AI agents are starting to browse, compare, and buy on behalf of people — not someday, now. Mainstream browsers now ship agentic-browsing audits, and checkout protocols built for AI agents are already live inside mainstream ecommerce platforms. The question for any consumer-facing business isn’t “should we care about this?” anymore. It’s “can an agent actually use our site today?”
We ran that question against a real cohort: 219 lighting companies — 147 independent showrooms and 72 brands — using a scanner that checks whether an AI agent can read a site, find what it sells, and complete a purchase. This is what that cohort looks like — not a certified claim about the entire industry. Read the note below before the findings.
Before you read this
Neural Partners builds and migrates websites for lighting companies, and this data comes from a free readiness scan we offer as part of that business. The sample is our own first-party contact list for this industry — not a neutral census or a stratified sample (full caveats in the Methodology section below). We are publishing the findings that cut against platforms we compete with alongside the ones that do not, because we think that is more useful to you than a comfortable summary — but weigh the source before the rest of the report.
Executive summary
Four numbers carry the story:
Invisible to agents. These companies return nothing an AI agent can read — most sit behind an active bot wall.
Average score among the companies agents can actually see. Mid, not hopeless.
Zero way to transact. No commerce or interoperability protocol of any kind.
Inherited, not built. Every agent-buyable company got there via its platform, not its own work.
This isn’t one score. It’s two groups. Roughly a third of the companies we scanned can’t be read by an agent at all — usually because their website platform blocks automated visitors before an agent ever gets a look. The other two-thirds average a respectable 57, but that readiness is almost entirely inherited: every top-scoring company we found runs the same class of modern commerce platform, and every company capable of completing an agent-driven purchase got there by default, not by design. (This describes our scanned cohort, not a certified industry census — see the disclosure above and the Methodology section below.)
Nobody in this cohort has done intentional agent-interoperability work — the technical layer that would let an agent negotiate directly with a company’s own systems sits at zero across the board. The category is wide open. Agent-readiness in this industry, today, is rented, not built. Whoever helps a company own it — or unknowingly locks it into a platform that caps it — sets that company’s ceiling.
The shape of the industry: three segments, not one score
The single biggest correction we made before publishing this report: a company that’s invisible to AI agents isn’t a low score. It’s a different state entirely, and folding it into an average distorts everything downstream. Separate it out, and the real shape of the industry appears.
| Segment | n | Scored (agents can read it) | Agent-blind | Unreachable | Invisible share |
|---|---|---|---|---|---|
| Showrooms | 147 | 89 | 49 | 9 | 39% |
| Brands / OEM | 72 | 55 | 11 | 6 | 24% |
| Combined | 219 | 144 | 60 | 15 | 34% |
Of the 60 agent-blind companies, 46 are actively walling out automated visitors with a bot-detection page — the kind that serves a CAPTCHA instead of content — 11 return no meaningful content without JavaScript, and 3 serve an empty page shell. Not one of the 219 companies opted out deliberately through the standard, polite mechanism (robots.txt). This invisibility is unintentional. That’s exactly the point.
The segment finding, corrected
Showrooms and brands score almost identically once you isolate the companies agents can actually see — 58 versus 57. The real difference isn’t the quality of the work; it’s that 39% of showrooms are invisible to agents versus 24% of brands, and the large majority of bot walls in this cohort sit on showroom sites. Showrooms aren’t building worse websites — they’re more likely to be running on platforms that wall out automated visitors by default.
Five findings
The publishable core of this study — five findings, each anonymized at the platform-tier level, each carrying its sample size, each checked against raw per-check evidence.
Finding 1 — More than a quarter of the industry is invisible to AI agents, and it’s inherited, not chosen
60 of 219 companies (27%) return nothing an agent can read — 34% counting domains that were unreachable outright. The dominant cause isn’t neglect. It’s active bot walls (46 companies) that the business itself almost certainly doesn’t know it has. And invisibility tracks the website platform, sharply:
| Platform tier | n | Share invisible to agents |
|---|---|---|
| General modern commerce platforms | 83 | 4% |
| Custom-built sites | 17 | 24% |
| Unknown / no detected platform | 49 | 45% |
| Vertical-specific industry platforms | 70 | 66% |
On a general modern commerce platform, roughly 1 company in 25 is invisible to agents. On a vertical, industry-specific platform, it’s 2 in 3. Custom-built (n=17) and unknown-platform (n=49) tiers are modest samples — read their percentages as directional, not precise.
Finding 2 — Of the companies agents can read, nearly three in four expose zero way to transact
106 of 144 scored companies pass none of the four completion protocols we checked for. And the split by platform tier is stark: zero-completion runs 52% on general modern commerce platforms — and 100% on every other tier. Not one custom-built, unknown-platform, or vertical-platform site in our scored set can complete an agent-driven transaction.
Finding 3 — The industry can be found, but not bought from
Discovery — whether an agent can locate and read a company — averages 70 out of 100. Completion — whether an agent can act on what it found — averages just 39. 27% of scored companies sit in what we call the “found but not buyable” quadrant: an agent can locate them, read them, summarize them, and then hits a dead end at the exact moment intent becomes a transaction. The loss concentrates precisely where it’s most expensive.
Finding 4 — Every agent-buyable company got there by accident
38 companies (26% of scored) publish a commerce profile that lets an agent complete a purchase (the protocol is called UCP) — the only agent-commerce protocol with real adoption in this cohort. Every single one of those 38 runs a general modern commerce platform whose vendor ships that capability by default. The profiles are real and functional — most are payment-capable — but none were built by the company itself.
Worth being precise about what this does and doesn’t prove: since UCP is the only completion protocol we found in the wild, and one platform ships it automatically, “100% inherited” partly reflects that construction — it’s close to true by definition, not just by observation. What it does show cleanly: no company in this cohort has done intentional agent-interoperability work. The deeper layer that would let an agent negotiate directly with a company’s own systems (A2A) sits at 0 of 144, and a browser-native equivalent (WebMCP) shows up exactly once, unverified. The leaders lead because of a platform choice, not because they built something the others didn’t.
Finding 5 — The gap between mid-pack and leader is a weekend of work, not a rebuild
18 scored companies (13%) already clear 80 out of 100 — proof the ceiling is reachable with tooling that exists today. What separates the middle of the pack from them is mostly missing signals of intent, not missing infrastructure: 67% of scored companies have no llms.txt file, 49% lack basic meta and social tags, 47% have no structured data markup, and 31% fail all three at once.
These are files and markup, not replatforming projects — which is also why “wait and see” is the most expensive strategy on the table. The fixes are cheap for you, and just as cheap for whoever competes with you.
The good news: where the industry already wins
The same data, read as opportunity. Nothing here is spin — each point is the honest upside of a verified number.
Discovery is mostly a solved problem
Scored companies average 70/100 on Discovery. 98% permit AI crawlers, 100% serve identical content to bots and browsers, and 70% have a working sitemap. If a site is observable at all, agents can probably find and read it today.
The leaders are ordinary businesses
The 18 companies that scored 80+ are lamp stores and fixture brands — not technology companies. All of them run detected, mainstream commerce platforms whose vendor ships agent-readiness by default. The ceiling is commercially available right now, to anyone.
The biggest fixes are the cheapest
The most common failures — no llms.txt (67%), missing meta tags (49%), no structured data (47%) — are files and markup, not infrastructure. A focused week of work can move a mid-pack company into the top quartile.
Nobody has won yet
Intentional agent-interoperability adoption sits at 0% across the cohort. Every company reading this is, at worst, one platform decision and one sprint behind the leader — and at best, first.
What the industry is already doing right
Most of what this study measures pays off today, for humans, regardless of when agents arrive in force. Read through that lens, the scored companies are quietly better than the readiness framing alone suggests:
| Human-web fundamental | Share of scored companies |
|---|---|
| Open to crawlers, no cloaking (trust basics) | 98–100% |
| Accessible, usable forms (assistive tech works) | 68% |
| 3 of 4 human-web fundamentals present (a11y, meta, structured data, sitemap) | 51% |
| SEO-ready today (structured data + meta together) | 36% |
| Perfect Discovery score | 17% |
The underdog finding
Independent showrooms out-fundamental the national brands. Showrooms beat brands on accessibility (72% vs. 62%), match them on overall score (58 vs. 57), and hold the majority of the top-quartile spots. Where their sites are observable at all, small lighting retailers are punching at — and above — national-brand level. The industry’s problem isn’t craft. It’s that a third of that craft sits behind platforms agents simply can’t see.
The ladder: where agent-readiness fits, and what comes first
Agent-readiness isn’t a separate discipline that arrives someday. It’s the top rungs of a ladder this industry is already climbing — and every rung pays for itself before the next one matters.
| Rung | What it means | Pays off today via | Cohort today |
|---|---|---|---|
| 1 · Work for humans | Accessible markup, labeled forms, stable layout | Conversion, usability, accessibility exposure | Forms 98% · a11y 68% |
| 2 · Be findable | Sitemap, meta / social tags, structured data | Search rankings and rich results, social sharing | Sitemap 70% · meta 51% · structured data 53% |
| 3 · Be machine-readable | llms.txt, machine-readable product data | AI search citations, arriving now | llms.txt 33% |
| 4 · Be transactable | Commerce & interoperability protocols — agents can act and buy | Agentic commerce, the frontier | 26% commerce profile · 0% deep interoperability |
The cohort’s shape: solid on rung one, halfway up rung two, a cliff at rungs three and four. That’s the honest pitch to a company that isn’t ready to think about “agentic strategy” yet — climb the next rung. It pays for itself now, and it happens to be the same ladder.
The hard truths
The same data, read without comfort. This is what makes the optimistic view credible.
- Dozens of businesses are unknowingly paying a vendor to make them invisible. Bot walls aren’t a decision a company makes — they ship with the platform. Most affected businesses have no idea an AI agent, or the crawler behind any AI search index, sees a CAPTCHA page where their storefront should be.
- The vertical-platform trap is structural. A company on a vertical, industry-specific platform can’t fix its agent-readiness with effort alone: two-thirds invisibility and total zero-completion are properties of the platform, not the tenant. The honest advice to those companies is to migrate or push their vendor — the data doesn’t support a third option.
- Readiness in this industry is dangerously concentrated. Nearly all of the agent-readiness we found — the top scorers, every agent-buyable profile, most machine-readable product data — traces back to one commerce-platform ecosystem. This industry hasn’t broadly adopted agentic commerce; one vendor shipped it to a large share of its customers at once. That’s a finding and a fragility: readiness this concentrated is one platform decision away from disappearing.
- This section and the one before it point the same direction, on purpose. Both halves of this report — the optimistic read and this one — end up supporting the same conclusion: the ceiling is reachable and undefended. That’s not neutral academic framing so much as this cohort’s actual shape, but you should know both sections are built toward that conclusion rather than presented as detached analysis.
- Completion is worse than the 39 average suggests. Two of its inputs — agent-fillable forms and followable actions, both around 98% — are saturated and no longer discriminate well; they quietly inflate the blended score. Strip them out and the real “can an agent act here” picture, running through commerce and interoperability protocols alone, is bleaker.
- Agent traffic is arriving before the industry is ready. Mainstream browsers now ship agentic-browsing audits; agentic checkout protocols are already live in mainstream commerce platforms. The gap this report measures is being monetized right now by whoever is ready — and more than a quarter of this industry isn’t even visible for the race.
Methodology & what this data can — and can’t — support
We believe in publishing limitations alongside findings. Here is what this study supports, and where it should be read with care.
- Sample frame. 219 real companies drawn from Neural Partners’ own first-party contact list for this industry — our prospect list, not a neutral census or a stratified industry sample. Every figure in this report describes this specific cohort, not a certified industry rate. We plan to sample from a neutral frame (an industry association directory, for example) for future editions.
- Scanner design. A robots-honoring scanner made roughly a dozen read-only requests per company. We rechecked every aggregate in this report against the underlying per-check evidence before publishing, to catch aggregation errors — that is an internal QA pass, not third-party verification, and we are naming it as such rather than letting the phrase imply more than it does.
- Saturated signals. Two of the Completion inputs (agent-fillable forms, followable actions) sit near 98% and no longer discriminate well; they mechanically inflate the blended Completion average of 39. We have not recomputed Completion with these signals removed — that recomputation would produce a stronger, more honest number, and we are flagging it as unfinished rather than letting 39 quietly stand in for it. Treat 39 as a soft upper bound on transactability, not a precise estimate.
- Small subgroups. Several platform-tier breakdowns (Finding 1) rest on samples as small as n=17. We report them because the pattern is large and consistent, but treat the exact percentages in these cells as directional, not precise.
- Platform-dependent checks. Our machine-readable product-data check can currently only find product pages on platforms that expose them in a standard way. Its 57% pass rate describes “of the stores we could sample,” never the industry as a whole.
- Static proxies. Our accessibility and layout-stability checks are static heuristics aligned with — but not identical to — tools like Chrome Lighthouse. We label them as estimates, not lab results.
- Low-confidence result. Our single WebMCP detection (roughly 1% of the cohort) is unverified and low-confidence. We treat it as effectively zero until independently confirmed.
- Point-in-time. This is a single-day scan. A company’s bot-facing behavior can and does change day to day — we recommend reading any one snapshot as a photo, not a trend, and we plan to re-scan quarterly.
- Survivorship note. Our 100% “no cloaking” pass rate, by definition, excludes the companies most likely to treat automated visitors differently — the ones we couldn’t observe at all.
What this means for you
If you sell lighting — as a showroom or a brand — three things follow directly from this data:
- Find out which industry you’re in. If your site is agent-blind, that’s almost certainly a platform default you didn’t choose, not a reflection of your business. It’s fixable, and usually faster than it sounds.
- The ceiling is closer than it looks. Companies already at 80+ are ordinary lighting businesses on commercially available platforms. Most of the gap between mid-pack and leader is markup and files, not a rebuild.
- Nobody has built readiness on purpose yet. Intentional agent-interoperability work is at zero across this entire cohort — not because it’s hard, but because nobody’s tried. That’s an open question, not a settled one.
As disclosed at the start of this report
Neural Partners builds and migrates lighting-industry websites and offers the free scan this data comes from. Full methodology and sample caveats are in the Methodology section above.
Want your own score? Run the free Agentic Readiness Check to see exactly where your site lands — it’s the same scan behind this report. The print-ready PDF is up top.