Garbage In, Garbage Out: Structuring Your Source Data
The quality of AI-generated copy is a direct function of the quality of the source data provided. An LLM cannot invent technical specifications that do not exist in your product information management (PIM) system. Attempting to generate descriptions from a sparse PIM with inconsistent attribute fields will result in generic, unhelpful content. The foundational step is a data quality initiative. This involves enriching product data with structured attributes: dimensions, weight, material composition, country of origin, performance ratings, and compatibility information. This structured data, not prose, is the most reliable input for the AI.
Alongside structured data, the AI needs a clear definition of your brand voice. This cannot be a vague mission statement about 'quality and service'. It must be a concrete, machine-readable style guide. This guide should specify: a target reading age, preferred terminology (e.g., 'fixings' vs 'fasteners'), sentence length parameters, and a list of 'negative constraints' - words or phrases to be strictly avoided. For example, a premium brand might forbid words like 'cheap' or 'basic', while a technical distributor would want to ensure marketing adjectives do not obscure critical specifications. This brand voice document, combined with your best existing product descriptions as few-shot examples, provides the guardrails for the generation process.
The technical architecture should support this editorial process. Using vendor APIs, such as those from OpenAI or Anthropic, should be integrated directly into your PIM or commerce platform like Adobe Commerce. Crucially, all generated copy must be written to a dedicated 'draft' or 'staging' attribute, never directly to the live product description field. This creates an explicit separation between machine generation and human approval, allowing for review and iteration within a controlled environment before anything is published to the live site. This setup prevents accidental publication of unvetted content and provides a clear workflow for the editorial team.
"The risk with AI product copy is not gibberish. It is a fluent, well-formed sentence that calls a C16 timber a C24. By the time a customer flags it, you have already shipped wrong."
The Three-Tier Review Process for AI-Generated Copy
A scalable workflow for vetting AI copy can be organised into a three-tier review process. The first tier is Bulk Generation and Triage. Here, a merchandiser or junior editor uses the system to generate copy for an entire product category. Their role is not to perform a deep line-edit, but to conduct a rapid review to catch major failures: nonsensical paragraphs, formatting errors, or outputs that completely miss the mark. They are answering the question: 'Is this draft usable as a starting point?'. This ensures that the more senior reviewers are not wasting time on complete non-starters.
The second and most critical tier is Subject Matter Expert (SME) Review. This is where your most valuable asset, product knowledge, is applied. The draft copy is passed to a category manager, a technical buyer, or an engineer who has deep domain expertise. Their sole focus is factual accuracy. They are not checking for grammar or tone; they are verifying that the technical specifications, compatibility claims, and use cases described are correct. For a business in industrial distribution, this is the person who understands the nuances between different grades of stainless steel or the load ratings of specific components. This step is the primary defence against the trust-eroding errors mentioned earlier and it is non-negotiable for any serious B2B operation.
The third tier is Final Editorial Polish. Once the copy is confirmed to be factually accurate by the SME, it moves to a copywriter or editor. This person is the guardian of the brand voice. They perform a final check for grammar, spelling, and adherence to the style guide. They ensure the copy reads well, is persuasive, and fits within the layout of the product detail page. Their feedback, along with corrections from the SME review, should be collected and used to refine the AI prompts and style guide over time. This continuous feedback loop makes the generation process progressively more accurate and efficient, reducing the review burden with each cycle.
Measuring What Matters: From Word Count to Conversion Rate
The most immediate metric for success is a dramatic increase in editorial productivity. Based on iWeb's internal benchmarks from projects, an AI-assisted workflow can make a single editor as productive as five to ten manual copywriters. Measuring the number of SKUs processed per week provides a clear efficiency metric and helps build the business case. This allows you to quantify the return on investment by comparing the cost of the software and review process against the fully-loaded cost of a large, manual copywriting team.
However, efficiency is not the ultimate goal; commercial effectiveness is. The true measure of success lies in the impact on key commerce metrics. Organisations should implement A/B testing to compare the performance of AI-enriched product pages against control groups (e.g., pages with old, minimal copy). Key metrics to track include a reduction in on-page bounce rates, an increase in 'add to basket' rates, and ultimately, a rise in the overall conversion rate. Gauging performance against wider industry data, such as IMRG retail and B2B benchmarks, can provide valuable context for what constitutes a meaningful improvement.
Beyond direct conversion, there are significant secondary benefits that should be tracked. High-quality, unique product descriptions have a direct positive impact on search engine optimisation (SEO), leading to increased organic traffic as pages are no longer penalised for using duplicate manufacturer content. Furthermore, comprehensive descriptions that answer common customer questions before they are asked can lead to a measurable reduction in inbound calls to customer service centres and a lower rate of product returns due to mis-ordering. These are real, quantifiable cost savings that contribute directly to the profitability of the digital channel.
