Henkel.
Henkel is findable.
Found & recommended by AI agents
Can an agent use the surface to get the job done
This page measures two lanes. Commerce: can an agent find Henkel, and once it arrives, transact. Talent: can a candidate's agent find an open engineering role on Henkel's own career surface and prepare the application. After-sales, procurement, investor and press lanes run on different surfaces and are not yet measured.
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The product page answers the Color question. Pack size and dosage stay hidden.
We tested how AI agents handle Persil Color Megaperls, Henkel's color-care detergent. Our audit ran 18 measurements across three AI providers, in German, with zero errors. The AI visibility score: 35.28 of 100. Automated agents then read the product page on persil.de.
The page answers part of the question. Agents found the JETZT KAUFEN button and the clear split between Color for colored laundry and Universal for whites. Two details never surfaced: the 40 wash-load pack at 1.4 kg and the 75 ml (75 g) dosage. An agent checking pack size or dosage gets a partial answer.
The full agent test, including browser agents, has not run yet. A follow-up measurement is scheduled. The purchase itself runs through dm, Rossmann, Edeka, REWE, Amazon, and Kaufland.
One label, many breeds. From a plain reader to an autonomous operator, the kinds behind ChatGPT, Perplexity, and Claude Code:
Found, and able to transact?
Two questions, measured separately. A brand can be recommended and still un-buyable, or perfectly buyable and never found.
When someone asks an agent to buy a product, does it route to Henkel?
Henkel comes up about a third of the time. The rest of the time, an agent recommends an alternative first.
Discoverability · 18-datapoint auditOnce an agent is on Henkel's site, can it verify the color-care chemistry on the product page?
Plain reader and Search assistant and Coding agent reach the documentation in some runs; other agent classes do not.
text / search / code-class agents reach the documentation only in some runs; agents are blocked, no transaction follows · breakdown aboveEvidence · 65 / 100 A measure of how provable and consistent the result is, grounded in cross-method ground-truth agreement (the methods that ran returned the same price), not a separate measured run. A confidence layer on the two scores above, not a third sales axis.
The talent lane.
A candidate's agent, sent to find an open software-engineering role at Henkel and prepare the application. Same brand, second surface: the career portal.
Readable posting, unresolved apply step.
Henkel's career-CMS detail page serves the full job posting — title, req ID 25088672, location, and requirements — to a plain HTTP GET; but the actual application form lives on a separate Cornerstone OnDemand ATS (henkel.csod.com) that renders as an empty client-side shell, so an agent operating without JavaScript can read the posting but not see what the application form requires.
Furthest close-state reached: spec match · frozen task locked 2026-07-02
The score above is provisional where a class shows untested: untested classes are excluded from the calculation, so the pending check can only confirm or raise the value, never silently lower it.
AI Visibility · in measurement — the audit run for this lane has not landed yet. No Talent-lane composite is shown until both axes are measured.
What this does not yet cover
This measurement covers only discovery and reading of the posting via GET requests; no application was submitted, no login was attempted, and no form fields were filled or posted. The measured posting (Software Developer AI Driven Solutions, req 25088672) is located in Cairo, Egypt, not in the DACH region — a documented 2026-07-02 re-lock pass checked 9 distinct DACH-region software/IT/security requisitions and found all 9 expired (genuine 404s), so no DACH-region posting could be substituted. The Cornerstone application-form contents (login requirement, CV/cover-letter fields, form length) are unread because the ATS renders client-side; this requires a browser-based follow-up (Phase C), not a GET-only method.
Fairness note
Henkel operates its career surface directly: job detail pages, including the measured posting (Req 25088672, locked 2026-07-02), are served fully readable to a plain HTTP request, which is a clean architectural baseline. The application flow itself runs on a separate Cornerstone OnDemand system that was assessed only without JavaScript; browser-based agents were not tested, so the finding describes what a non-rendering agent sees, not a hard limit for all agents. No application was submitted, no form was filled, and no overall score is shown because the visibility measurement is still in progress.
What this means for Henkel.
On product discovery, Henkel is not lacking findability — AI Visibility 35.28 places Henkel mid-pack across openai/perplexity/anthropic. The Color-vs-Universal distinction surfaces cleanly on persil.de; the brand handles the spec-fidelity disambiguation between bleach-free Color and whites-Universal that the Persil portfolio is structured around. The gap is twofold. First: the 40 WL Beutel / 1.4 kg SKU detail and 75 ml dosage row do not surface on the locked SKU page, so an agent confirming pack-size or dosage gets only partial answers from the brand surface. Second: the German FMCG retail mode — color-care detergent queries route through dm / Rossmann / Edeka / REWE shelf listings or Amazon search rather than persil.de directly. The Henkel surface owns the product claim and the dosage chart — the retail intermediary owns the price, the pack-size selection, and the purchase. If the agent reaches persil.de, the question is whether the locked claim (Color = bleach-free AND 30-60°C AND Megaperls 40 WL) surfaces accurately on the product page.
What changes the outcome is filling the SKU-detail gap on the brand-own surface. Phase 2 found the page reaches with Color-line clarity, but pack-size / dosage details remain off-page. Named Hyperize offerings here: Reference Pages that make the 40 WL Beutel SKU + 75 ml dosage agent-readable on persil.de, plus Answer Pages on "Welches Color-Waschmittel für bunte Wäsche?" that route directly to the brand-own claim verification rather than to the dm / REWE / Edeka shelf listings.
The proof isn't more content. It's a repeatable lift in whether agents find, verify, cite, recommend, and complete the brand journey directly — re-measured each wave.
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Marketplaces like Amazon capture 70% of shopping demand before Henkel.
Channel position derived from AI Visibility response analysis: the German FMCG retail mode applies — Persil Color Megaperls is captured by drugstores (dm, Rossmann), supermarkets (Edeka, REWE, Kaufland), and Amazon.
Intermediaries dm · Rossmann · Edeka · REWE · Amazon · Kaufland
Hyperize-selected tasks.
One task from the public sector grid. Task list is frozen before each wave runs.
Persil Color Megaperls (40 WL Beutel, 1.4 kg, Color-Waschmittel 20-60°C)
- Close state
- the documentation
- Bottleneck
- Brand-own page reaches with Color-line clarity, but 40 WL / 1.4 kg / 75 ml dosage details do not surface on the locked SKU page; FMCG retail (dm / Rossmann / Edeka / REWE / Amazon / Kaufland) captures the close.
Fairness note
Wave 7 Q2 2026 partial measurement. Single task (Henkel Persil Color Megaperls, documentation_ready close, 40 WL Beutel SKU, locked spec: Color = bleach-free + 30-60°C + Megaperls-format). AI Visibility from the audit pass (35.28, 18/18 valid datapoints across openai/perplexity/anthropic, DE language, 0 errors). AI Usability derived from a fleet wave Phase 2 partial (HTTP + Coding partial; Phase 3 browser + Phase 4 ACT not executed this wave). Fairness Review pending the sector fairness grid.
How the score was produced.
Discoverability is audit-pipeline-derived. 3-provider sample (openai, perplexity, anthropic), 3 query variants per task, 2 runs per variant · 18 valid datapoints scored against a five-state handoff cascade. [S1]
AI Usability is derived from the access-profile above (usability-derivation/v1): how far the best agent reached (close state) modulated by how many agent classes succeeded. The per-class profile is the truth; the score is a reproducible summary of it, not a separate rating. Fleet phases (HTTP / Coding / Browser / ACT) produce the profile. [S2]
Agent Success Score = (AI Visibility × 0.20) + (AI Usability × 0.70) + (Evidence × 0.10)
On a 0–100 scale, displayed 0–10. AI Usability bundles the agent's reach + completion; AI Visibility is audit-derived discoverability. Weighting is public; the per-prompt derivation is not.
Measurement scope
Confidence C · one measured task on a 3-provider track (openai, perplexity, anthropic). Confidence promotes to B with a second task plus a fourth provider on the next wave.
Measurement timeline.
Each wave appends; nothing overwrites. Frozen Wave Rule.
- Entry · 01
25 May 2026
Wave · ProtocolWAVE-Q2-2026-W7-CONSUMER-GOODS
ars-methodology/v1.1
Wave 7 partial measurement landed. AI Visibility pass complete (D 35.28, 18/18 valid, 0 errors). a fleet wave Phase 1 ceiling + Phase 2 HTTP+coding committed_partial on Evidence completeness; Phase 3 browser + Phase 4 ACT not executed this wave. Brand-own page reaches with Color/Universal disambiguation, but the 40 WL / 1.4 kg SKU detail and 75 ml dosage row did not surface on the locked SKU page.
Evidence and provenance.
Public methodology references and internal evidence pointers behind every claim above.
- [S1]Accessed · 25 May 2026
AI Visibility audit · Henkel Wave Q2 2026 (dispatched 2026-05-25)
Internal · Hyperize evidence
- · AI Visibility score
- · the close state reached (documentation_ready)
- [S2]Accessed · 25 May 2026
Agent-fleet run · consumer goods · Henkel phase 1-4 (dispatched 2026-05-25)
Internal · Hyperize evidence
- · the per-class access profile (Phase 2 partial)
- Accessed · 25 May 2026
Public · hyperize.ai
- · fairness declaration
- · Third-Party Interception framing
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Task Selection.
The fairness doctrine behind the slate above. Five failure modes, six criteria, public before each wave.
Read ChallengeDisagree with this score.
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The DAX 40 Agent Success Index is a point-in-time snapshot of the agent-success of public digital touchpoints. Results are not statements about product quality, company performance, service quality, or the legal obligations of the brands named. Brand names and logos remain the property of their respective owners and are used solely for identification and reporting purposes in the context of editorial coverage (§ 23 MarkenG, Art. 5 GG).
Brands wishing to respond, engage, or correct a factual error may contact hello@hyperize.ai. Responses received are published in full alongside the findings. Full methodology and editorial-coverage notice: coverage statement.
