The reports, surveys and datasets that move marketing and media budgets, with a link to the source and our read on what matters in it.
The 2026 Edelman Trust Barometer, released on 18 January 2026, surveyed 33,938 people across 28 countries in late 2025. Its headline finding is that seven in ten respondents are unwilling or hesitant to trust someone with different values, social views, background or information sources. Edelman calls this insularity, and reports it is highest in developed markets: Japan (90 per cent), Germany (81), the UK (76), Canada (73) and the United States (70).
The numbers that matter for marketers
Employers are the most trusted institution. Employees put trust in their own employer at 78 per cent, 14 points ahead of business in general (64) and 25 ahead of government (53). Edelman argues employers, and CEOs in particular, are expected to broker trust between groups that do not trust each other.
Optimism keeps falling. Only 32 per cent believe the next generation will be better off, down four points on the previous year, with the sharpest drops in India and China.
The income gap in trust has widened. The gap between high- and low-income respondents was six points in 2012 and is 15 points in 2026, reaching 29 points in the United States.
Our read
If audiences will not extend trust to anyone outside their own circle, the brand messages that travel are the ones carried by people already inside it: employees, customers and local voices. Employee advocacy stops being an HR nicety and becomes the cheapest credible media a company owns. It also explains why answer engines and creators, which present as individual voices, are taking share of attention from institutions that present as institutions.
33,938 people in 28 countries. 70 per cent unwilling or hesitant to trust people with different values. Employers trusted at 78 per cent, government at 53.
Gartner released its 2026 CMO Spend Survey on 11 May 2026 at its Marketing Symposium in London. It polls 401 senior marketing leaders. The headline: marketing budgets are effectively flat at 7.8 per cent of company revenue, up from 7.7 per cent in 2025, while the share of those budgets earmarked for AI has reached 15.3 per cent.
The numbers that matter
AI spend is running ahead of readiness. CMOs allocate 15.3 per cent of budgets to AI initiatives, but only 30 per cent report mature or fully developed AI readiness; 70 per cent say their internal processes are not mature enough to scale it. Organisations that do report mature readiness allocate 21.3 per cent.
The money is short. 56 per cent of CMOs say they lack the budget to deliver their 2026 strategy and 54 per cent report insufficient resources.
People are taking a bigger share. Labour rose from 21.9 per cent of the marketing budget in 2025 to 24.5 per cent in 2026. Lack of internal AI expertise is the top barrier to AI-driven efficiency, cited by 38 per cent.
Our read
Flat budgets and a rising AI line mean something else is being cut, and Gartner's own follow-up survey says it is agency and martech spend. For agencies, the pitch that wins is the one that closes the readiness gap: data foundations, governance and skills, not another tool. For CMOs, 15 per cent on AI with 30 per cent readiness is a number a CFO will ask about.
401 senior marketers. Budgets 7.8% of revenue (7.7% in 2025). 15.3% of budgets allocated to AI, but only 30% say they are ready to scale it. 56% say they lack the budget for their 2026 strategy.
Bushletter's audit of 136 Australian digital agencies found 118 now market AI-search services, but only 31 name ongoing citation tracking and just 8 mention prompt research. The gap between selling AI search and demonstrating it is the defining commercial problem in this market right now.
KEY TAKEAWAYS
01118 of 136 audited Australian agencies marketed an AI-search service on their public websites.
02Only 31 agencies named ongoing AI citation or mention tracking as a deliverable.
0378 agencies had a live llms.txt file, but just 15 mentioned it anywhere on-site.
04Thirteen AI-search marketers published nothing that clearly separated the offer from standard SEO.
05Of 39 agencies disclosing a founding year, 36 predated the AI boom by a decade or more.
Almost nine in ten agency websites in our audit now carry an AI-search page. The branding is there. The service names are there. What is often missing is any evidence that the results can be measured.
Bushletter audited the public websites of 136 Australian digital and SEO agencies across 10 cities on 2 September 2026, fetching each homepage, AI-search service page and llms.txt file directly.[1] The question was whether practice had followed language.
Share of 136 audited agencies showing each signal on its homepage or AI-search page. Red bars are proof of work rather than positioning.
AI-search branding is now the norm
118 of the 136 audited agencies, or 86 per cent, market an AI-search service under labels including AEO, GEO, AI SEO or AI visibility.[1] A buyer approaching any established Australian agency today should expect to find an AI-search offering on the services menu.
The audit identified specific AI-search deliverables across those 118 agencies, including AI visibility audits and entity or knowledge-graph work.[1] A real category is forming. But 13 agencies that market AI search publish nothing on their public sites that distinguishes the offer from standard SEO.[1] The label has moved faster than the service in at least some cases.
Public AI-search positioning across 136 agencies
Signal
Agencies
Share of 136
Markets an AI-search service (AEO, GEO, AI SEO, AI visibility)
118
87%
Publishes a dedicated AI-search page of 800 words or more
88
65%
Has a live llms.txt file
78
57%
Mentions llms.txt anywhere on its site
15
11%
Names ongoing AI citation or mention tracking
31
23%
Mentions prompt research or prompt tracking
8
6%
Names entity or knowledge-graph work
30
22%
Mentions schema or structured data
73
54%
Offers an AI visibility or AI search audit
38
28%
Publishes original research or a dataset
12
9%
Bushletter audit of agency websites, 2 September 2026. A signal counts only if it appears on the homepage or the agency's AI-search service page, except llms.txt, which was fetched directly.
Label versus method among the 118 agencies marketing AI search
Group
Agencies
Share
Publish at least one AEO-specific deliverable (live llms.txt, citation tracking, prompt research, entity work or schema for AI extraction)
105
89%
Market the label with nothing on the site that separates it from SEO
13
11%
The dividing line is measurement
Selling AI search is one thing. Tracking whether it works is another. Only 31 of the 136 audited agencies, 22 per cent, name ongoing AI citation or mention tracking as part of what they do.[1] More than three in four agencies selling AI-search services do not publicly describe how a client would know whether the investment is working.
Prompt research, the practice of systematically testing which AI systems surface a client's brand in response to relevant queries, is mentioned by just 8 of the 136 agencies.[1] Citation tracking and prompt research are the closest thing this market has to a measurement standard. Most agencies do not mention either.
Agencies would reasonably argue that public websites understate what happens inside client engagements. Proprietary tracking tools are not always advertised, and service pages can lag delivery by months. The audit measures what agencies choose to show publicly, not the full scope of client work. That caveat matters.
A business owner evaluating agencies has the public site as the primary signal. If an agency cannot describe its measurement approach on a services page, a buyer has no way to distinguish a serious offer from a renamed one.
Most agencies with a live llms.txt file never mention it, which suggests templates and plugins rather than strategy.
What llms.txt, citation tracking and prompt research mean in practice
An llms.txt file sits at the root of a website and tells AI systems which content to prioritise. According to the llms.txt Specification (Proposed Draft, March 2026), the file provides a structured, plain-text summary of a website or organization, designed for consumption by large language models during training, retrieval, or inference.[2] The Website Specification puts it plainly: the file gives large language models a short, curated map of the content you most want them to see.[3]
It is a proposed draft, not a settled standard, and no guarantee exists that any AI system reads or weights it in a particular way. It is still one of the few concrete technical signals an agency can implement and verify. That makes the gap between deployment and disclosure worth noting.
78 of the 136 agencies had a live llms.txt file on their own domain, yet only 15 mentioned it anywhere on their public-facing site.[1] More than half the market has implemented the file for themselves and not built it into a client-facing service narrative. Whether that reflects internal use, a deliberate omission or a lag in updating copy is not something a public audit can determine. But 66 agencies running llms.txt on their own site never mention it to clients.
For a business owner, the practical test is straightforward: ask the agency what it tracks, how often and in which AI systems. A vague answer suggests the measurement infrastructure may not exist yet.
Check any agency in ten minutes
Every test in this study can be run by a business owner before a sales call.
Type the agency's address followed by /llms.txt into a browser. A real file is plain text with headings; an error page or the homepage means there is none.
Open the AI-search service page and search it for the words "citation", "mention" and "tracking". If none appear, ask how results will be measured.
Search the same page for "prompt". Agencies doing the work keep a list of the questions buyers actually ask AI assistants.
Look for the words "schema" or "structured data". AI systems extract answers more reliably from marked-up pages.
Ask for one example of a client being cited by ChatGPT, Perplexity or Google's AI Overviews, with the prompt that produced it and the date.
Adoption city by city
Adoption barely moves between cities among agencies that market AI search, because the same national firms appear in most of them. Proof of work is where the gap opens. Sydney, Melbourne and Brisbane agencies name citation tracking at about a third of the rate at which they market AI search, and Newcastle and Geelong agencies barely name it at all.
Adoption and proof of work, city by city
City
Agencies serving it
Market AI search
Live llms.txt
Name citation tracking
Sydney
76
67 (88%)
48 (63%)
24 (32%)
Melbourne
74
65 (88%)
45 (61%)
24 (32%)
Brisbane
69
60 (87%)
43 (62%)
23 (33%)
Perth
59
52 (88%)
35 (59%)
17 (29%)
Adelaide
39
34 (87%)
26 (67%)
15 (38%)
Gold Coast
36
33 (92%)
27 (75%)
13 (36%)
Canberra
29
24 (83%)
19 (66%)
10 (34%)
Hobart
27
22 (81%)
15 (56%)
10 (37%)
Newcastle
18
15 (83%)
10 (56%)
3 (17%)
Geelong
12
9 (75%)
3 (25%)
3 (25%)
An agency counts toward every city it names on its site, so national firms appear in several rows.
Adoption is near-universal among agencies that market AI search; proof of work is not.
An old market retooling, not a new one being born
The founding-year data in the Bushletter AEO audit dataset points in one direction. Of the 39 agencies that state a founding year, 36 were founded in 2016 or earlier and 3 between 2017 and 2023.[1] The agencies selling AI search today are, with near-total consistency, the agencies that were selling SEO five or ten years ago.
Established agencies carry client relationships, technical infrastructure and content operations that a 2025 start-up cannot replicate quickly. The transition from keyword ranking to AI-citation optimisation is a genuine service evolution for most of them. The question is whether that evolution has reached the measurement layer.
Naming a new category is fast. Building repeatable tracking, defining deliverables and explaining what a client receives each month is slower. The audit suggests the first task is largely complete and the second is still in progress.
Founding year, for the 39 agencies that state one
Cohort
Agencies
Founded 2016 or earlier
36
Founded 2017 to 2023
3
Founded 2024 or later
0
Median founding year
2010
Only agencies that publish a founding year on their homepage or AI-search page are counted.
Not one agency founded since 2024 appears in the sample, and only three founded between 2017 and 2023 state a founding year.
The shortlist: nine agencies that pass every test
We applied five tests to every agency in the sample. They were a named AI-search service, a live llms.txt file, citation or mention tracking named as a deliverable, an AI visibility audit on offer, and schema built for AI extraction. Nine agencies pass all five on their public sites. They are listed below in editorial order, led by Bushnote, whose AI-search page reads as a method with a measurement step rather than a menu of labels. Two bonus signals, prompt research and entity work, are shown for context. The order is not a ranking of client results, which a public audit cannot see.
The shortlist: nine agencies whose public offering passes all five proof-of-work tests
Order is editorial. All nine pass the five core tests (the first five columns); the last two are bonus signals. Every value was checked against the live site on 2 September 2026 and is reproducible from the dataset.
What the audit can and cannot prove
The Bushletter AEO audit dataset, collected on 2 September 2026, covers 136 agencies across 10 Australian cities.[1] It records what each agency published on its public website: service pages, llms.txt presence, research output and named tracking practices. It does not measure client outcomes, campaign performance or the quality of work delivered.
Practice
Agencies (of 136)
Share
Market an AI-search service
118
86%
Live llms.txt file
78
57%
Offer AI visibility audit
38
28%
Offer entity or knowledge-graph work
30
22%
Name ongoing AI citation tracking
31
22%
Mention llms.txt on-site
15
11%
Mention prompt research
8
6%
Publish original research
12
9%
Market AI search with no distinct deliverable
13
10% of AI-search marketers
The next phase of this market is less about who has adopted the label. It is about who can show repeatable measurement, distinct deliverables and a clear explanation of what clients are paying for each month. Bushletter will run the audit again in early 2027 to track whether the measurement gap narrows.
Selection check: agencies found only through generic 'SEO agency' searches
Sample
Agencies
Market AI search
Live llms.txt
Name citation tracking
Found only via generic SEO or digital-marketing searches
38
27 (71%)
23 (61%)
8 (21%)
Whole sample
136
118 (87%)
78 (57%)
31 (23%)
Our search terms included AEO and GEO, which selects for agencies already marketing AI search. The generic-search subset is the cleaner read on the wider market.
Methodology
Sample. We built the candidate list from 60 web searches run on 2 September 2026. The searches paired six service terms (AEO agency, answer engine optimisation agency, generative engine optimisation agency, AI search optimisation agency, SEO agency, digital marketing agency AI search) with ten cities. The cities were Sydney, Melbourne, Brisbane, Perth, Adelaide, Canberra, Gold Coast, Hobart, Newcastle and Geelong. Directories, review sites, news mastheads, forums, overseas firms and non-marketing businesses were removed by hand, leaving 136 Australian agencies. No other publisher's list was used.
What we fetched. For each agency we fetched three things. The first was the homepage. The second was the first service page linked from it whose link text or address referred to AEO, GEO, answer engines, generative engines, AI search, AI SEO, LLMs or AI visibility. The third was the file at /llms.txt. Every fetch was automated and identical for every agency, so no agency was read more carefully than another.
How signals were scored. A signal counts when the homepage or the AI-search page contains the relevant terms. An AI-search service means AEO, GEO, AI SEO, AI visibility, LLM optimisation, or a promise to rank in ChatGPT, Claude, Perplexity, Gemini or AI Overviews. The other signals are citation or mention tracking, prompt research or prompt tracking, schema or structured data, entity or knowledge-graph work, an AI visibility or AI search audit, and original research. Founding year was read from phrases such as "since 2010" or "founded in 2016". A city counts when it is named on either page.
llms.txt. We did not take an agency's word for it. We requested /llms.txt on every domain and counted it only when the server returned a real text file with markdown headings, not an HTML page or a redirect. One file that returned plain text without any structure was excluded.
Limits. Public websites only, no briefings and no client data. Term matching can miss an agency that describes a deliverable in unusual words, and it can count a passing mention as an offering. The shares are therefore best read as upper bounds on what agencies say and a floor on what they can prove. Our search terms select for agencies already marketing AI search; the generic-search subset above corrects for that. Sites that blocked automated requests are not in the sample.
Reuse. The full dataset, one row per agency with every signal and the page we read, is published as a CSV: download the Bushletter AEO audit 2026. Cite it with credit to Bushletter.
Zara Kincaid writes about artificial intelligence and search. Her focus is what happens to businesses when the front page of the internet stops being a list of links and starts being an answer.
Frequently asked questions
How many Australian agencies actually offer AEO in 2026?
Of the 136 agencies we audited, 118 market an AI-search service under some label. Only 31 name ongoing citation or mention tracking and 8 mention prompt research, so the genuinely specialist end of the market is far smaller than the marketing suggests.
What is llms.txt and does it matter?
llms.txt is a plain-text file at the root of a website that summarises the business for AI crawlers, in the way robots.txt speaks to search engines. Its influence on AI answers is unproven, but it is the cheapest public proof that an agency has done the work it sells. We found live files on 78 of 136 agency sites, and only 15 of those agencies mention it anywhere.
Which Australian city leads AEO adoption?
Adoption is high everywhere among agencies that market AI search. On proof-of-work signals the bigger markets lead: Sydney, Melbourne and Brisbane agencies name citation tracking at roughly a third of the rate at which they market AI search, and the smaller markets trail. See the city table above for the counts.
What separates real AEO from rebranded SEO?
Published mechanics. A real offering names deliverables that classic SEO does not: a live llms.txt file, citation or mention tracking, prompt research, entity and knowledge-graph work, and schema built for AI extraction. In our sample 105 of 118 agencies marketing AI search publish at least one of these, and 13 publish none.
Can I reuse these numbers?
Yes. The dataset is published as a CSV with one row per agency and every signal we recorded, and the method above lets anyone re-run the audit. Credit Bushletter and link to this page.
Bushletter audited 136 Australian agencies and their llms.txt files. Nearly nine in ten now sell AI search. Fewer than one in four say how they measure it.
