Independent AI research for small and mid-sized businesses

The library at a glance

A quick read on the scope of what’s covered before you dig into any single report.

Start here: the AI Adoption Report

All published reports

Every AIBizMaster report is built on named primary sources, states its own methodology and update policy, and is reviewed on a regular cycle as the underlying data changes. Each card below is a quick summary; the Research Library table further down this page is where every report actually links out to its full page.

Explore AI research and resources by topic

Each card below represents one subject area, not a page type, and not a legal or trust page, just a genuine topic in AI for small business. Six are original research reports, listed and linked in full in the Research Library table further down this page. The rest are AIBizMaster resources that turn that research into an actual decision, including our Tools hub, which has free calculators and assessments for sizing a decision with your own numbers before you commit any budget.

Explore research by industry

None of our reports are industry-specific yet, each one draws on cross-industry data by design. But the reasons AI matters, and the software categories worth looking at, genuinely differ from one kind of business to the next. The six examples below apply our cross-industry findings to specific business types, each linking onward to the report where its data comes from.

Built for more than one kind of reader

Every report is written first for the small business owner making a buying decision, that’s the audience every sentence gets edited for. But the underlying sourcing standard (named primary sources, stated confidence levels, disclosed methodology) makes the same data useful well beyond that original audience. Agencies use it to set client expectations, investors use it to read market structure, and journalists use it because every figure already carries the attribution a byline needs. The table below maps each reader type to where in this library they should actually start, rather than making everyone read all six reports front to back.

Most recently published or updated

AI pricing, funding, and productivity data move fast enough that a report published even six months ago can understate what’s actually happening in the market. This timeline exists so you can tell, at a glance, whether the number you’re about to cite is from last week or last year, and jump straight to whichever report just changed.

1. 1

   Jul 13, 2026: AI Investment Statistics 2026

   Published: global venture funding, big tech capex, and named funding rounds.

2. 2

   Jul 13, 2026: AI Productivity Statistics 2026

   Published: peer-reviewed evidence on time saved and where AI hurts productivity.

3. 3

   Jul 13, 2026: AI Implementation Statistics 2026

   Published: why most AI implementations fail, and what the successful minority do differently.

4. 4

   Jul 12, 2026: AI Pricing Benchmarks 2026

   Updated: expanded vendor pricing directory across 40+ named platforms.

5. 5

   Jul 11, 2026: AI ROI Report 2026

   Published: cost savings, revenue impact, and payback periods by industry and business size.

6. 6

   Jul 9, 2026: AI Adoption Report 2026

   Published: the foundational report on who’s actually using AI in 2026.

Every report, published and upcoming

Search or scan the full library below. Reports marked “Coming Soon” are in active research and not yet published, they’re listed here for transparency about our roadmap, not as placeholder content.

How AIBizMaster research is built

Our six-stage process

1. 1

   Collect

   Identify every named primary source relevant to the topic: analyst firms, peer-reviewed studies, government datasets, and direct vendor disclosures.

2. 2

   Verify

   Check every statistic against its original publisher, not a secondary aggregator, before it’s eligible for inclusion in a report.

3. 3

   Compare

   Weigh conflicting figures across sources, note methodological differences, and flag disagreements rather than picking whichever number is most convenient.

4. 4

   Analyze

   Identify what the verified data actually means for a small business owner, not just what it means in the abstract.

5. 5

   Publish

   Ship the report with full source attribution, a stated confidence level, and a declared update policy, never as an unattributed list of facts.

6. 6

   Update

   Re-run verification on a monthly-to-quarterly cadence depending on category volatility, and revise the report, not just the byline date, when the data changes.

Standards behind every report

Citing AIBizMaster research

Questions about a specific use case not covered above? We’re generally permissive about this and would rather answer a question than have research transparency work against wider use of the data.

Cross-report findings at a glance

A single statistic in a single report is a data point; the same statistic set next to a related one from a different report is a pattern. The two charts below pull figures from across the library specifically because they only mean something in relation to each other. Adoption without a measured result, and funding without a sense of scale, are both easy to misread in isolation.

Original AIBizMaster analysis, across reports

Each report in this library is produced independently, on its own dataset, but read together, three patterns keep resurfacing regardless of which report you start from. These aren’t summaries of any single report. They’re what emerges only when you compare all six against each other, the kind of analysis a standalone report can’t do on its own.

The most-cited numbers across our research

These are the specific figures other writers, consultants, and internal decision memos cite most often from our library, the ones worth knowing even if you never read the full report they come from. Each one links back to its source report, where the full context, methodology, and confidence level live; treat the number here as the headline, not the whole story.