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A real BuzzView analysis in this category
Remberg’s AI visibility across ChatGPT, Google AI Overviews & Perplexity — one of the brands tracked in this category, straight from the live tool.
We sent 8 real buying questions to ChatGPT and Google AI Overviews – the questions businesses in this category actually ask. Then we counted which providers get named, and how often. The result is a ranking built from real AI answers, not opinion.
IBM Maximo (8.0%) and SAP (8.0%) dominate the answers – the remaining 39 named providers split what's left. If you're not here, you simply don't exist to AI users. That's exactly the gap BuzzView makes visible.
Share of all brand mentions across 8 prompts (Share of Voice). The longer the bar, the more often AI names the provider – across every question tested.
Behind the ranking — what the numbers actually mean for brands in this space.
Across 8 buying prompts sent to ChatGPT and Google AI Overviews, AI systems named 39 distinct providers in the maintenance and asset management space. That figure alone tells a story. In categories where a handful of platforms dominate the conversation — think cloud storage or CRM — you typically see five or fewer names account for 70–80% of all mentions. Maintenance software is not that market yet. The sheer breadth of providers surfaced signals a category that AI has not yet learned to compress into a tight shortlist.
IBM Maximo and SAP sit jointly at the top, each earning 7 mentions and an 8.0% Share of Voice. Remberg follows with 6 mentions and 6.9% SoV, while MaintainX and ToolSense each reach 5 mentions. Fiix rounds out the top six with 4 mentions. Together these six providers account for 34 of the 87 total mentions — precisely 39.1% of all AI-generated references. The remaining 61% is distributed across 33 other providers, with 27 of them appearing only once or twice each.
This pattern reflects the structural reality of industrial software procurement. Unlike B2C or horizontal SaaS, maintenance and asset management purchases are deeply embedded in operational contexts — manufacturing plants, facility management contracts, public infrastructure. Each procurement cycle differs by industry vertical, asset type, regulatory requirement, and integration stack. AI systems are trained on this heterogeneous purchase intent, which means they surface a wider range of solutions rather than defaulting to a single dominant answer.
The 27 providers in the "27 more tools" bucket collectively hold 41.4% of all mentions. That is more than any single named provider. For brands currently sitting outside the top twelve, this is not a hopeless position — it reflects a fragmented AI landscape where visibility is still being established and where smart content investment can move the needle faster than in more consolidated categories.
Maintenance and asset management is not yet a winner-takes-all AI category — 39 named providers across 8 prompts means the ranking is still being written. Brands that invest in AI visibility now are buying real estate before the market consolidates.
Among the two providers tracked in this study, Remberg leads with a visibility score of 62.5% and a Share of Voice of 12.8%, while ToolSense registers 50.0% visibility and 8.3% SoV. On the surface, the gap looks modest — both platforms are present in AI responses, both are named multiple times. But a 12.5-percentage-point visibility differential on only 8 prompts is meaningful. It means Remberg is being surfaced in a broader range of prompt types, including best-of lists, comparisons, and use-case-specific queries, while ToolSense is surfacing more selectively.
In the maintenance software category, visibility is primarily driven by two factors: the depth of publicly available technical documentation and the breadth of third-party review coverage. AI language models learn from G2, Capterra, Gartner Peer Insights, and industry analyst publications such as LNS Research and ARC Advisory Group. Providers that have cultivated a strong presence on these platforms — with detailed comparison pages, use-case breakdowns by industry vertical, and integration documentation — consistently appear across a wider range of prompt types.
Remberg's higher visibility also reflects its positioning in the German-speaking manufacturing market, where it has built up substantial case study content, localized landing pages for specific industries such as automotive and food production, and active participation in trade press such as MM MaschinenMarkt. This creates a breadth of topically relevant content that AI systems can draw on when matching the platform to specific buyer contexts — from "CMMS for SMEs" to "EAM for regulated manufacturing environments."
ToolSense, despite a strong SoV figure of 8.3%, could narrow the gap by expanding its content footprint beyond core product pages. The maintenance software buyer researches across verticals — facilities management, logistics, construction, public utilities — and AI answers mirror that research path. A provider that publishes detailed, sector-specific answers to the questions buyers actually ask will gain visibility in prompt types where it currently does not appear.
Visibility in maintenance software AI responses is built on technical depth, review platform coverage, and vertical-specific content — not just brand awareness. The 12.5-point gap between Remberg and ToolSense is closeable through targeted content investment.
Prompt structure has a decisive influence on which providers AI surfaces. Best-of and category-leader prompts — "What is the best CMMS for manufacturing?" or "Which EAM platform do enterprises use?" — consistently surface IBM Maximo and SAP first. These are the platforms with the longest track record, the most extensive analyst coverage, and the deepest integration with enterprise ERP environments. Their presence at the top of best-of lists is a function of historical dominance and the sheer volume of published reference material that AI has absorbed.
Alternative-seeking prompts tell a different story. When buyers ask "What are the alternatives to IBM Maximo?" or "What should I use instead of SAP PM for a mid-sized plant?", the competitive field opens up. Remberg, MaintainX, Fiix, and ToolSense all appear more frequently in these response sets. This reflects their strategic positioning as modern, cloud-native alternatives to legacy enterprise systems — a narrative that resonates specifically with buyers who are already aware of the incumbent but are actively looking to move away from complexity and high total cost of ownership.
Comparison prompts — "Remberg vs IBM Maximo vs SAP PM" — generate the most structured AI outputs, with side-by-side feature analysis, pricing tier commentary, and implementation complexity notes. These prompts tend to surface providers that have invested in head-to-head comparison content: dedicated landing pages, analyst report citations, and user review summaries that explicitly address the comparison question. MaintainX performs notably well here due to its extensive comparison content targeting Maximo and SAP alternatives.
Use-case and vertical prompts — "Best maintenance software for facility management companies in Germany" or "CMMS for food production with regulatory compliance" — produce the most differentiated results. Planon leads in facility management contexts, Hexagon surfaces for asset-intensive industries such as energy and utilities, and ToolSense appears in prompts focused on asset and equipment tracking for field service teams. Brands that publish vertical-specific solution pages unlock these prompt types as an additional visibility channel entirely separate from their core product positioning.
Content strategy in maintenance software must be mapped to prompt type: best-of pages drive category authority, comparison pages win alternative-seeking buyers, and vertical landing pages unlock use-case queries. Brands that only publish one content type are leaving two-thirds of their AI visibility potential unrealized.
Sentiment analysis across the leaderboard reveals a clear divide between platforms that generate qualitative endorsement in AI responses and those that are mentioned in a purely factual, neutral context. MaintainX stands out: 3 of its 5 mentions carry a positive sentiment signal, representing a 60% positive rate — the highest ratio among all providers in this study. IBM Maximo and SAP each achieve 43% positive rates (3 positive out of 7 mentions), while Remberg reaches 33%. At the other end, Planon and Brightly receive exclusively neutral mentions, with zero positive signals across all their appearances.
What drives positive sentiment in maintenance software AI responses? The primary driver is user outcome language in the source material that AI has processed. Platforms with a high density of customer success stories describing quantified outcomes — "reduced unplanned downtime by 34%", "cut maintenance backlog by 60% in six months", "achieved full ISO 55001 compliance within one year" — tend to generate AI responses that frame the platform in an outcomes-positive context rather than a features-neutral one. MaintainX has invested heavily in this type of content, with an extensive library of industry-specific case studies.
Third-party validation also plays a significant role. Platforms that consistently appear on "best CMMS" lists published by G2, Software Advice, and Capterra — and that have accumulated reviews emphasizing ease of implementation and fast time-to-value — are more likely to be mentioned positively in AI responses. MaintainX and Fiix (2 positive out of 4 mentions, 50%) both perform well on this dimension. Hexagon also earns a 67% positive rate (2 out of 3 mentions), likely driven by its strong positioning in asset-intensive industry analyst reports from firms like IDC and Gartner.
Planon's exclusively neutral sentiment is instructive. It is a well-established platform in the integrated workplace management system segment, with significant enterprise deployments. But its AI mentions tend toward feature enumeration rather than outcome endorsement. This is a content gap, not a product gap. Platforms like Planon and Brightly that receive only neutral mentions should audit their publicly available case study, review, and analyst content and identify where outcome-driven language is absent — then close that gap systematically.
Positive AI sentiment is built on outcome-driven content — customer success stories with quantified results, strong third-party review presence, and analyst citations. Providers receiving only neutral mentions should treat this as a content brief, not a product problem.
The full data picture for maintenance and asset management points to a category in the early-to-middle phase of AI search maturity. The defining characteristic of a mature AI category is compression: a small number of providers — typically three to five — account for the majority of mentions, sentiment is mixed as AI systems have processed enough critical commentary to generate negative signals, and the gap between the top-ranked and mid-ranked provider is substantial. None of these conditions apply here. Forty-one percent of all mentions go to an undifferentiated group of 27 providers, and not a single brand in the entire study received a negative sentiment signal.
The absence of negative sentiment is particularly revealing. In more mature AI categories — cloud security, CRM, or HR software — AI systems have absorbed enough critical review content to generate nuanced, sometimes negative assessments of specific platforms. The fact that zero providers in this study received negative mentions suggests that AI has processed relatively little deeply critical content about maintenance software. This is partly a function of the industry's procurement culture: industrial software buyers are less likely to publish negative vendor reviews publicly, and the trade press tends toward feature reporting rather than comparative criticism.
The two-way tie between IBM Maximo and SAP at the top — both at exactly 8.0% SoV and 7 mentions — is another marker of immaturity. In a more developed AI landscape for this category, we would expect a clearer hierarchy, with one platform pulling decisively ahead based on accumulated training signal. The tie suggests that AI systems are currently drawing on different subsets of source material — IBM Maximo stronger in manufacturing and utilities contexts, SAP PM stronger in enterprise ERP-integrated environments — without yet having synthesized a consistent category narrative.
For brands in the maintenance and asset management space, the formative phase is the best time to invest in AI visibility. The ranking is not locked in. Remberg's 62.5% visibility score shows that a modern, well-documented platform can reach the top tier of AI recommendations without the decades of market presence that IBM and SAP carry. The window to shape how AI systems understand and categorize this market is open — but it will not remain open indefinitely. As buyer-generated content, analyst reports, and AI-native comparison platforms accumulate, the category narrative will solidify, and the cost of changing position will rise significantly.
Maintenance and asset management is in AI search's formative phase — fragmented, sentiment-neutral, and without a locked-in hierarchy. Brands that build their AI content footprint now will claim positions that become exponentially harder to displace once the category matures.
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No wishful thinking: the ranking comes from exactly these prompt types – best-of questions, comparisons, alternatives and use cases.
Visibility score = share of prompts where the provider appears in the AI answer at all. 100% means: present for every relevant question.
Remberg’s AI visibility across ChatGPT, Google AI Overviews & Perplexity — one of the brands tracked in this category, straight from the live tool.
We set the real search and buying questions of your industry – exactly how your customers actually ask AI.
Every prompt runs against all major AI models. We count mentions, position, sentiment and the cited sources.
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