1. Executive Summary
This report presents findings from a structured AI Engine Optimization (AEO) prompt study conducted across 50 manufacturing-relevant queries submitted to both ChatGPT (GPT-4o) and Google Gemini in March 2026. Queries spanned 14 thematic categories — from production scaling and traceability to digital thread and MES transformation — and were evaluated for vendor visibility, response type, and citation behavior.
The study reveals two fundamentally different AI response philosophies that have direct implications for how manufacturing technology vendors should approach content strategy and AEO investment. ChatGPT exhibits strong incumbency bias, returning a narrow set of enterprise-grade vendors with high consistency. Gemini, by contrast, draws from a broader and more diverse vendor ecosystem — offering meaningful visibility opportunities for smaller, specialized players.
A key methodological note: queries were run from Budapest, Hungary using fresh accounts and incognito browsing to minimize personalization effects and ensure baseline platform behavior. As discussed in the Observations section, there is evidence that Gemini may dynamically adjust response patterns over successive interactions.
Opportunity Note for Smaller Vendors
Google Gemini presents a genuine on-ramp for smaller MES vendors. Unlike ChatGPT — which overwhelmingly recycles a small pool of enterprise incumbents — Gemini surfaces niche and emerging tools (Fabrico, Evocon, Jidoka Tech, HiveMQ, HighByte, Manual.to, and many others) across a wide range of prompts. These vendors appear due to strong technical documentation, third-party citations, and presence in industry publications. For small companies, a Gemini-first content strategy focused on educational depth, citation-worthy assets, and structured data markup offers the clearest near-term AEO ROI.
2. Scope & Methodology
All 50 prompts were structured as practitioner-level questions a manufacturing executive or operations leader might ask when evaluating technology investments. Prompts were scored on a composite scale from 1–5 based on relevance, specificity, and buyer intent. The highest-scoring prompts (4.5+) represent high-value “money queries” where top-of-funnel AI visibility is most commercially significant.
Each response was tagged by answer type (Educational vs. Hybrid), citation behavior (Yes/No), and up to four vendor mentions. This report analyzes patterns across both dimensions to inform AEO strategy for MES and adjacent manufacturing software vendors.
Prompt Theme Distribution
The 50 prompts covered 14 distinct manufacturing themes including: Scaling, Digital Thread, Operations, Visibility, Traceability, Transformation, Workflows, Quality, Compliance, Automation,
Integration, Planning, Standardization, and Data Integrity. Transformation (10 prompts) and Workflows (8 prompts) were the most heavily represented categories.
Evaluation Environment
- Platform: ChatGPT (GPT-4o) and Google Gemini (latest available model)
- Query origin: Budapest, Hungary
- Session type: Fresh user accounts, incognito browser windows for all evaluations
- Evaluation period: March 10–12, 2026
- Queries submitted sequentially in theme order as per the master prompt sheet
3. Platform Comparison
The two platforms demonstrate fundamentally different behaviors in response type, citation patterns, and vendor selection logic. The table below summarizes the key structural differences across the eight dimensions evaluated in this study.
| Dimension | ChatGPT (GPT-4o) | Google Gemini |
| Response Type | 100% Hybrid (educational + vendor) | ~80% Educational, ~20% Hybrid |
| Citations Provided | 0 of 50 responses (0%) | ~47 of 50 responses (~94%) |
| Unique Vendors Mentioned | ~24 unique vendors | ~60+ unique vendors |
| Top Vendor | Siemens Opcenter (38 mentions, 76%) | Tulip (11 mentions, 22%) |
| Vendor Diversity | Low — top 4 cover ~80% of mentions | High — broad long tail of niche vendors |
| Small Co. Visibility | Minimal | Significant |
| Response Consistency | Very High | Moderate — evolving behavior observed |
| Geographic Sensitivity | Low apparent sensitivity | Higher — EU vendors surface in EU queries |
Response Type: Hybrid vs. Educational
ChatGPT consistently delivered Hybrid responses across all 50 prompts — blending educational context with direct vendor recommendations. This positions ChatGPT as a confident recommender, but one that draws from a pre-established short list of enterprise vendors with no mechanism to surface newer or lesser-known solutions unless they have already achieved significant brand recognition in training data.
Gemini began the evaluation as almost exclusively Educational — providing conceptual frameworks, methodology, and category descriptions without committing to specific vendors. However, a notable behavioral shift was observed over the two-day evaluation period: Gemini began surfacing explicit
vendor names directly within answer bodies (not just reference sections), suggesting either a dynamic personalization mechanism or a response to the accumulating context of the query sequence.
Citation Behavior: A Structural Divide
One of the most striking findings is citation behavior. Gemini provided source citations in approximately 94% of responses, creating a direct pathway for readers to explore vendor-specific content. ChatGPT provided zero citations across all 50 prompts.
This is not merely a UX difference — it represents a fundamental gap in how each platform signals credibility and enables downstream discovery. For vendors, Gemini’s citation behavior creates a dual-channel opportunity: appear in the AI-generated answer itself, and appear in the cited sources. ChatGPT’s citation absence means the only path to visibility is being mentioned by name in the response body — a significantly harder threshold to cross for smaller vendors.
Geographic Considerations
This study was conducted from Budapest, Hungary. While both platforms are globally deployed, localization effects can influence results — particularly for Gemini, which integrates more actively with Google Search infrastructure and may weight regionally prominent sources differently. Vendors with strong European press coverage, EU-published case studies, or partnerships with European system integrators may benefit from a geographic signal advantage in Gemini results when queried from EU locations.
ChatGPT showed no apparent geographic sensitivity in this study. North American companies targeting EU buyers should ensure their content is indexed and cited by European industry publications where feasible.
4. Vendor Analysis
ChatGPT Vendor Visibility Rankings
ChatGPT’s vendor recommendations are highly concentrated. Siemens Opcenter appeared in 38 of 50 responses — a 76% mention rate — making it by far the most AI-visible MES vendor on the platform. Rockwell FactoryTalk and Dassault DELMIA Apriso form a second tier. Below this, mentions drop sharply with no emerging challengers in sight.
| Vendor | Mention Frequency | Rate |
| Siemens Opcenter | |||||||||||||||||||| | 38×
(76%) |
| Rockwell FactoryTalk | ||||||||||||||| | 28×
(56%) |
| Dassault DELMIA Apriso | |||||||||||| | 22×
(44%) |
| PTC ThingWorx | |||||||| | 16×
(32%) |
| Siemens MindSphere | ||||| | 10×
(20%) |
| Tulip | ||||| | 9× (18%) |
| SAP | |||| | 8× (16%) |
| Siemens Tecnomatix | ||| | 6× (12%) |
| VKS | || | 3× (6%) |
| Dozuki | | | 2× (4%) |
Gemini Vendor Visibility Rankings
Gemini’s vendor landscape is strikingly different. No single vendor dominates — Tulip leads with 11 mentions (22%), followed by Fabrico at 9 (18%). The long tail is populated by niche, specialized vendors rarely seen in ChatGPT responses: Evocon, MachineMetrics, Jidoka Tech, HiveMQ, HighByte, and many others. This diversity indicates Gemini’s retrieval is more sensitive to content quality and citation signals than to brand size.
| Vendor | Mention Frequency | Rate |
| Tulip | |||||||||||||||||||| | 11×
(22%) |
| Fabrico | |||||||||||||||| | 9× (18%) |
| Siemens Opcenter | ||||||||||| | 6× (12%) |
| HiveMQ | ||||||||||| | 6× (12%) |
| Factory AI | ||||||||||| | 6× (12%) |
| Jidoka Tech | ||||||| | 4× (8%) |
| Dozuki | ||||||||| | 5× (10%) |
| Augmentir | ||||| | 3× (6%) |
| SAP | ||||| | 3× (6%) |
| HighByte | ||||| | 3× (6%) |
Cross-Platform Vendor Overlap
Only a small number of vendors appear with meaningful regularity on both platforms: Siemens Opcenter, Tulip, SAP, PTC, Dassault DELMIA, Rockwell FactoryTalk, Critical Manufacturing, Augmentir, Poka, and Dozuki. These vendors represent the highest-priority targets for platform-agnostic AEO investment. The vast majority of each platform’s vendor universe is non-overlapping, reinforcing that AEO strategy must be platform-specific.
Small Vendor Opportunity Map (Gemini)
The following vendors appeared exclusively or predominantly on Gemini, providing clear evidence that content-driven visibility is achievable without enterprise-scale brand recognition. This is a critical finding for independent software vendors, niche solution providers, and growth-stage manufacturing tech companies.
| Fabrico | Evocon | MachineMetrics |
| Shoplogix | Augmentir | Jidoka Tech |
| HiveMQ | HighByte | Litmus |
| Manual.to | Clypp | Redzone |
| Tractian | Ombrulla | DigiSailor |
| ANASOFT | Factory AI | Katana |
5. Key Observations
| 01 | Gemini Is Shifting — Personalization or Platform Evolution?
A notable behavioral change was observed in Gemini’s responses over the two-day evaluation. Early responses were almost exclusively Educational — rich in methodology and framework but light on direct vendor naming. By the latter half of the study, Gemini began explicitly naming vendors within response bodies rather than confining them to reference sections alone. Two explanations are plausible. First, Gemini may incorporate session-level or account-level signals that shift response style as a user pattern is established — a form of implicit personalization even without a declared preference profile. Second, the progressive specificity of the prompts themselves may have triggered more vendor-explicit responses. Either way, this underscores that Gemini’s AEO surface is dynamic and context-sensitive. Building presence across both educational content (for early-funnel queries) and product-specific pages (for late-funnel queries) is essential. |
| 02 | ChatGPT Favors Incumbents — Hard to Displace, Hard to Influence
ChatGPT’s vendor recommendations showed remarkable consistency — and rigidity. The same five to six platforms appeared across the vast majority of responses regardless of prompt theme, buyer context, or operational specificity. This suggests ChatGPT’s manufacturing vendor recommendations are largely derived from high-volume training data patterns rather than real-time retrieval or content freshness signals. For smaller vendors, this is a strategic reality check: attempting to crack ChatGPT without significant brand mass or broad third-party coverage is unlikely to yield near-term results. Smaller companies should redirect content investment toward Gemini and emerging AI search platforms where retrieval-based signals matter more than training-data saturation. For enterprise vendors with sufficient market presence, the primary levers for ChatGPT training influence remain analyst reports, widely-cited comparison articles, and integration partner pages. |
| 03 | ChatGPT’s Rotation Pattern — Fairness Algorithm or Bias?
An interesting pattern emerged in ChatGPT’s responses: vendor order appeared to rotate across similar prompts in a way that did not strictly correlate with relevance. Rather than always leading with Siemens Opcenter (the most-mentioned vendor), ChatGPT occasionally foregrounded Rockwell FactoryTalk or Dassault DELMIA in responses where Opcenter would have been the most contextually appropriate recommendation. This may reflect a deliberate design choice — a response diversity mechanism intended to avoid appearing to endorse any single vendor. If so, it has a meaningful AEO implication: the goal for enterprise vendors may not be to always appear first, but to remain consistently within the recommended set. This is a concept closer to Share of Voice than to rank position. |
| 04 | The Publication Gap — A Missed Signal Worth Recovering
This study did not systematically track publication and media mentions surfaced in AI responses — a significant missed opportunity. Gemini’s citation behavior frequently referenced third-party publications, industry blogs, and trade press. These citations are likely strong signals in Gemini’s retrieval process and represent a direct pathway for vendors to influence AI visibility through earned media. However, we have added Section 7 to address opportunities for media leverage. |
| 05 | Geographic Query Origin — Does Budapest Matter?
Running this study from Budapest, Hungary introduces a potential geographic bias, particularly for Gemini, which is more tightly integrated with Google’s regional search infrastructure. EU-prominent vendors (ANASOFT, Proxus, Transition Technologies) appeared in several Gemini responses, likely reflecting regional content weighting. ChatGPT showed no apparent geographic sensitivity. For vendors targeting North American buyers, replicating this study from a US IP address is advisable to establish a comparison baseline. The geographic signal, while potentially modest, could meaningfully affect which niche vendors appear in Gemini responses for regionally-specific manufacturing queries. |
6. Strategic Recommendations
Based on the findings across all 50 prompts and both platforms, the following recommendations are segmented by vendor profile. AEO strategy should not be platform-agnostic — the content signals that earn visibility on Gemini are fundamentally different from those that drive ChatGPT recognition.
| Small & Mid-Size Vendors | Enterprise / Incumbent Vendors |
| 01 Prioritize Gemini-first AEO content strategy | 01 Maintain ChatGPT visibility through analyst coverage and partner citations |
| 02 Publish deep educational content aligned to prompt themes | 02 Audit Gemini response quality —
incumbency doesn’t guarantee dominance |
| 03 Build citation-worthy assets: technical guides, case studies, comparisons | 03 Invest in long-tail content to defend against niche challengers on Gemini |
| 04 Target EU industry publications and trade press for Gemini signals | 04 Monitor cross-platform vendor rotation patterns quarterly |
| 05 Focus ChatGPT efforts on broad brand awareness, not direct AEO tactics | 05 Develop media-citation tracking as an AEO KPI alongside traditional SEO |
| 06 Use structured data markup and FAQ schema to improve AI indexability | 06 Run AEO prompt studies from target buyer geographies |
7. Media Signal Priority Matrix
One of the most actionable outputs of this AEO study is understanding which publications, analyst firms, communities, and influencers carry the strongest signal for both AI citation likelihood and actual buyer influence. The matrix below synthesizes findings from this study with additional research to produce a prioritized media investment guide for MES and manufacturing technology vendors.
Scores are on a 1-10 scale. AI Citation Score reflects how likely Gemini or ChatGPT is to draw from or reference that source. Buyer Influence Score reflects how meaningfully that outlet drives actual shortlisting and purchase decisions. The Composite Score is the average of both. The full 29-source sortable matrix with strategic notes is included as a companion Excel file (AEO_Media_Signal_Matrix.xlsx).
| Source / Publication | Category | AI
Score |
Buyer Score | Comp. | Quadrant | Priority |
| Gartner (Mfg & SC Practice) | Analyst Firm | 9 | 10 | 9.5 | PRIORITIZE | Tier 1 |
| IDC Manufacturing Insights | Analyst Firm | 8 | 9 | 8.5 | PRIORITIZE | Tier 1 |
| LNS Research | Analyst Firm | 7 | 9 | 8.0 | PRIORITIZE | Tier 1 |
| MESA International | Analyst Firm | 8 | 8 | 8.0 | PRIORITIZE | Tier 1 |
| Forrester (Mfg Practice) | Analyst Firm | 7 | 8 | 7.5 | PRIORITIZE | Tier 1 |
| IndustryWeek | Trade Publication | 8 | 9 | 8.5 | PRIORITIZE | Tier 1 |
| Manufacturing Dive | Trade Publication | 8 | 8 | 8.0 | PRIORITIZE | Tier 1 |
| Automation World / PMMI | Trade Publication | 7 | 8 | 7.5 | PRIORITIZE | Tier 1 |
| WEF Global Lighthouse Network | Community | 8 | 9 | 8.5 | PRIORITIZE | Tier 1 |
| Mfg Leadership Council | Community | 7 | 9 | 8.0 | PRIORITIZE | Tier 1 |
| ABI Research (Industrial) | Analyst Firm | 7 | 7 | 7.0 | INVEST | Tier 2 |
| Verdantix | Analyst Firm | 6 | 7 | 6.5 | INVEST | Tier 2 |
| Control Engineering | Trade Publication | 7 | 7 | 7.0 | INVEST | Tier 2 |
| Smart Manufacturing (SME) | Trade Publication | 7 | 7 | 7.0 | INVEST | Tier 2 |
| The Manufacturer (UK/EU) | Trade Publication | 6 | 7 | 6.5 | INVEST | Tier 2 |
| CESMII | Community | 7 | 7 | 7.0 | INVEST | Tier 2 |
| Tulip Blog | Vendor Thought Lead. | 7 | 6 | 6.5 | INVEST | Tier 2 |
| HighByte Blog | Vendor Thought Lead. | 6 | 6 | 6.0 | INVEST | Tier 2 |
| Walker Reynolds (LinkedIn) | Influencer | 6 | 7 | 6.5 | INVEST | Tier 2 |
| r/ManufacturingTechnology | Community | 5 | 6 | 5.5 | MONITOR | Tier 3 |
| Manufacturing Happy Hour | Podcast/Newsletter | 5 | 6 | 5.5 | MONITOR | Tier 3 |
| Ilan Nutovits (LinkedIn) | Influencer | 5 | 6 | 5.5 | MONITOR | Tier 3 |
| PRIORITIZE
High AI citation AND high buyer influence. Maximum ROI — focus here first. |
INVEST
Strong in one dimension. Worth investing based on your specific vendor profile. |
8. Closing Note
This study represents an early-stage snapshot of AI vendor visibility in a fast-evolving landscape. Both ChatGPT and Gemini are actively updating their retrieval, citation, and personalization mechanisms. AEO is not a one-time optimization — it is an ongoing discipline that requires regular prompt auditing, content refreshing, and competitive monitoring.
The data collected here provides a strong baseline for the AEO Guidebook’s manufacturing chapter and should be refreshed quarterly as AI platform behavior continues to evolve. The most important near-term action for any manufacturing technology vendor is to run their own version of this study using their specific target queries and buyer personas — this report is a template and a methodology as much as it is a set of findings.