On this page
In SMBLoan’s survey of 200 Australian business owners and decision-makers, 80% said their business used AI tools. Across all respondents, 14% said AI saved more than half of labour time. That is approximately 17.5% of AI users, using the rounded 80% adoption figure as the denominator.
The results are self-reported estimates. No timesheets, payroll records or financial accounts were inspected to confirm saved hours or profit. The survey can describe what these respondents believed; it cannot establish measured productivity gains across Australian businesses.
How much time respondents said AI saved
| Response | All respondents |
|---|---|
| More than 50% of labour time | 14% |
| 26–50% | 19% |
| 11–25% | 23% |
| 1–10% | 16% |
| No time saved | 7% |
| Does not use AI | 20% |
The displayed percentages total 99% because the published results are rounded. Of all respondents, 33% reported savings above a quarter of labour time. Dividing 33 by the reported 80% adoption figure gives about 41% of AI users. Likewise, the 7% reporting no time saved is about 9% of AI users. The previous claim that 20% of AI users reported no savings confused non-users with users reporting no saving.
These ratios are calculations from rounded percentages, not a recovered respondent-level cross-tabulation. “Time saved” also does not mean the same percentage of staff could be removed: freed time may go into checking output, serving more customers or work that was previously left undone.
Profit expectations were hypothetical
The second question asked what better, or “perfected”, AI might contribute in future. The table reports the expected profitability bands printed for four functions. These are expectations about hypothetical capability, not profit growth already achieved.
| Function | Up to 10% uplift | 11–20% | 21–30% | No meaningful reduction in workload |
|---|---|---|---|---|
| Writing and communication | 29% | 30% | 15% | 25% |
| Sales and customer service | 25% | 28% | 15% | 32% |
| Administration and workflow | 26% | 31% | 20% | 23% |
| Decision-making and analytics | 26% | 28% | 15% | 32% |
The source’s final response option refers to workload, although the question asks about profitability. That wording difference limits interpretation. Administration and workflow had 20% expecting a 21–30% profitability uplift. No published national table substantiates the earlier “up to 38% higher profitability” headline, which has been withdrawn.
A hoped-for improvement in profit is not a business case for a particular subscription or loan. The survey did not price implementation, training, security review, human checking or the cost of replacing a tool. It also did not establish whether an improvement would come from extra revenue, lower costs or both.
Compare the 2024 and 2026 questions carefully
The earlier 2024 report surveyed 205 respondents: 25% said they were using AI, while another 35% planned to adopt it. The later survey reported 80% current use among 200 respondents and explicitly included AI embedded in workplace software and customer-service systems.
These were different samples and differently framed questions, not repeated observations of the same businesses. Planned adoption in 2024 cannot be counted as actual adoption. The difference between 25% and 80% is therefore a difference between published survey results, not a verified adoption growth rate. We retain the earlier study here as context rather than as a separate current AI article.
Build a finance case from measured savings
Start with one repeated task and a baseline: hours used, errors needing correction, delivery time and the cost of the existing process. Run the proposed tool long enough to include setup and review work. Count the time needed to check outputs and handle exceptions, then estimate the net change.
Distinguish time released from cash saved. If staff remain employed, the benefit may be capacity for extra work rather than a smaller wage bill. A forecast should show what that extra capacity earns and when customers pay. Subscription fees, implementation and training begin before those receipts are certain.
Capital equipment and software subscriptions can need different funding structures. The equipment-finance guide explains asset funding; the line-of-credit guide covers recurring cash gaps. Neither should be justified solely by the highest hoped-for return in a survey.
State tables and limits
The retained report does not disclose respondent counts by state. We have removed ACT, Tasmania and Northern Territory columns from the displayed profitability tables rather than infer their tiny sample sizes from rounded cells. Even the five displayed states have unverified subgroup bases, so the results should not be used to rank state performance.
The original Western Australian creative-AI cell is 17% for the 21–30% uplift band, not the 33% stated in the former narrative. The corrected table is retained below without a claim of statistical significance. Download the original subgroup tables, including omitted columns, for source tracing; their presence does not validate the samples.
Historical state tables: subgroup counts unavailable
Reported labour savings
BY STATE | NSW | VIC | QLD | SA | WA |
|---|---|---|---|---|---|
AI saves more than 50% of our time or labour | 13% | 16% | 9% | 11% | 22% |
AI saves 26 to 50% of our time or labour | 26% | 18% | 20% | 6% | 17% |
AI saves 11 to 25% of our time or labour | 20% | 23% | 22% | 28% | 22% |
AI saves 1 to 10% of our time or labour across the business | 15% | 11% | 20% | 28% | 22% |
AI currently saves 0%, it has not reduced our workload | 9% | 5% | 4% | 11% | 9% |
The business does not currently use AI tools. | 18% | 26% | 24% | 17% | 9% |
Writing and communication expectations
| Profitability | NSW | VIC | QLD | SA | WA |
|---|---|---|---|---|---|
| Up to 10% more profitability | 35% | 18% | 29% | 39% | 30% |
| 11 to 20% more profitability | 28% | 36% | 20% | 22% | 43% |
| 21 to 30% more profitability | 20% | 16% | 18% | 6% | 9% |
| None, AI improvements would not meaningfully reduce our workload. | 18% | 30% | 33% | 33% | 17% |
Sales and customer service expectations
| Profitability | NSW | VIC | QLD | SA | WA |
|---|---|---|---|---|---|
| Up to 10% more profitability | 24% | 23% | 24% | 28% | 26% |
| 11 to 20% more profitability | 28% | 25% | 36% | 22% | 30% |
| 21 to 30% more profitability | 19% | 13% | 9% | 6% | 26% |
| None, AI improvements would not meaningfully reduce our workload. | 30% | 39% | 31% | 44% | 17% |
Administration and workflow expectations
| Profitability | NSW | VIC | QLD | SA | WA |
|---|---|---|---|---|---|
| Up to 10% more profitability | 28% | 18% | 38% | 28% | 26% |
| 11 to 20% more profitability | 26% | 41% | 18% | 33% | 26% |
| 21 to 30% more profitability | 25% | 15% | 22% | 6% | 30% |
| None, AI improvements would not meaningfully reduce our workload. | 21% | 26% | 22% | 33% | 17% |
Decision-making expectations
| Profitability | NSW | VIC | QLD | SA | WA |
|---|---|---|---|---|---|
| Up to 10% more profitability | 26% | 23% | 31% | 28% | 22% |
| 11 to 20% more profitability | 28% | 30% | 22% | 28% | 26% |
| 21 to 30% more profitability | 15% | 15% | 11% | 6% | 22% |
| None, AI improvements would not meaningfully reduce our workload. | 31% | 33% | 36% | 39% | 30% |
Creative-AI expectations
| Profitability | NSW | VIC | QLD | SA | WA |
|---|---|---|---|---|---|
| Up to 10% more profitability | 24% | 18% | 31% | 33% | 26% |
| 11 to 20% more profitability | 33% | 31% | 24% | 17% | 26% |
| 21 to 30% more profitability | 19% | 18% | 18% | 17% | 17% |
| None, AI improvements would not meaningfully reduce our workload. | 25% | 33% | 27% | 33% | 30% |
Publication and correction record
The retained website publication date is 17 February 2026, while the source report is labelled March 2026. We have not recovered a fieldwork record that resolves that mismatch. The page therefore uses “2026 survey” and preserves both historical labels here; it does not invent a March fieldwork or February release date.
The October 2026 correction separates all-respondent and AI-user denominators, removes the unsupported 38% headline and causal claims about integration, and discloses missing recruitment, weighting, response-rate and subgroup information. Contact hello@smbloan.com.au for source questions. The figures are not current monitoring of AI adoption.