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A real BuzzView analysis in this category
Nexthink’s AI visibility across ChatGPT, Google AI Overviews & Perplexity — one of the brands tracked in this category, straight from the live tool.
We sent 12 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.
Dynatrace (3.4%) and ServiceNow (3.4%) dominate the answers – the remaining 76 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 12 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 IT-Management & Testing.
Across 12 real buying prompts, AI models named 76 distinct providers in the IT-Management & Testing space. That is a remarkably wide field — far broader than categories like CRM or email marketing where a handful of giants reliably dominate every response. The sheer number of named tools reflects the structural diversity of the category itself: IT-Management and Software Testing are not one market but a cluster of adjacent disciplines, from digital employee experience platforms to remote access tools to automated test orchestration frameworks.
Despite that breadth, a familiar power-law pattern still emerges at the top. Dynatrace, ServiceNow, and Tosca each appear four times in the leaderboard — the maximum possible across the 12 prompts tested — giving them a 3.4% share of voice. Ivanti, Jira, Nexthink, Splunk, AnyDesk, TeamViewer, UFT, Selenium, and Tricentis each appear three times for a 2.6% share. Together these 12 brands account for 39 of the 116 total mentions, roughly 34%. The remaining 66% of mentions are spread across 64 other providers, each named only once or twice.
This split reveals something important about how AI models perceive maturity in enterprise software. In categories with clearer market leadership — think cloud infrastructure or business intelligence — AI tends to funnel recommendations toward three to five dominant names. In IT-Management & Testing, the recommendation landscape is flatter. No single vendor has achieved the kind of cross-subcategory dominance that would let it monopolize AI attention. Dynatrace is strong in observability; ServiceNow owns ITSM workflows; Selenium and Tricentis lead in open-source and enterprise test automation respectively. Each is a category captain in its lane, but no one crosses all lanes.
For brands in this space, the fragmentation is a double-edged reality. On the one hand, there is no entrenched AI monopoly to displace — the table is still being set, and a well-positioned content strategy can earn a seat. On the other hand, competing for generic "IT management tool" queries is difficult when 76 names are in play. The smarter path is subcategory specificity: owning a distinct vertical slice of the AI conversation rather than trying to win the whole category at once.
With 76 providers sharing 116 mentions, no vendor dominates — but the top 12 already hold a structural advantage. Brands not yet in that group need a subcategory-first entry strategy, not a category-wide one.
Among the three vendors tracked in detail — Nexthink, TeamViewer, and Tricentis — the raw visibility score is identical: 37.5%. This means each brand appeared in AI responses for roughly four out of every twelve prompts tested. At first glance, this looks like a dead heat. But visibility measures breadth — how many different prompts surfaced a brand at least once — and breadth alone does not determine competitive standing in an AI-driven discovery environment.
The share of voice figures tell the more telling story. Nexthink leads at 9.7%, TeamViewer follows at 8.8%, and Tricentis trails at 5.5%. Nexthink's advantage comes from appearing not just broadly but prominently — it tends to be named early in AI responses and in multiple subcategory contexts, including digital employee experience measurement and IT operations analytics. TeamViewer benefits from near-universal brand recognition in remote access, which translates reliably into AI citations whenever remote support prompts arise.
Tricentis's lower share of voice despite equal visibility points to a specific dynamic in the testing automation subcategory: the presence of well-documented open-source alternatives. When AI models respond to prompts about automated test orchestration, they frequently list Selenium alongside or even before Tricentis, diluting the share available to any single commercial vendor. Tricentis appears broadly — its 37.5% visibility is real — but competes in a response landscape where Selenium, UFT, and others each claim their own share of mentions.
What drives stronger visibility-to-SOV conversion in this category? The evidence points to three factors: a clearly owned subcategory with limited competition (Nexthink in digital employee experience), strong third-party documentation in the form of analyst reports and comparison articles, and consistent naming in community discussions indexed by AI training data. Brands that want to convert visibility into share of voice need to create content that makes AI models feel confident recommending them specifically — not just mentioning them as one option among many.
Visibility scores can mask meaningful competitive gaps — Nexthink converts its 37.5% visibility into a 9.7% SOV while Tricentis converts the same visibility into only 5.5%. Subcategory ownership and third-party validation are the differentiators.
The prompt types tested across the 12 queries reveal that IT-Management & Testing is a category where the question format matters enormously. Best-of prompts — such as "Was sind die besten IT-Management Tools für Großunternehmen in Deutschland?" — tend to generate broad, inclusive lists. AI models respond by covering multiple subcategories simultaneously, naming ServiceNow for ITSM, Dynatrace for observability, Jira for project-adjacent workflows, and Splunk for log analytics all in the same answer. No single vendor wins these prompts outright; instead, brands that appear in multiple subcategories accumulate mentions.
Comparison prompts — like "Vergleiche Nexthink, Splunk und Dynatrace im Bereich Digitale Mitarbeitererfahrung" — produce a very different dynamic. Here, the named vendors in the prompt itself dominate the response, which means brands that have established enough market presence to be mentioned in the question have already won half the battle. Nexthink benefits disproportionately from comparison prompts because buyers who are already evaluating it tend to frame their queries around it, creating a self-reinforcing citation loop in AI responses.
Alternative-seeking prompts — "Welche Alternativen gibt es zu Splunk?" — are where smaller or emerging players find their window of opportunity. AI models responding to these prompts look for credible alternatives, and brands with strong review platform presence, detailed comparison content, and analyst coverage get named even if they lack the brand recognition of a Splunk or Dynatrace. This is the most democratizing prompt type in the category and the one where content investment pays off fastest for challengers.
Use-case and vertical prompts — "Vergleiche Tricentis, Micro Focus UFT und Selenium im Bereich Automatisierte Tests orchestrieren" — reward deep subcategory documentation above all else. Tricentis, UFT, and Selenium dominate these responses not because of general brand power but because they have exhaustive technical content, integration guides, and community resources that AI models can cite confidently. For brands targeting specific testing workflows or specific industries, this prompt type is the highest-value strategic target — and the one most responsive to focused content investment.
Each prompt type creates a different competitive landscape. Brands should map their content strategy to the prompt types where they can realistically win — challengers should prioritize alternative and use-case prompts over broad best-of queries where incumbents have structural advantages.
The sentiment breakdown across the leaderboard reveals a clear fault line in how AI models characterize providers in this category. Nexthink earns 2 positive mentions out of its 3 total — the highest positive ratio in the leaderboard. Dynatrace, ServiceNow, Tosca, Ivanti, and Jira each register 1 positive mention. By contrast, Splunk, AnyDesk, TeamViewer, UFT, Selenium, and Tricentis each receive 0 positive mentions — all their citations are neutral. ServiceNow is the only brand in the top group to receive a negative mention, appearing in 1 response with a cautionary note alongside its positive citation.
What separates the positive-sentiment earners from the neutral-only group in IT-Management & Testing? The key driver appears to be outcome specificity. Nexthink's positive mentions typically accompany concrete outcome language — AI responses describe it as actively improving digital employee experience scores, reducing helpdesk ticket volume, or enabling proactive IT issue resolution. Dynatrace earns positive framing when AI models discuss its AI-powered root cause analysis capabilities. Tosca draws positive language around its codeless automation approach and enterprise compliance use cases.
The neutral group tells a different story. Splunk is named reliably but described in functional terms — it collects and analyzes machine data, it integrates with SIEM environments — without AI models volunteering that it is particularly good or preferable. Selenium earns consistent neutral mentions as the industry-standard open-source testing framework, but "industry standard" is not the same as "recommended." TeamViewer and AnyDesk are described as remote access tools without AI adding qualitative endorsement. These brands are present in the conversation but not advocated for — a crucial distinction when buyers are using AI as a decision shortlist generator.
The implication is that positive AI sentiment in enterprise software categories is driven by measurable outcomes, not feature lists. Content that helps AI models associate a brand with specific, quantifiable business improvements — faster incident resolution, lower test maintenance costs, higher employee productivity scores — is what converts neutral mentions into positive ones. Third-party case studies, analyst benchmark results, and customer ROI reports are the raw material AI models draw on when deciding whether to recommend enthusiastically or merely mention.
Positive sentiment in AI responses comes from outcome-specific content, not product descriptions. Brands currently earning neutral-only mentions — including major players like Splunk and Selenium — have a clear content gap to close with measurable result narratives and third-party validation.
The aggregate picture from all 116 mentions across 76 providers paints a clear portrait of an AI-search category in its early development phase. In mature AI-search categories — cloud storage, CRM, project management — repeated measurement typically reveals stable shortlists of five to eight brands that appear consistently across prompt types, with shares of voice clustering above 8-10% for the top names. IT-Management & Testing shows none of these consolidation signals. No provider exceeds 3.4% share of voice; the top 12 are virtually tied; and 64 additional providers each chip away at the remainder.
This fragmentation is partly structural and partly temporal. Structural fragmentation comes from the category's genuine breadth: IT-Management covers ITSM, endpoint management, digital employee experience, IT operations analytics, and remote access — each a distinct market with its own incumbents. Software Testing covers functional testing, performance testing, security testing, and test automation, again with distinct leaders in each lane. No single platform spans all of these convincingly, so AI models naturally distribute mentions across the full landscape rather than funneling toward one or two dominant names.
The temporal dimension matters equally. AI models are trained on content that exists at the time of training, which means the brands currently cited most frequently are those with the longest and deepest content trails — not necessarily the best products. Dynatrace, ServiceNow, Selenium, and Splunk benefit from years of technical documentation, analyst reports, and community discussions that predate the AI-search era. Newer entrants or repositioning incumbents face a training-data deficit that content strategy can begin to address, but only over time and at scale.
For brands that are currently absent from the AI leaderboard in this category, the early-stage nature of the AI-search landscape is genuinely good news. The positions are not yet locked. The brands that invest now in subcategory-specific content — technical depth, outcome narratives, comparison coverage, and community presence — have a realistic path to entering the AI shortlist within 12 to 18 months. Waiting until the category matures and consolidates around a fixed set of AI-recommended vendors will make entry exponentially harder and more expensive.
IT-Management & Testing is an early-phase AI-search category where positions are still forming. Brands that invest in subcategory-specific AI visibility content now — before consolidation sets in — can capture leaderboard positions that will be far harder to achieve 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.
Nexthink’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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