We ‘ve got used to reading through product information on screens of all sizes. We look at lists, filters, thumbnails, comparison tables, and that’s largely the world which still matters. However, it no longer tells the whole story. We are increasingly talking to our machines, whether it’s Alexa, Google Home, Siri, or a laptop. And we ask questions, we expect prompt and useful answers. And remember, in most cases, we’re not asking an array of smart devices – we only hear one, so we need to be able to trust in that information.
The increase in voice-enabled devices and AI assistants is fundamentally reshaping how we discover, compare, and buy products. This shift is still emerging, but things are only going in one direction: if your product data isn’t structured for this kind of ‘conversation and context’ search, it’s not going to be selected, surfaced, or recommended. It will simply be discarded as an unreliable source.
So, let’s take the bull by the horns and examine the details of this shift: How it works, how it impacts your business and how you can adapt to it by simply managing your product information better.
From keywords to conversations
Traditional search patterns have driven businesses to think in terms of fragments: Quickly written phrases like “running shoes men”, “industrial valve 2 inch”, “cordless drill 18V” have been the assumed norm. But voice search flips that logic on its head.
When it comes to the command “Alexa!” or “Siri!”, people follow up by speaking in full sentences which are layered with intent:
- “Siri, what’s the best waterproof jacket for hiking in cold weather?”
- “Alexa, find me ISO-certified safety gloves available in bulk, delivered within two weeks.”
- “Hey, Google, which coffee grinder works best with a French press?”
These queries are richer, more specific, and far more demanding when it comes to the structure and breadth of the requested info. They require product data which explains multiple dimensions of query, among which you might find:
- What a product is for
- Who it’s suitable for
- What it’s compatible with
- Why it’s a good fit in a given context
- If a certain variant is best/most suitable
- When it’s available
- How much it costs (per unit)
AI assistants aren’t browsing. They’re deciding. And just relying on keyword-heavy descriptions and thin attribute sets just don’t cut the mustard any more.
The single-answer problem
A screen can show twenty or more options. A voice assistant usually gives one, and that transforms the competitive dynamic. Visibility is no longer about ranking somewhere high up on page one – it’s all about being the answer. To achieve that ‘triumph’, your product data needs to be unambiguous, complete, and machine-readable.
This raises the bar in three key areas:
- Precision beats persuasion
AI assistants will prioritise clarity over creativity. If key attributes are missing, inconsistent, or buried in free-form text, the product will simply be excluded from results.
- Context matters as much as specs
Among others, the following all heavily influence whether a product is selected by an AI assistant:
- Use cases
- Environments
- Certifications
- Compatibility
- Sustainability claims
- Constraints like price, availability or location
- Consistency: A deal-making/breaking factor
Conflicting data across channels degrades trust, and not only for the human user. An AI system’s algorithms reward clean and stable information sources.
Structured information beats storytelling
Voice and AI systems rely to an enormous extent on structured data to interpret intent and extract the best answers. This is where an awful lot of product catalogues fall apart.
Providing unstructured descriptions like “ideal for professional use” or “perfect for demanding environments” mean nothing to an AI assistant unless they’re backed by explicit, standardised attributes like certifications, materials, tolerances, ratings, and operating conditions.
Structured data means you can provide useful, relevant and accurate information in response to the following examples:
- Accurate comparison (“Which of these is quieter?”)
- Confident recommendations (“This is suitable for outdoor use”)
- Clear explanations (“It works with your existing system because…”)
Using schema markup, controlled vocabularies, and normalised attributes are no longer just desirable ‘SEO extras’ but explicit requirements for AI visibility.
PIM as the foundation for conversational commerce
This is where Product Information Management gives you a serious return on investment. A PIM system provides the layer of structure, governance, and enrichment which voice and AI assistants depend on. A well-implemented PIM solution enables allows businesses to fix the fundamentals of product data management by:
- Centralising the truth
One authoritative, definitive product record, shared across eCommerce platforms, marketplaces, search engines, voice assistants, and emerging AI channels and tools.
- Enriching for search intent
Beyond the core specs, PIM supports use cases, compatibility rules, certifications, sustainability data, and contextual attributes which map directly to spoken queries.
- Enforcing consistency
Controlled vocabularies ensure that, for instance, labels for colour like “navy”, “dark blue”, and “midnight” don’t fragment discoverability.
- Modelling relationships
Products don’t exist in isolation and a PIM captures connections like accessories, replacements or alternatives. AI assistants can then use these to answer follow-up questions and comparisons.
- Scaling without chaos
As new channels, assistants, and interfaces emerge, structured product data can be reused without having to reinvent the wheel.
In brief, a PIM converts your catalogue into a comprehensive knowledge base.
B2C and B2B: different questions, but the same problem
In consumer-led commerce, voice search tends to lean towards intentions geared towards convenience and lifestyle. In B2B, it moves further towards hard facts, precision and risk reduction. However, the underlying information requirement is identical: Depth and accuracy of relevant data.
So, a consumer might ask for “eco-friendly laundry detergent near me”. A procurement manager might ask for “REACH-compliant components with a two-week lead time”. In both cases, the assistant can only work with what your data explicitly states. If that information lives in PDFs, emails, or inconsistent spreadsheets, it might as well not exist.
Preparing for what comes next
Voice search is only the next iteration of data-driven purchasing. Generative AI assistants are already moving from answering questions to guiding decision-making: For example, comparing options, explaining trade-offs, and recommending next steps.
Those businesses which continue to treat product data as basically static content will find themselves struggling to compete. Those that treat it as a foundational element of a structured and evolving infrastructure will be ready and future-proofed.
The practical steps towards this offer a simple but powerful payoff:
- Clean data
- Richer and more in-depth attributes
- More robust product data governance
- PIM at the centre, as the ‘golden record’
It’s what happens when customers stop typing and start asking: You will be listened to! Get in touch with us today at Start with Data and we can talk at greater length about your ambitions and how we can help you to achieve a state where structured, enriched product data and PIM will guarantee your catalogue stays visible, relevant, and selectable in voice- and AI-driven commerce.