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A real BuzzView analysis in this category
Celonis’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.
SAP (11.6%) and UiPath (7.2%) 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 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 this space.
Across 12 real buying prompts submitted to ChatGPT and Google AI Overviews, the AI engines collectively named 39 distinct providers in the Process Mining & Automation space — generating 69 total mentions. That is a remarkably wide distribution for an enterprise software category. Unlike consumer verticals where two or three household names routinely dominate, process mining and automation are fractured across legacy ERP incumbents, specialist pure-plays, and a rapidly growing cohort of industrial AI newcomers. The AI systems clearly do not perceive a single obvious market leader that crowds out all alternatives.
SAP leads the leaderboard with 8 mentions and an 11.6% share of voice — a comfortable first place, but far from the monopoly its ERP dominance might suggest. UiPath and Celonis tie at 5 mentions each (7.2% SoV apiece), followed by Camunda at 4 mentions (5.8%). Appian, Automation Anywhere, and Siemens each collect 3 mentions. Add those six together and you reach 28 mentions, or roughly 40.6% of the total — meaning that nearly six in every ten AI-generated mentions flow to providers outside the top six. That is a power law, but a shallow one by enterprise software standards.
The remaining 27 providers share 28 mentions among themselves — an average of just over one mention each. In practical terms this means the AI models are drawing on a broad knowledge base of documentation, analyst reports, and web content that references many niche players: platforms specialising in industrial AI, IoT-driven process intelligence, low-code orchestration, and robotic desktop automation. For a category that sits at the intersection of multiple technology disciplines, this breadth is structurally expected. Buyers ask AI about very different problems under the same umbrella label.
What this concentration pattern signals for enterprise brands is that the category has not yet consolidated in the AI-search layer the way it has in traditional analyst rankings such as Gartner Magic Quadrants. A Gartner leader does not automatically translate into an AI visibility leader. The AI surface rewards content breadth, use-case specificity, and third-party citation density — not just brand size. A mid-tier specialist that has accumulated strong documentation, customer case studies, and analyst coverage can outperform an ERP giant on focused prompt types, as the Celonis and UiPath data demonstrate.
With 39 providers sharing 69 mentions and no single brand commanding more than 11.6% share of voice, Process Mining & Automation remains wide open in AI search — giving well-positioned challengers a realistic path to top-three visibility without competing on brand budget alone.
Among the three tracked firms in this dataset, Celonis achieves a visibility score of 50.0% — meaning it appeared in half of all measured prompts and earned the highest share of voice at 14.8%. Camunda follows at 37.5% visibility and 10.3% SoV. craftworks, an industrial AI specialist, registers 25.0% visibility and 8.7% SoV. The gap between the top and bottom tracked providers is exactly 25 percentage points, or a factor of two. That is not a coincidence of brand size; it reflects structural differences in how each brand has built its AI-readable content footprint.
Celonis has invested heavily in thought leadership content, detailed technical documentation, and high-profile enterprise case studies — particularly in manufacturing, retail, and financial services. This creates a dense web of third-party references, analyst citations, and comparison articles that AI language models can draw on when assembling answers about process intelligence tools. The result is consistent appearance across a wide range of prompt types, from best-of lists to vendor comparisons to use-case-specific questions. Their content strategy is engineered — whether deliberately or not — to maximise AI retrieval.
Camunda occupies a distinct niche as an open-source-rooted workflow orchestration platform with a large developer community. Its visibility is driven by a different content mechanism: developer documentation, GitHub references, community forum discussions, and technical blog posts that explain real implementation patterns. These are exactly the types of content AI models treat as high-signal when answering technical comparison prompts. craftworks, by contrast, is a younger industrial AI consultancy whose public content footprint is thinner — fewer third-party references, less comparison coverage — which directly suppresses its prompt-level visibility despite genuine product capability.
The lesson for Process Mining & Automation brands is that visibility is not a function of product quality or even of total marketing spend — it is a function of how well your brand is described, compared, and endorsed in publicly available web content that AI models ingest. Analyst reports, G2 and Capterra review aggregations, media coverage of customer wins, and detailed implementation guides all feed the retrieval layer. A brand scoring 25% visibility today can realistically reach 40%+ within two to three quarters by targeting the right content types, provided it has genuine proof points to anchor them.
The 25-point visibility gap between Celonis and craftworks is a content infrastructure gap, not a brand awareness gap — and content infrastructure can be systematically built in a matter of months with the right strategy.
The sample prompts in this dataset reveal that AI buying questions in the Process Mining & Automation category are far from uniform. A "best tools for Großunternehmen" prompt ("Was sind die besten Process Intelligence Tools für Großunternehmen in Deutschland?") systematically surfaces enterprise incumbents — primarily SAP, Celonis, and IBM — because the AI model associates large enterprise requirements with established vendors that appear prominently in analyst frameworks and procurement documentation. SAP's eight total mentions are disproportionately driven by this prompt type, where its ERP integration story gives it a structural narrative advantage.
Comparison prompts ("Vergleiche Celonis, SAP und Oracle") follow a different logic entirely: the AI is constrained to discuss the named brands, so SoV within those answers is mechanically determined. But the interesting signal is in the adjacent recommendations the AI adds — the "you might also consider" layer. In process mining comparisons, this is where Camunda and UiPath consistently appear, because their technical positioning is well-documented in comparison articles and software review platforms. Brands that appear in the "also consider" layer of comparison prompts are gaining awareness without being the primary subject of the query — a highly leveraged position.
Alternative-seeking prompts ("Welche Alternativen gibt es zu SAP?") are the highest-intent query type in enterprise software and they function as a direct opportunity for challengers. Here Celonis, UiPath, Camunda, and Appian accumulate disproportionate share because alternative-seeker content — "SAP alternatives for process mining", "Celonis competitors" — is a well-established SEO content format that AI models readily retrieve. Brands that have explicitly created alternative-positioning content, or that appear in such content created by third parties, benefit enormously from this prompt type.
Use-case and vertical prompts ("Produktqualität mit AI vorhersagen", "Industrial AI Tools für Industrieunternehmen") introduce a completely different set of players — Siemens and craftworks appear almost exclusively here, because their content is anchored in industrial and manufacturing use cases rather than general process management. This is the content opportunity most often missed by process mining vendors: vertical-specific use case pages, industry-specific case studies, and application-domain documentation that matches exactly how industrial buyers formulate their questions to AI. Without that content, a vendor simply does not exist in the AI's response to those prompts, regardless of how good their product is.
Winning AI visibility in Process Mining & Automation requires four distinct content tracks: best-of authority content for enterprise positioning, comparison framing for alternative-seeker capture, technical documentation for developer-intent queries, and vertical use-case pages for industrial and domain-specific prompts.
Sentiment in AI responses is not a soft metric — it is a direct signal of how language models characterise each vendor when recommending them to a buyer. Celonis stands out sharply: 4 of its 5 mentions carry positive framing, with only 1 neutral. That means when ChatGPT or Google AI brings up Celonis, it is typically doing so with an endorsing, capability-affirming framing — "Celonis is recognised as the leader in process intelligence", "Celonis offers the most mature process mining capabilities for enterprise" — rather than simply listing it as an option. This is what positive AI sentiment looks like in practice, and it is the gold standard for enterprise AI visibility.
Camunda earns 2 positive and 2 neutral mentions from 4 total, while Appian and Automation Anywhere both achieve 2 positive out of 3 mentions — a solid ratio. SAP collects 2 positive and 6 neutral from 8 mentions, which reflects its status as a universal reference point that AI models mention out of completeness as much as out of endorsement. SAP is the "of course" answer — reliably named, rarely celebrated. IBM, Salesforce, and ServiceNow sit entirely in neutral territory across their 2 mentions each — present in the data but not particularly praised. No provider in this dataset received a negative mention, which is notable but not surprising: AI models in enterprise software contexts tend to avoid explicitly negative framings unless a vendor has severe documented problems.
What drives positive sentiment in Process Mining & Automation? The pattern across the data points to three factors. First, specificity of outcomes: vendors described with concrete results — reduced cycle times, measurable compliance improvements, identified process bottlenecks with dollar values — earn positive framing more consistently than vendors described in generic capability terms. Celonis benefits from abundant case studies with quantified ROI. Second, recognition by credible third parties: analyst endorsements from Forrester, IDC, and Gartner translate directly into the positive qualifying language AI models use. Third, community and developer adoption: Camunda's open-source reputation creates an authenticity signal that pure commercial platforms struggle to replicate.
For brands currently sitting in neutral-only territory — IBM, Salesforce, ServiceNow, Augury — the path to positive sentiment is not primarily about AI optimisation; it is about generating the outcome-focused, third-party-validated content that AI models retrieve and then characterise positively. A single well-cited analyst report, a detailed case study with a named enterprise customer and measurable results, or a prominent media feature can shift a brand from "neutral also-mentioned" to "positively endorsed" in AI responses. Sentiment improvement in this category is a content quality problem with a structured solution.
Celonis's 80% positive mention rate versus IBM's, Salesforce's, and ServiceNow's 0% reveals that in Process Mining & Automation, positive AI sentiment is built on documented outcomes and analyst validation — not on brand heritage or market capitalisation.
Mature AI-search categories typically show a clear power-law distribution: two or three names capture 50% or more of all mentions, the AI consistently frames those names as the default recommendations, and challengers face a significant barrier to appearing in responses at all. Process Mining & Automation does not yet fit that description. With 39 distinct providers named across just 12 prompts, and the top provider (SAP) holding only 11.6% share of voice, this is a category where the AI models are still assembling their "mental model" of the competitive landscape — drawing on a diverse and rapidly evolving body of content rather than a settled consensus view.
This fragmentation is partly a product of the category's structural complexity. Process Mining and Automation are genuinely distinct technology disciplines that have been bundled together in vendor marketing — Celonis plays primarily in process intelligence and mining, while UiPath and Automation Anywhere are robotic process automation specialists, while Camunda sits in workflow orchestration, while craftworks targets industrial AI applications. AI models are sensitive to these distinctions: when a prompt asks about process intelligence, it surfaces different providers than when it asks about RPA or workflow automation. The category label is broad enough to encompass multiple competitive arenas, each with its own emerging leader.
The inflection-point nature of the category is also visible in the composition of mentions. Of the 69 total mentions, 28 go to providers outside the tracked and leaderboard cohort — 27 distinct names sharing 28 mentions. This is characteristic of a category where AI models are naming long-tail specialists and regional players because the dominant-provider narrative has not yet hardened. In twelve to eighteen months, based on how similar enterprise software categories have evolved in AI search, we would expect the top-three providers to increase their collective share significantly as their content ecosystems grow and as AI models develop more confident default recommendations.
For brands operating in Process Mining & Automation, the strategic implication is urgent and time-limited. The window to establish AI-search leadership is open now — positions are still being contested, sentiment is still being shaped, and the AI models have not locked in their default answer hierarchy. A brand that invests systematically in AI visibility over the next six to twelve months — building outcome-specific content, earning third-party coverage, accumulating review platform presence, and creating vertical-specific use case documentation — can realistically move from the long-tail of unnamed providers into the top-five visibility cohort. In eighteen months that same investment will be harder to translate into the same gains, because the leaders will have entrenched their positions and new entrants face a compounding disadvantage.
Process Mining & Automation is at its AI-search inflection point: fragmented, contested, and not yet dominated — which means brands investing in structured AI visibility now will gain ground that becomes exponentially harder to capture once the category consensus solidifies.
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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.
Celonis’s AI visibility across ChatGPT, Google AI Overviews & Perplexity — one of the brands tracked in this category, straight from the live tool.
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