact

• Faster decisions
• Fewer pre-purchase questions
• Lower bounce on PDPs with heavy review volume

5. Prepare for “agentic” discovery

The next wave of discovery is being the product AI assistants recommend when shoppers ask natural language questions.

McKinsey describes “agentic commerce” and estimates that by 2030 it could drive up to $1T in U.S. B2C retail and $3T–$5T globally in orchestrated revenue.

That’s not a small channel. It’s an entirely new interface layer for commerce.

What to do now

• Build richer, more structured product data (attributes, use cases, compatibilities)
• Write descriptions that answer natural-language intent (“best for…”, “works with…”, “fits…”, “ideal if…”)
• Maintain accuracy and transparency. AI agents will punish ambiguity and inconsistencies

Implementation framework

These tactics work as an integrated system: channel-specific descriptions create visibility, search optimization ensures ranking for correct queries, complete product data powers algorithms and recommendation engines, review summaries reduce decision friction, and agent-ready content extends discovery into conversational AI.

Week 1–2: foundation

Centralize product data into one source of truth. Audit existing data, identify gaps, document brand voice and style guidelines.

Week 3–4: high-impact product testing

Select 10–20 key products. Generate channel-specific descriptions. Measure impressions, CTR, conversion. Document what works.

Month 2–3: systematic scaling

Expand by category. Enrich attributes systematically. A/B test description variants.

Month 4+: optimization and expansion

Add chat-based discovery experiences, seasonal refresh cycles, and deeper automation only after the review workflow is stable.

Critical metrics

• Search impressions (visibility)
• Click-through rates (relevance)
• Conversion rates (effectiveness)
• Time spent on content creation (efficiency)
• Error rates (quality control)
• Customer specification questions (information gaps)
• Return rates (accuracy of information)

Common failure modes

• Over-automation: publishing unverified specs creates returns and negative reviews
• Platform uniformity: identical content across platforms wastes the whole point
• Keyword-first writing: ranking without differentiation produces traffic without sales

Decision framework

Week 1 experiment

Pick one product category and run this test:

• Create platform-specific descriptions for Amazon + one additional channel
• Optimize for search using platform-specific keyword research
• Enrich missing attributes
• Track baseline metrics and measure for 2–3 weeks

Scaling decision criteria

Expand when:

• Improvement in at least two of three core metrics (impressions, CTR, conversion)
• QC can keep error rates under control
• Team can create consistent prompts and reviews

Pause and refine when:

• Error rates exceed 5% of published content
• Time savings fall below 40%
• Brand voice issues appear in more than 20% of output

The businesses succeeding with AI use it for repetitive, scalable work that typically buries ecommerce teams—so humans can focus on strategy, judgment, and quality.

Measure everything. Scale what works. Review all output before publication.

FAQ