2026 AI Visibility Report: The State of Expense Management | Trakkr

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Generated source analysis. Market figures and findings below are generated claims, with no measurement records attached.

Generated 10 Jan 2026 · Read the source

A comprehensive analysis of how Large Language Models and AI search engines perceive and recommend expense management solutions in 2026.

The 2026 Expense Management landscape has transitioned from a search-engine-optimized market to an AI-visibility-driven one. Our analysis reveals a significant divergence between legacy incumbents and AI-native fintech platforms. While SAP Concur maintains a presence in enterprise-level queries, newer entrants like Ramp and Brex dominate 'best-of' recommendations due to their superior technical documentation and high-velocity content strategies. We observe that AI platforms now prioritize 'integration depth' and 'automation efficacy' as the primary ranking factors for business software recommendations.

Key findings in the source

Generated claims, including numeric figures. No underlying measurements are attached.

Ramp leads the category with a 94% visibility rate across 'efficiency-focused' queries.
Legacy players like SAP Concur suffer from 'technical debt' in their public documentation, leading to lower scores in Claude and Perplexity.
78% of AI-driven recommendations are influenced by third-party review aggregators and API documentation rather than primary marketing sites.
Perplexity has become the primary platform for 'feature-by-feature' comparison, while ChatGPT is the leader for 'brand awareness' queries.

The category scorecard.

Proposed scoring · illustrative source values. No measured ranking or trend is published here.

Ramp92 / 100Illustrative source score

Generated source profile. Scores and trends have no observed answer sample or measured history attached.

Source trend: rising (illustrative, no measured history attached)
Platform source scores: chatgpt: 94 / 100 · claude: 90 / 100 · gemini: 89 / 100 · perplexity: 95 / 100
Strength in source: Superior API documentation and real-time feature indexing.
Weakness in source: Lower visibility in legacy enterprise migration queries.

Brex89 / 100Illustrative source score

Generated source profile. Scores and trends have no observed answer sample or measured history attached.

Source trend: stable (illustrative, no measured history attached)
Platform source scores: chatgpt: 88 / 100 · claude: 91 / 100 · gemini: 85 / 100 · perplexity: 92 / 100
Strength in source: Dominates 'startup' and 'scale-up' intent queries.
Weakness in source: Inconsistent performance in European market queries.

SAP Concur76 / 100Illustrative source score

Generated source profile. Scores and trends have no observed answer sample or measured history attached.

Source trend: declining (illustrative, no measured history attached)
Platform source scores: chatgpt: 82 / 100 · claude: 68 / 100 · gemini: 79 / 100 · perplexity: 75 / 100
Strength in source: Unmatched brand authority in global enterprise segments.
Weakness in source: Perceived as high-friction by AI models analyzing user sentiment.

Expensify81 / 100Illustrative source score

Generated source profile. Scores and trends have no observed answer sample or measured history attached.

Source trend: stable (illustrative, no measured history attached)
Platform source scores: chatgpt: 84 / 100 · claude: 78 / 100 · gemini: 82 / 100 · perplexity: 80 / 100
Strength in source: Strong visibility for small business and freelancer queries.
Weakness in source: Lower scores in 'corporate card integration' comparisons.

Navan85 / 100Illustrative source score

Generated source profile. Scores and trends have no observed answer sample or measured history attached.

Source trend: rising (illustrative, no measured history attached)
Platform source scores: chatgpt: 86 / 100 · claude: 83 / 100 · gemini: 84 / 100 · perplexity: 87 / 100
Strength in source: High visibility in travel-integrated expense queries.
Weakness in source: Mid-tier performance in standalone card management queries.

Platform behavior in the source.

Generated descriptions and brand lists. No provider responses are attached to establish this behavior.

ChatGPT

Prioritizes historical brand authority
Favors clear, concise value propositions
Strong emphasis on user reviews and social proof

ChatGPT often groups brands by company size (SMB vs Enterprise) more distinctly than other models.

Top brands in the source: Expensify, Ramp, SAP Concur

Perplexity

Heavily weighted toward real-time pricing and feature tables
Cites technical documentation and changelogs
Sensitive to recent news and PR

Perplexity is the only platform that consistently identifies specific pricing tiers without being prompted.

Top brands in the source: Ramp, Brex, Navan

Transactional Intent (e.g., 'best expense software for startups')

Source volume: high · Source competition: high

Top performers in the source: Ramp, Brex

Technical/Integration (e.g., 'NetSuite expense automation')

Source volume: medium · Source competition: high

Top performers in the source: Airbase, Ramp

Educational/Compliance (e.g., 'IRS mileage rates 2026')

Source volume: high · Source competition: low

Top performers in the source: Zoho Expense, Expensify

Generated source analysis. Suggested strategies and trends from the source. Their effects have not been measured.

Generated 10 Jan 2026 · Read the source

Winning strategies in the source

Publishing exhaustive 'Integration Guides' in Markdown format for better LLM ingestion.
Maintaining updated, public-facing pricing tables with clear feature matrices.
Aggressive seeding of case studies on high-authority technical domains.

Common weaknesses

Gating critical product information behind PDF downloads (LLMs often deprioritize these).
Vague marketing language like 'AI-powered' without defining specific LLM use cases.
Broken or outdated links in documentation footers.

Emerging trends in the source

AI agents are now performing 'shadow trials' of software by reading documentation.
Voice-to-query visibility is becoming critical for mobile expense management.

What to try next.

Suggested actions from the source. Impact labels are estimates.

For Chief Marketing Officers (CMOs)

Brand equity is being rewritten by AI's ability to parse technical utility over marketing slogans.

Shift 20% of SEO budget toward 'AI Engine Optimization' (AIEO) focusing on technical clarity.

Suggested impact: high

For Product Managers

Integration depth is the #1 cited reason for AI recommendations in the enterprise segment.

Prioritize public API documentation and 'no-code' connector visibility.

Suggested impact: medium

For SEO & Content Teams

LLMs favor structured data and comparison tables over long-form narrative blog posts.

Convert top-performing comparison articles into structured data formats (JSON-LD/Schema).

Suggested impact: high

Methodology stated in the generated source

The file does not include the prompt set, observed responses or deterministic aggregate needed to verify these methodology claims. Its reported sample and collection window should not be treated as verified measurements.

The Trakkr AI Visibility Index (AVI) analyzes 50,000+ proprietary prompts across ChatGPT-4o, Claude 3.5 Sonnet, Gemini Pro, and Perplexity. We measure 'Share of Model Response' (SMR), sentiment polarity, and citation frequency. Data was collected between January 1 and April 30, 2026.

Source title: 2026 AI Visibility Report: The State of Expense Management
Generated: 10 Jan 2026
Interpretation: Use the generated hypotheses as inputs to your own category tests. Keep observed response evidence separate from this source scorecard.

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