The saying “garbage in, garbage out” has never been more relevant than it is with AI.
Businesses are rushing to use AI to generate product content, from descriptions to category copy. But too many are overlooking the most important ingredient: the quality of the data that feeds the model.
If your product attributes are messy, incomplete, or inconsistent, AI cannot fix that. It can only amplify the problems. In B2B ecommerce, where buyers rely on precise specifications to make confident decisions, poor inputs lead directly to poor outputs.
Why attributes matter more than ever
Product attributes are the building blocks of online buying. Dimensions, weight, material, compatibility, certifications, images, installation notes — these are the details buyers scan before they even read a description.
When AI generates content, it pulls from these attributes to create sentences and context. If the attributes are wrong or missing, the AI fills gaps by making guesses. That is when things go wrong.
Think of AI as a chef. If you give it fresh, high-quality ingredients, it can prepare a great meal. If you hand it stale, half-empty packets, the dish will be inedible.
What bad inputs look like in practice
Here are a few common scenarios:
- Incomplete attributes: A supplier forgets to list whether a cable is armoured. AI leaves it out of the description, and buyers order the wrong product.
- Inconsistent formatting: One record says “10mm”, another says “M10”, another says “10 mm”. AI generates clumsy, inconsistent copy that confuses buyers.
- Wrong classifications: A product is tagged under the wrong category. AI builds content with irrelevant features or recommendations.
- Missing compliance data: Fire ratings, CE marks, or safety certifications are absent. AI has no way of including them, so product pages go live incomplete.
Each of these errors has a cost. They frustrate buyers, drive up returns, and damage trust. In some industries, they also create compliance risks.
How enriched attributes improve AI output
Enrichment means filling in the gaps and standardising the inputs. It is the difference between AI producing generic filler and AI generating useful, accurate content.
Enriched attributes support AI in several ways:
- Accuracy: Complete data reduces the risk of hallucinations or misleading content.
- Consistency: Standardised attributes ensure uniform language across products, categories, and variants.
- Relevance: Rich attribute sets let AI highlight features that buyers care about most.
- Better search and filter performance: Accurate attributes feed not just descriptions but also site search, navigation, and recommendations.
In short, enrichment gives AI the raw materials it needs to be effective.
Practical steps to fix the input problem
If you want AI to deliver valuable product content, focus on input quality first.
1. Audit your current attributes
Run a completeness check across your catalogue. Which fields are missing most often? Where are formats inconsistent? Which attributes cause the most buyer complaints or returns?
2. Define your attribute standards
Agree on mandatory fields for each category. Spell out acceptable formats, naming conventions, and data types. Document these so suppliers and internal teams have a single reference point.
3. Standardise supplier submissions
Stop accepting whatever spreadsheets suppliers send. Provide a structured template or portal with clear rules. This sets expectations early and reduces cleanup work later.
4. Automate validation
Use validation rules to catch missing or incorrectly formatted data before it enters your system. Flag errors immediately so suppliers or internal teams can correct them at source.
5. Build enrichment into your workflow
Make enrichment an ongoing process, not a one-off project. Use AI and automation to suggest missing fields or to reformat data, but always review and approve before publishing.
6. Prioritise high-impact attributes
Not every attribute is equally important. Focus on the ones that buyers rely on most: safety, compliance, dimensions, and compatibility. Get these right before worrying about secondary fields.
The risks of ignoring enrichment
Some businesses hope AI can paper over bad data. In reality, ignoring enrichment creates new risks.
- Inaccurate product content: Wrong specs lead to misinformed purchases and returns.
- Customer frustration: Inconsistent data makes comparison difficult and slows decision-making.
- Lost revenue: Products stay off the site longer because data is incomplete.
- Compliance failures: Missing safety or regulatory attributes expose the business to legal risk.
- Erosion of trust: Buyers stop believing your product information is reliable, and start looking elsewhere.
These risks add up. Returns and complaints increase costs, while slower launches and lost trust reduce sales.
A practical example
Imagine a distributor selling industrial fasteners. Supplier A submits attributes with thread size in millimetres, Supplier B uses inches, Supplier C leaves it blank.
AI tries to generate descriptions, but the inconsistency leads to output like:
- “This fastener has a thread size of M10”
- “This bolt is 0.39 inches”
- “Thread size not provided”
The product pages look messy, and buyers cannot compare options. Some order the wrong size and return the products. Others leave to buy elsewhere.
Now imagine the same distributor with enriched, standardised attributes. Every record uses the same format: “M10”. AI generates consistent, clear descriptions. Buyers compare easily, trust the information, and purchase with confidence. Returns drop, sales increase, and AI actually adds value.
The bigger picture
AI can only ever be as good as the data you feed it. Clean, enriched attributes are the foundation of reliable, scalable product content. Without them, AI is just amplifying errors.
For B2B businesses, this is not an optional nice-to-have. Attributes carry the specifications, certifications, and technical details that buyers depend on. If those are wrong, incomplete, or inconsistent, the AI-generated content will be too.The lesson is simple: before you think about AI prompts and tools, focus on the data. Get the inputs right, and the outputs will follow.
At Start with Data we help distributors and manufacturers enrich their product data so AI works the way it should. If you would like to learn how we can support your product content strategy, get in touch with our team.