Siemens.
Browser-class agents configure the controller and reach order-ready.
Found & recommended by AI agents
Search-class agents touched the close in some runs; the full agent-fleet access profile lands in a later wave.
This page measures the commerce lane: can an agent find Siemens, and once it arrives, transact. Talent, after-sales, procurement, investor and press lanes run on different surfaces and are not yet measured.
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Browser agents configure and reach the order. A Siemens ID login is the wall.
We asked five kinds of AI agent to reach an order-ready state for a SIMATIC S7-1500 PLC with PROFINET on siemens.com. A browser agent ran the TIA Selection Tool end to end, created a device, opened the order list, and clicked "Continue to order" in all three runs, with full spec fidelity. A plain fetch and a script both kept the locked specs but reached only a generic SiePortal shell, not a product-resolved order. The order itself sits behind a Siemens ID login that no agent passed.
One label, many breeds. From a plain reader to an autonomous operator, the kinds behind ChatGPT, Perplexity, and Claude Code:
Scope. This is one product in one scope. Siemens spans automation, drives, mobility, smart infrastructure, and Healthineers, each with its own commercial surface.
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 source a component, does it route to Siemens?
Siemens comes up about four times in ten. The rest of the time, an agent recommends an alternative first.
Discoverability · 18-datapoint auditOnce an agent is on Siemens's site, can it place the order?
Search-class agents surface order-ready in some runs; the full agent-fleet access profile lands in a later wave.
What this does not yet cover
The agent reaches order-ready: product selected, price visible, no login. The actual checkout, cart and payment, sits behind a login wall and was not part of the test. The agentic purchase itself is unproven.
Evidence · 70 / 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.
What this means for Siemens.
On product discovery, Siemens is not failing to be read, the SIMATIC spec holds from raw HTTP to the configurator. It stalls at the close: the order is reachable but hard-gated by a Siemens ID login, and the simplest agents reach only a generic portal shell. The login and the shell are the gap.
Make the close callable, not a cleaner catalog. A structured, agent-reachable order path that does not dead-end at a login wall, and product-resolved surfaces so HTTP and coding agents reach the same order state the browser does, instead of a generic portal shell.
The proof isn't a richer configurator. It's agents completing a product-resolved order across classes, not only the browser, re-measured each wave.
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No intermediary stands between agents and Siemens. The gap is being found, not the channel.
Industrial controller procurement reaches siemens.com directly through SiePortal and the TIA Selection Tool. No marketplace or distributor structurally displaces the brand for this product class; the open question measured here is the surface and the login gate, not the channel.
Hyperize-selected tasks.
One task from the public sector grid. Task list is frozen before each wave runs.
Siemens SIMATIC S7-1500 PLC
- Close state
- order-ready
- Bottleneck
- Specs fully readable; the product-resolved order is reachable via the configurator but hard-gated by a Siemens ID login. Simple/programmatic agents stall on a generic SiePortal shell.
Fairness note
Wave 2 (a fleet wave Industrials). Single measured task (SIMATIC S7-1500 PLC, order_ready close state, login-gated). AI Visibility 44.03 (18/18 valid datapoints across 3 providers); AI Usability derived from the a fleet wave access profile. Confidence C, single task; 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
22 May 2026
Wave · ProtocolWAVE-Q2-2026-W2
ars-methodology/v1.1
First v3 measurement. AI Visibility 44.03 (18/18 valid datapoints, 3 providers, DE). AI Usability derived from a fleet wave: browser agents reached order_ready 3/3 via the TIA Selection Tool, HTTP/coding partial (specs held, close shell-level), ACT reached order-ready through the configurator with the final order submission gated by a Siemens ID login. Confidence C, single task.
Evidence and provenance.
Public methodology references and internal evidence pointers behind every claim above.
- [S1]Accessed · 22 May 2026
Gate-1 audit run · Siemens Wave Q2 2026
Internal · Hyperize evidence
- · AI Visibility score (18/18 valid datapoints across 3 providers)
- · the close state reached (order_ready)
- [S2]Accessed · 22 May 2026
Hyperize fleet · a fleet wave Industrials (access profile)
Internal · Hyperize evidence
- · how each kind of agent fared (text/code partial, browser 3/3, act order-reached + Siemens ID gate)
- · the close state reached (order_ready, login-gated)
- · the tested product (SIMATIC S7-1500 with PROFINET)
- Accessed · 22 May 2026
Public · hyperize.ai
- · fairness declaration
- · Third-Party Interception framing
Read the doctrine. Challenge the score. Extend the slate.
Task Selection.
The fairness doctrine behind the slate above. Five failure modes, six criteria, public before each wave.
Read ChallengeDisagree with this score.
Send evidence under public Fairness Review. Failed reviews are documented with the named failure mode.
Challenge ExtendSubmit your own task.
Open Surface Run · additive measurement. The Hyperize-selected slate stays frozen; your task gets the same methodology.
SubmitEditorial coverage
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.
