Every results figure we publish comes from industry research, with a verifiable source and year. Here we document the origin of each number so you can check it yourself.
Last updated: June 2026In an enterprise artificial intelligence market saturated with inflated promises, we choose the opposite path: to show the source behind every data point. If a number appears on any Vantegrate page (about AI agents, WhatsApp Business automation, document OCR, conversational business intelligence or logistics), it has its entry in this source table, with the link to the original study. This is what we mean by transparency: verifiable data, citable benchmarks and zero unfounded guarantees.
Trust is not declared, it is demonstrated. Most B2B AI vendors publish results figures without saying where they come from, and many of those figures do not hold up to verification. We prefer to expose our sources so you can judge the strength of each claim on your own terms.
This page serves two functions. For you as a client or evaluator, it is the proof that our marketing claims have real backing in studies from consultancies, independent analysts and academic papers. For us, it is an internal commitment: no Vantegrate team can publish a number on the site without it having a documented entry here. In practice, it is the source of truth for every benchmark we communicate.
Not all numbers mean the same thing, and part of being transparent is explaining what kind of data you are reading in each case. That is why we classify each numerical claim into one of three categories, and we identify it in the "Type" column of each table:
Industry benchmark. This is a data point that comes from independent research: consultancies such as McKinsey, Bain or Deloitte, analysts such as Gartner or Forrester, peer-reviewed academic papers, industry associations and reference reports for the sector. When we say "industry studies show that...", we are in this category. It represents the industry average or range, not a result we guarantee to you. It is the basis of almost everything we communicate.
Vendor data. Some relevant figures are published only by companies that sell the solution the data describes. We include them because they provide useful reference when no independent source exists, but we always label them as such, because they come from an interested party and may be subject to selection bias. Whenever an equivalent independent source exists, we prioritize it over the vendor's.
Improvement documented in our own cases. When we publish a number as a Vantegrate result of our own, it is because it is measured in real production with a client who agreed to be a case study. These cases are always identified as such and are never presented as a general industry benchmark.
The following is the complete list of sources that back every figure on the site, grouped by topic area. Each row includes the claim, the industry benchmark with its range and year, the link to the original source and the type of data.
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| WhatsApp Business open rate vs. email | WhatsApp 75-98% (baseline 58-79% unoptimized); email 20-25% real-world | Bird, "Measure WhatsApp Performance Against Benchmarks" (2024); Brevo, Marketing Orchestration Benchmark (2026) | Industry benchmark |
| WhatsApp click-through vs. email | WhatsApp 15-45%; email 2-3% | Bird (2024); MailerLite, Email Benchmarks (2025) | Industry benchmark |
| Engagement lift over email | 4-6x on conversational B2C campaigns | Bird (2024) | Industry benchmark |
| WhatsApp response rate | 5-10x higher than email depending on the use case | Aurora Inbox (2026) | Vendor data |
| Cart recovery by conversational channel | WhatsApp 15-30% vs. email 2-5% | Kanal (2026); Lojas Renner case via Meta | Vendor data |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| Cost to process a manual invoice | USD 9.40 average; USD 12.88 non-best-in-class; USD 2.78 best-in-class | Ardent Partners, "AP Metrics That Matter in 2025" (N=212) | Industry benchmark |
| Median invoice cost (general organizations) | USD 21.40 | APQC, Open Standards Benchmarking (2024-2025) | Industry benchmark |
| Invoice cycle time | Best-in-class 3.1 days vs. 17.4 days for the rest | Ardent Partners (2025), via Medius | Industry benchmark |
| ML extraction accuracy on legible documents | High 90% range | Gartner, Market Guide for AP Invoice Automation (2024-2025) | Industry benchmark |
| Touchless / STP rate on PO-backed invoices | 85-90% | Basware, Medius, SoftCo (leading vendors in the Gartner Magic Quadrant 2025) | Vendor data |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| "Where is my order" inquiries as % of support | 30-50% of volume (up to 70-80% at peaks) | Salesforce, WISMO Guide (2024); Sendcloud (2024) | Industry benchmark |
| Top-quartile monthly financial close | ~6 days | APQC (2024) | Industry benchmark |
| WISMO reduction with proactive tracking | Reductions of 25-90% depending on the case | Sendcloud (2024) | Vendor data |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| Manual win rate with evidence (fraud) | 8-20% | Chargeback.io, Chargeback Statistics (2026); Mastercard, State of Chargebacks (2025) | Industry benchmark |
| Average US win rate (all categories) | 54% (Brazil 36.9%) | Mastercard (2025), via Chargeflow | Industry benchmark |
| Win rate with evidence automation (CE 3.0) | Up to 80% | Chargeflow (2025) | Vendor data |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| Traditional BI adoption among employees | Stalls at 25-35% | Gartner, Survey Analysis on BI Adoption (2017-2022); BARC/Eckerson, via TechTarget (2022) | Industry benchmark |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| The 5-minute rule in lead response | 100x more likely to connect and 21x more likely to qualify vs. 30 min; industry average 47 hours; 78% buy from the first responder | Oldroyd, MIT/InsideSales (2007); Harvard Business Review, "The Short Life of Online Sales Leads" (2011); replicated by Casey Response AI (2026) | Industry benchmark |
| Best-in-class Net Revenue Retention | ~110-120%; venture-backed median 106% | ChartMogul, SaaS Retention Report (2024) | Industry benchmark |
| Top-quartile enterprise NRR (ACV >USD 100K) | 130%+; enterprise median 118% | Optifai (2026), cross-referencing ChartMogul | Industry benchmark |
| Average MQL-to-SQL | 13% cross-industry; B2B SaaS 18-22%; top quartile 25-35% | HubSpot via Only-B2B (2025) | Industry benchmark |
| Free-to-paid conversion | Median 8%; with card required ~30% (5x vs. no card) | ChartMogul, SaaS Conversion Report (2026) | Industry benchmark |
| Median CAC payback | 20 months in 2024 (up from 12-14 historically) | KeyBanc / Sapphire Ventures, Private SaaS Survey (2024), via Bantrr | Industry benchmark |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| Digital bank onboarding abandonment | 63% average (up to 70% in some countries) | Signicat (2020), via Innovatrics | Industry benchmark |
| Institutions losing clients to slow onboarding | 70% (highest figure on record) | Fenergo, KYC Report (2025), via FinTech Global | Industry benchmark |
| Healthy cost-to-collect | Below 2% of the amount recovered | Prodigal (2025), citing BLS/Deloitte | Industry benchmark |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| No-show rate before reminders | ~23% (median) | Systematic review, NCBI (2021) | Industry benchmark |
| No-show reduction with SMS reminders | 34-38% average | JAMA / Journal of Telemedicine and Telecare, via Klara (2023) | Industry benchmark |
| Claims denial rate | 5-10% benchmark; 30% rejected on first submission | MD Clarity (2024); HBMA via Voyant Health (2025) | Industry benchmark |
| First-pass acceptance target | 98% (efficient practices >90%) | BellMedEx (2024) | Industry benchmark |
| Adherence improvement with digital interventions | +11-19 percentage points (Cohen's d = 0.40, p<0.001) | JMCP (2020); JMIR (2025) | Industry benchmark |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| QC productivity improvement with digitization | +30-40% productivity; -50% QC costs; -65% deviations; -90% deviation closure time | McKinsey, "The Future of Pharma Quality Control" (2020-2024) | Industry benchmark |
| Scale-up and batch release acceleration | 20-30% | McKinsey (2020-2024) | Industry benchmark |
| Annual losses from cold-chain excursions | USD 2.5-12.5 billion globally (broad estimates up to USD 35 billion) | IATA via DHL (2019); AJHP (2023) | Industry benchmark |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| Demurrage per container per day | USD 2,008 average across the top 7 North American ports; USD 734 at LA/Long Beach | Container xChange + Drewry, Demurrage & Detention Benchmark Report (2023) | Industry benchmark |
| OTIF required by large retailers | Walmart 98% (3% of COGS penalty); Amazon >=90%; top-performer 95-98% | Walmart, via Zipline Logistics; Red Stag Fulfillment (2024) | Industry benchmark |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| First-time fix rate | 75-80% average; 88%+ best-in-class | Aberdeen Group via PTC/ServiceMax (2013-2024) | Industry benchmark |
| Cost of a technical visit (truck roll) | USD 200-300 base; USD 1,100+ fully loaded | TSIA / Aberdeen, via Field Technologies Online; Help Lightning (2024) | Industry benchmark |
| Visits requiring a second dispatch | 25-33% | Help Lightning (2024) | Industry benchmark |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| Aftermarket EBIT margin vs. new equipment | 25% aftermarket vs. 10% new equipment (analysis of 30 industries) | McKinsey, "Industrial Aftermarket Services: Growing the Core" (2017-2024) | Industry benchmark |
| Aftermarket operating margin | 2.5x that of new equipment | Deloitte, via Genalpha | Industry benchmark |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| World-class OEE | 85% (achieved by only ~3% of plants); discrete manufacturing average 66.8% | TPM/Nakajima standard and ISO 22400; OXMaint (2024-2026) | Industry benchmark |
| Unplanned downtime reduction with predictive maintenance | 30-50% within 12 months | UpKeep (2024), via Manufacturing Lead Generation; OXMaint (2024) | Industry benchmark |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| Conversion of retailers with omnichannel (BOPIS/curbside) | 3.7-3.9% vs. 3.1% without omnichannel (relative lift ~25%) | Digital Commerce 360, Omnichannel Report (2025) | Industry benchmark |
| Average ticket lift with omnichannel personalization and BOPIS | 10-15% | Digital Commerce 360 (2025) | Industry benchmark |
| Claim | Industry benchmark | Source | Type |
|---|---|---|---|
| Fully loaded cost of an SDR (US market) | USD 106,000-141,000 annually | The Bridge Group, SDR Metrics Report | Industry benchmark |
Some of the numbers above come from companies that sell the solution the data describes. We include them because they provide useful reference, but we always mark them as "vendor data" so you know they come from an interested party and may be subject to selection bias. When an independent source exists (consultancy, analyst, paper, industry association), we always prioritize it over the vendor's.
Most available benchmarks are global or centered on the United States. Data specific to enterprise artificial intelligence in LATAM (Argentina, Mexico, Brazil, Chile, Colombia and Peru) is still scarce in analyst literature. When we use a regional data point (for example, the payment dispute win rate in Brazil or WhatsApp Business cases in the region), we flag it explicitly. In the remaining cases, absolute values may differ by country, and it is best to take them as directional reference, not as an exact figure for your market.
Every results figure comes from one of three sources: an independent research benchmark (consultancies, analysts, papers), a figure published by an industry vendor (labeled as such), or our own case measured in production with a real client. All sources are listed with their link in the table on this page.
No. Benchmarks describe the industry average or range according to industry studies, not a guaranteed result. Actual results depend on the implementation, the quality of the data and the adoption of each organization. We explain this in detail in the disclaimer at the bottom of this page.
An industry benchmark comes from an independent source with no commercial interest in the result (for example, a McKinsey study or an academic paper). Vendor data comes from a company that sells the solution the data describes, so it may be biased; we include it only when there is no independent source, and we always make that clear.
We review and refresh the sources periodically, and we correct any figure that becomes outdated relative to the latest research. The date of the last update appears at the bottom of the page.
We do not publish it as a number. We replace it with a qualitative description, such as "significant and consistent improvement." We prefer to communicate less than to claim something we cannot back with evidence.
Because trust is demonstrated with verifiable evidence, not with statements. It is our way of standing out in an enterprise AI market full of unsupported figures, and an internal commitment not to communicate any number we cannot document.
The benchmarks on this page describe the average or range of each industry according to the research available at the time of publication. They are informational and for reference purposes.
None of the figures presented here constitutes a guarantee of result. Vantegrate does not guarantee that your organization will obtain the same numbers, or equivalent results, simply by adopting our tools. The benchmarks reflect what other organizations in the sector have reported, not what you will achieve.
Actual results vary from one company to another and depend on factors that are outside Vantegrate's control. Among them: the degree of responsibility and rigor each organization brings to implementing its processes, the correct configuration and use of the tools, the quality and cleanliness of the data available, the level of commitment and adoption from users, and the particularities of each company's operational, regulatory and market context. A partial implementation, low internal adoption or poorly defined processes can produce results substantially different from the reference benchmarks.
The content of this page also does not constitute legal, financial, regulatory or professional advice of any kind. For decisions that depend on these benchmarks, consult with qualified advisors in your jurisdiction.
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