This article is for manufacturers of standard industrial products sold from a catalog, mostly through distributors: bearings, motion and fluid power components, connectors, sensors, fasteners, enclosures, lubricants and similar lines. If you build parts to a customer’s drawing, our article on how contract manufacturers win RFQs through AI search fits better. Makers of pumps, compressors and valves can read how equipment makers reach more bid lists.
The short version
- Engineers are letting AI into component choice: in a Newark survey (opens in a new tab), 86% of engineers trusted AI to play at least some role in selecting components, and 23% of those would trust it completely.
- The choice is made early: in a 2026 survey of 400 North American engineers commissioned by Weidmuller (opens in a new tab), 43% said most component selections are finalized in the concept or schematic phase.
- Your part competes inside huge catalogs: Grainger’s 2025 annual report lists more than 5,000 primary suppliers, about 2 million products in its high-touch business, about 13 million at Zoro and about 29 million at MonotaRO.
- Ordering is already digital: Fastenal’s digital footprint (opens in a new tab) was 61.6% of its sales in the second quarter of 2026, and Grainger’s CEO said electronic procurement is “closer to 40%” of its business.
- Engineers still verify: in the 2026 State of Marketing to Engineers (opens in a new tab) research by TREW Marketing and GlobalSpec, technical buyers rated their trust in AI answers 4.7 out of 10, and 77% still use a search engine more often than AI.
Who buys catalog industrial products, and what is one design-in worth?
Two buyers: the engineer who specifies a part into a design, and the maintenance or purchasing team that reorders it.
The first buyer is a design engineer choosing components for a new machine, product or line. Once a part is on the drawing and the bill of materials, it is bought for as long as the product is made, usually through a distributor. The second is a maintenance, repair and operations (MRO) buyer who needs a replacement fast, often by part number, and orders from whichever distributor has it in stock.
Most of that volume flows through distribution. The US Census Bureau estimates that merchant wholesalers of machinery, equipment and supplies sold $59,883 million of goods in July 2026 alone, seasonally adjusted, 12.1% more than a year earlier. Electrical wholesalers, which carry connectors, controls and sensors, sold $111,485 million.
There is no public benchmark for what one design-in is worth; it depends on the part’s price and the product’s volume and life. The mechanism is the point. Our inference: a single specification decision can produce years of repeat distributor orders that no salesperson ever sees, so the moment a part is chosen is worth far more than one sale. Parts built to a customer’s drawing follow a quote-led path instead, covered in how contract manufacturers win RFQs.
Where does AI already sit when engineers choose components?
At the start of research and in early component selection, used widely but checked against datasheets and publications.
Industry surveys show engineers bringing AI into the parts decision, carefully:
| Finding | Source |
|---|---|
| 69% of technical buyers use generative AI during the purchasing process | TREW Marketing and GlobalSpec, 2026 |
| 21% routinely research purchases on generative AI platforms, up 8 points in a year | TREW Marketing and GlobalSpec, 2026 |
| 31% routinely use industry directory websites | TREW Marketing and GlobalSpec, 2026 |
| 86% trust AI to play at least some role in component selection | Newark survey |
| 91% have used AI-based tools in their PCB design workflow | Weidmuller and EETech Research, 2026 |
| 79% say reliability and durability matter most in connectivity components, against 22% for price | Weidmuller and EETech Research, 2026 |
Newark’s respondents also described the limits: several wanted AI as “an enhanced search engine of sorts,” with every selection reviewed by an engineer, especially for safety-critical designs.
The design files themselves are moving online too. TraceParts (opens in a new tab), a platform for downloadable 3D models, says it hosts nearly 120 million searchable part numbers used by more than 6 million registered engineers and designers. An engineer who drops your model into a design has, in effect, chosen your part.
Purchasing is changing faster still. Gartner forecasts, as reported by Digital Commerce 360 (opens in a new tab), that AI agents will intermediate more than $15 trillion of business spending by 2028, relying on “verifiable data feeds.” That is a forecast, not a measurement, but it points at product data, the thing a catalog manufacturer controls.
What do engineers and MRO buyers ask AI about parts?
Questions built from specifications, standards, equivalents and availability, often naming a part number or a rival brand.
We wrote the examples below to show the shape of these questions; they are not captured from real buyers or assistants:
| Moment | Example question |
|---|---|
| Specification | “Inductive proximity sensor, 30 mm sensing range, IP69K, for a washdown food line” |
| Standard | “Pneumatic cylinders to ISO 15552 with 50 mm bore, stainless, which brands?” |
| Equivalent | “Drop-in replacement for a discontinued solenoid valve, part number …” |
| Compliance | “Which manufacturers make NSF H1 food-grade gear oil?” |
| Comparison | “Compare sealed deep-groove bearings from three brands for high-speed motors” |
| Availability | “Where can I buy this M12 connector in stock for next-day delivery?” |
Each question is a filter on attributes. If your sensing range, rating, standard, material or cross-reference only lives inside a PDF datasheet or a configurator, an assistant may not be able to match it. The equivalence question deserves special attention: when a rival’s part is discontinued or out of stock, the brand an assistant offers as the substitute picks up the business.
How does an AI answer turn into distributor orders?
Through a design-in or a replacement order, then repeat purchases through whichever distributor the buyer uses.
For a new design:
- Named. An engineer asks an assistant, a search engine or a distributor’s search box which parts meet a requirement.
- Checked. The engineer reads the datasheet, downloads the 3D model and often orders samples.
- Specified. The part goes onto the drawing and bill of materials. In the Weidmuller survey, 43% said most selections are finalized early, and another 26% said selection is iterative across stages.
- Bought for years. Production purchasing orders the specified part number, usually from distribution.
For a replacement, the path is shorter: an MRO buyer looks up the failed part or an equivalent, checks availability, and orders through a distributor website or an electronic procurement link. Grainger’s CEO told analysts, as reported by Digital Commerce 360 (opens in a new tab), that electronic procurement “is the biggest share we have at this point. Closer to 40%.” Fastenal said its digital footprint, its vending and inventory systems plus electronic business, reached 61.6% of sales, with electronic business daily sales up 12.6%.
The money comes from sell-through. Our inference: AI visibility matters most at the naming step for new designs and at the equivalence step for replacements; after that, availability and price at the distributor decide the order. Pumps, compressors and valves reach buyers through bid lists instead, as how equipment makers reach more bid lists explains.
What decides whether an assistant names your part?
Product facts it can read and confirm, plus who sells the part and whether it is in stock.
What the platforms document:
- Structured product data counts. OpenAI’s shopping help page (opens in a new tab) says ChatGPT considers “structured metadata from first-party and third-party providers (e.g., price, product description)” when choosing products, and ranks the merchants offering a product by “availability, price, quality, and whether they are the maker or primary seller of that item.” Merchants can apply to send a direct product feed.
- Google’s shopping answers draw on a product database. Google says AI Mode shopping uses its Shopping Graph (opens in a new tab), with more than 50 billion product listings, more than 2 billion of them refreshed every hour.
- Assistants search, then answer. OpenAI says ChatGPT search (opens in a new tab) rewrites a question into one or more targeted queries, and a site must allow its OAI-SearchBot crawler to be eligible.
These pages describe consumer shopping, and none of them explains how an industrial part is chosen for a design question. Applying them to components is our inference.
What our studies observed, across other industries:
- In our study of hidden searches, ChatGPT ran a mean of 3.7 searches per buyer question and looked for prices in 23.8% of its answers.
- In our pricing study, 61.9% of the software plan prices assistants quoted were fully faithful to the official page; most of the rest were real variants found elsewhere on the vendor’s own site or on third-party pages. Inconsistent prices across your own pages and your distributors’ pages give an assistant more than one answer to repeat.
- In our study of agent-readable websites, 45.8% of top homepages carried JSON-LD structured data, so many sites still give machines no structured description at all.
What we infer for catalog manufacturers: the trust factors are the ones an engineer already checks. Complete specifications in text, standards and approvals (UL, CE, ATEX, NSF, IP ratings) named precisely, cross-references to competitor and legacy part numbers, application notes, published reliability data and in-stock availability at named distributors.
What happens to sales when your parts are left out?
You lose design-ins you never hear about and replacement orders that go to the brand an assistant offered instead.
We found no public measurement of orders lost to AI absence, so here is the labeled reasoning:
- Selection happens before contact. The TREW and GlobalSpec research found that 62% of the technical buying journey happens online before a vendor is contacted, and the top reason buyers reach out at all is pricing or inventory questions (26%). A part missing at the research stage is rarely added later.
- Catalogs are crowded. With about 2 million products at Grainger alone and 85,000 net additions in 2025, a distributor’s own search and content decide which brand appears first. Grainger said its category reviews focus on “improving product search, organization, and content.”
- Familiarity tips close calls. In the same engineers’ survey, 53% said brand familiarity influenced their most recent purchase. We infer that being named in AI answers and technical publications builds the familiarity that later decides a tie.
- Wrong data does damage. An assistant that repeats an outdated rating or a discontinued part number can steer an engineer away. Finding the page an assistant took the outdated rating from comes first; our guide to fixing wrong brand information in AI answers shows how.
How does GEO work for a catalog manufacturer?
Generative engine optimization (GEO) makes your parts easy for AI assistants to find, match to a requirement and verify.
For a component maker, the work usually includes:
- One page per product family, with specs in text. Dimensions, ratings, materials, standards and operating ranges written as text and tables on the page, not only in PDF datasheets, configurators or images.
- Cross-reference and equivalence pages. Honest tables mapping your part numbers to legacy, discontinued and competitor numbers, with the differences stated.
- Consistent data in every catalog. The same attributes, names and part numbers on your site and at each distributor, from national houses to regional specialists, so an assistant does not find conflicting facts.
- Product feeds and structured data. Where platforms accept feeds or read structured product data, supply them accurately and keep them current.
- Design resources that can be found. 3D models, application notes and selection guides published where engineers and crawlers can reach them.
- Independent coverage. Application stories and technical articles in trade publications, which 76% of technical buyers routinely read. For why third-party coverage carries weight with assistants, see how brands build authority for AI search.
- Measurement. Ask a fixed set of specification, equivalence and where-to-buy questions across ChatGPT, Gemini, Perplexity, Copilot and Google’s AI features, repeat them, and record which parts and sources are named. Our article on designing AI visibility tracking explains why one answer is not enough.
None of this guarantees a mention. It makes your part the easiest one for an assistant to match and for an engineer to confirm. For the research on which product facts move AI choices, see what kind of product content AI shopping assistants prefer.
What can’t the data tell a component maker yet?
It shows engineers using AI in research, not how many design-ins or distributor orders AI answers cause.
- Trust is not attribution. The Newark and Weidmuller surveys measure attitudes and tool use. Neither links an AI answer to a specified part.
- Sponsors have interests. Newark is a distributor, Weidmuller sells components, TREW and GlobalSpec sell marketing to engineering firms, and TraceParts hosts CAD content for manufacturers.
- Platform pages describe consumer shopping. OpenAI and Google document how product results work for shoppers. How assistants handle technical part questions is not documented, and we have not measured it for industrial parts.
- Forecasts are forecasts. Gartner’s $15 trillion figure is a prediction about AI agents in purchasing, not today’s behavior.
Where should an industrial manufacturer start?
Start with your best-selling part families and ask assistants the specification and replacement questions your customers ask.
That first check shows whether your parts are named for the requirements they meet, whether assistants describe your ratings and part numbers correctly, which distributor and publication pages they rely on, and which brand they offer as the equivalent when yours is the one being replaced.
If your growth depends on being designed in and reordered through distribution, talk to us about a review of your product visibility. We will show which of your parts assistants name, where they get the facts, and which changes are most likely to put your part numbers on more drawings and distributor orders. What the follow-on work involves, such as cross-reference pages, consistent distributor data and repeated part-question checks, is outlined on our generative engine optimization service page.
Frequently asked questions
Should we prioritize our own site or our distributors’ product pages?
Both, with one set of facts. Assistants may cite either, and OpenAI says it ranks merchants partly on whether they are the maker or primary seller, so your own page should be the most complete version.
Do engineers trust AI to pick components?
Partly. 86% in Newark’s survey would let AI play some role, but trust in AI answers averaged 4.7 out of 10 in the TREW and GlobalSpec research, so engineers check datasheets before committing.
Are PDF datasheets enough?
Keep them, but put the key specifications on the page as text too. A requirement can only be matched to your part if the attribute can be read and compared.
Does this help with replacement and MRO orders?
Yes, especially for equivalence questions. Clear cross-reference pages and accurate stock information at distributors help an assistant offer your part when another brand’s part fails or is discontinued.
Sources
- Newark (2025), Newark research shows engineers now trust AI to select components (opens in a new tab)
- tED magazine (2026), Weidmuller USA releases new survey (opens in a new tab)
- W.W. Grainger (2026), 2025 Annual Report
- Digital Commerce 360 (2026-02-03), Grainger leans on AI, data and digital tools as Q4 sales rise and profits fall (opens in a new tab)
- Digital Commerce 360 (2026), Fastenal digital sales (opens in a new tab)
- TREW Marketing and GlobalSpec (2026), State of Marketing to Engineers research report (opens in a new tab)
- US Census Bureau (2026-09-10), Monthly Wholesale Trade: Sales and Inventories, July 2026
- TraceParts (n.d.), Understanding Industrial Engineers and Designers (Survey Report) (opens in a new tab)
- Digital Commerce 360 (2025-11-28), Gartner: AI agents will command $15 trillion in B2B purchases by 2028 (opens in a new tab)
- OpenAI Help Center (2026), Shopping with ChatGPT Search (opens in a new tab)
- OpenAI Help Center (2026), ChatGPT search (opens in a new tab)
- Google (2025-05-20), Shop with AI Mode, use AI to buy and try clothes on yourself virtually (opens in a new tab)
- Underneath (2026), The hidden searches AI assistants run before they answer
- Underneath (2026), How faithfully do AI assistants quote software prices?
- Underneath (2026), Do websites serve Markdown to AI agents?