When AI is asked about spend management tools — who does it recommend?
We sent 40 real buying questions to ChatGPT and Google AI Overviews – the questions finance teams actually ask. Then we counted which providers get named, and how often. The result is a ranking built from real AI answers, not opinion.
Nearly half of all AI recommendations go to just 6 of 181 providers.
DATEV (6.3%) and Lexoffice (3.3%) dominate the answers – the remaining 181 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.
Who does AI recommend for spend management tools?
Share of all brand mentions across 40 prompts (Share of Voice). The longer the bar, the more often AI names the provider – across every question tested.
Five things the data reveals about this category
Behind the ranking — what the numbers actually mean for brands in this space.
181 providers named. The top 6 capture just 20.6% of all 428 mentions.
Across 40 prompts sent to ChatGPT and Google AI Overviews, AI systems named 181 distinct providers in the Spend Management and Firmenkarten space. That figure alone is striking. It means AI has indexed an enormous breadth of tools — from established accounting platforms to niche fintech startups — and treats none of them as an obvious monopoly. The category is not a duopoly or a standard three-player market. It is an exceptionally fragmented landscape where AI distributes recognition widely and no single brand dominates the answer space.
The top 6 providers in the leaderboard — DATEV, Lexoffice, sevDesk, Circula, Moss, and Lexware — together accumulated 88 mentions out of 428 total. That is 20.6%. Put differently, nearly 80% of all AI mentions in this category go to providers outside the top 6. The remaining 286 mentions are distributed across 169 other named providers, each appearing too infrequently to crack the visible leaderboard. This is a classic long-tail distribution, but an unusually pronounced one for a B2B software category.
This fragmentation reflects the structural reality of the German-speaking spend management market. Unlike CRM, where Salesforce has established near-total mind share, or project management, where a handful of platforms own the conversation, spend management sits at the intersection of accounting, expense management, corporate cards, and liquidity planning. Each sub-segment has its own set of specialist tools, and AI models have learned to distinguish them. When a prompt asks about Reisekostenabrechnung, different brands surface than when a prompt asks about Liquiditätsplanung or Firmenkreditkarten.
The practical implication is that there is no single gatekeeping position to defend or attack. DATEV holds the highest raw mention count at 27 mentions (6.3% share of voice), but that represents a thin margin of leadership in a market where the number two and three brands — Lexoffice and sevDesk — are tied at 14 mentions each. The ceiling for any individual brand growing its AI visibility is high precisely because the field is so open. Any tool with focused content investment can realistically push into the top tier.
In a category where 181 providers compete for 428 AI mentions, no brand is safe by default — and no challenger is locked out. The market is wide open for the brands that invest in structured AI visibility content now.
Kontist appears in 100% of relevant prompts. Pile is present in just 25%. A 75-point gap separates them.
Among the ten tracked providers in BuzzView's panel, the visibility scores range from a perfect 100.0% for Kontist down to 25.0% for Pile. This 75-percentage-point gap is not the result of market size differences alone — Pile is a legitimate, funded fintech operating in the corporate cards segment. The gap reflects a fundamental difference in how these brands have positioned themselves relative to the content and signals that AI systems use to build their awareness of a product. One brand is legible to AI across all relevant prompt types; the other is nearly invisible outside a narrow set of questions.
What drives high visibility in the spend management and Firmenkarten category is a combination of structured review coverage, comparison content, and category-defining language. Kontist has built strong organic presence around freelancer-specific financial management, a well-defined niche where AI can reliably match its positioning to user intent. Circula (87.5% visibility) and Candis (75%) similarly benefit from clear category ownership — Circula around expense and benefit management, Candis around accounts payable automation. The pattern is consistent: brands that own a clearly defined sub-problem within the broader category earn more prompt appearances.
The middle band — Pliant and Finway both at 62.5% — represents brands that are present in most prompt types but not dominant in any. They appear when AI casts a wide net but fall out when prompts become more specific or when a competitor has stronger anchoring to that particular use case. Commitly sits at 50% visibility despite holding an impressive 20.0% share of voice within the prompts where it does appear — meaning when it is named, it is named repeatedly. This is a different strategic position than Kontist's broad presence: deep resonance in a narrower slice of the prompt space.
For brands in the lower visibility tier — Norman at 37.5% and Pile at 25% — the challenge is not product quality but content surface area. AI systems build their awareness through structured information: G2 reviews, Capterra comparisons, editorial roundups, case studies with outcome data, and product documentation that uses the same vocabulary buyers use in prompts. Brands that have not invested in these channels simply do not appear in AI answers, regardless of their actual market position or customer satisfaction scores. Visibility is earned through content infrastructure, not product excellence alone.
A 75-point visibility gap within a single category is a competitive moat built from content signals, not product differentiation. Brands at the bottom of the range are not unknown — they are under-documented in the sources AI trusts.
Different questions surface completely different winners — and most brands only win one type.
The 40 prompts in this study cover four distinct intent types: best-of questions ("Was sind die besten Spend-Management-Tools?"), direct comparisons ("Vergleiche Kontist, Wise Business und N26 Business"), alternative-seeking queries ("Welche Alternativen gibt es zu DATEV?"), and use-case or vertical prompts ("Was sind die besten Tools für Spesen und Benefits für KMU?"). Each prompt type creates a different competitive landscape within the same broad category, and the brands that dominate one type do not automatically dominate the others. This is the most underappreciated structural feature of AI visibility in multi-use-case software markets.
Best-of prompts heavily favor legacy-anchored brands with broad name recognition. DATEV benefits enormously here because its market presence and the volume of content that references it as a benchmark create strong associative signals for AI systems evaluating what counts as a "top" tool. Lexoffice and sevDesk similarly benefit from years of SEO-optimized review and comparison content that positions them as default recommendations in German SMB accounting discussions. These brands have become the mental shorthand AI uses when the question is open-ended and unspecified by vertical or company size.
Alternative-seeking prompts shift the competitive picture toward challenger brands. When a user asks for alternatives to DATEV, AI systems must identify tools that occupy adjacent or overlapping positions — and this is where Lexware, Circula, Candis, and Moss gain disproportionate share. These brands have built content that explicitly positions them in comparison to market leaders, making them legible as alternatives in AI's pattern-matching. Use-case prompts go further still: vertical queries about freelancer tools surface Kontist; queries about expense management surface Circula; accounts payable prompts surface Candis. Each brand's content has effectively trained AI to associate it with a specific problem type.
The content strategy implication is significant. Brands that have only invested in broad "best tool" positioning will only win best-of prompts — and in a fragmented market, those account for a minority of total buyer intent. The highest-ceiling strategy is to build a content architecture that covers all four prompt types: establish category presence for best-of queries, create direct comparison pages for head-to-head queries, publish explicit alternative-framing content, and develop deep use-case documentation that matches the specific verticals and company sizes your buyers represent. Kontist's 100% visibility score reflects exactly this type of full-coverage content strategy.
Winning AI visibility in spend management requires content coverage across all four prompt intent types — not just category-level best-of positioning. Brands that only publish generic comparison content leave three-quarters of buyer prompts unanswered.
Kontist earns positive framing in 70% of its mentions. DATEV, despite leading on volume, earns it in just 14.8%.
Raw mention counts and sentiment tell very different stories in this category. Across the 12 brands tracked in the leaderboard, the ratio of positive to neutral mentions varies dramatically. Kontist stands out with 7 positive mentions out of 10 total — a 70% positive rate that is the highest in the dataset. Candis follows with 5 positive out of 9 (55.6%), and both Circula and sevDesk reach 45% positive rates. By contrast, DATEV — the brand with the most raw mentions at 27 — earns just 4 positive mentions, meaning 85% of its AI appearances are neutral and framed as informational rather than enthusiastic.
In the spend management and Firmenkarten category, positive sentiment in AI outputs is driven by three primary signals: outcome-specific customer testimonials, quantified efficiency claims (expense reports processed, time saved per employee), and third-party validation from analyst coverage, award recognition, or credible media. Kontist's positive framing correlates with its well-documented positioning around tax savings for freelancers — a concrete, outcome-oriented value proposition that AI systems can evaluate and present approvingly. Neutral mentions, by contrast, tend to reflect factual product descriptions without outcome anchoring.
SAP and Spendesk present the most striking sentiment data for their position in the ranking. Both have 9 mentions each, but SAP earns positive framing in just 1 of those 9, and Spendesk similarly earns just 1 positive mention. SAP's neutral framing is structurally predictable — enterprise software is rarely described with enthusiasm in general buyer prompts, and SAP Concur is positioned as a compliance tool rather than a product users advocate for. Spendesk's low positive rate is more telling: despite being a purpose-built modern spend management platform with strong product reviews, its AI presence appears heavily informational rather than recommendation-oriented.
Zero negative mentions across all tracked brands is notable and somewhat counterintuitive. In consumer categories, AI systems readily surface criticisms — slow support, pricing opacity, feature gaps. In the spend management B2B space, the absence of negative sentiment likely reflects the professional register of the source material AI trains on: B2B software reviews skew constructive, and CFO-targeted editorial content rarely uses adversarial framing. This means the competition for AI-generated positive sentiment is entirely won or lost through constructive content — case studies, ROI documentation, named customer outcomes — rather than through managing negative press.
Volume and sentiment are independent variables. DATEV's leadership in raw mentions does not translate to positive framing. Brands that publish outcome-specific customer stories and quantified ROI claims earn significantly higher positive sentiment rates from AI — and positive framing meaningfully increases conversion from AI-generated recommendations.
181 providers, no dominant player, shifting sub-segments: Spend Management is in AI's most contested early-growth phase.
A category's AI maturity level determines how easily a new entrant can gain visibility and how risky it is for an incumbent to stay passive. Mature AI categories — cloud storage, CRM, project management — are characterized by a small number of highly dominant brands (one or two players holding 30–50% share of voice), stable rankings across prompt types, and very high barriers to new entrants because AI's associative models are already deeply anchored. The Spend Management and Firmenkarten market looks nothing like this. With 181 named providers and a top brand holding just 6.3% share of voice, this category is firmly in the early-growth phase of AI visibility development.
Early-growth AI categories are defined by high fluidity. Rankings are not yet locked in because AI systems are still in the process of building stable associative models for the space. The brands that appear in our leaderboard today — DATEV, Lexoffice, sevDesk — hold their positions not because of irreversible first-mover advantages but because they happen to have generated enough structured content, reviews, and comparison material to create reliable associations. Their positions are defensible but far from permanent. A brand that invests aggressively in AI-optimized content today can realistically crack the top 5 within 12 to 18 months.
The sub-segment fragmentation in this category adds another layer of opportunity. Spend Management as a label encompasses at least five distinct buyer problems: freelancer tax and banking (Kontist's territory), SMB expense and benefits management (Circula's territory), accounts payable automation (Candis's territory), corporate card programs (Moss, Pleo, Pliant), and full liquidity and financial planning for mid-market companies (Finway, Helu.io, Commitly). Each sub-segment has its own prompt vocabulary, its own set of competing brands, and its own AI coverage quality. A brand does not need to dominate the entire category to win — dominating one sub-segment creates substantial, durable AI visibility.
The window for establishing strong AI positions in this category is open now but will not remain open indefinitely. As AI models continue to train on progressively larger content datasets and as more brands begin publishing AI-targeted content, the rankings will consolidate. The brands investing in visibility infrastructure today — structured comparison pages, outcome-driven case studies, sub-segment-specific content hubs, and review platform coverage — will be the ones that AI's associative models lock onto as the category matures. The brands that wait will find the ceiling dropping rapidly once consolidation begins. In AI visibility, being second to invest is often the same as being too late.
Spend Management is in the most actionable phase of AI visibility development: fragmented, fluid, and not yet consolidated. The brands that build structured AI content infrastructure today will lock in positions that become exponentially harder to displace once the category matures.
Real questions finance teams ask AI
No wishful thinking: the ranking comes from exactly these prompt types – best-of questions, comparisons, alternatives and use cases.
AI visibility of the tested providers
Visibility score = share of prompts where the provider appears in the AI answer at all. 100% means: present for every relevant question.
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Kontist’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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