Most AI research asks executives what they plan to do. This report starts somewhere messier and more useful: what business owners actually asked for when they were considering an implementation, and what changed in selected delivered systems afterward.
The demand dataset contains 1,305 inquiry-form submissions received between November 19, 2024 and July 10, 2026. Of those, 949 selected a reason for the call, 701 wrote a detailed brief, and 273 disclosed annual revenue. A separate review covered 50 inquiry calls, 41 of which contained substantive discussion.
The dataset and the limits
| Source | Records | What it contributes |
|---|---|---|
| Inquiry forms | 1,305 submissions | Call reason, revenue band, website, written brief, and budget answers where provided. |
| Detailed written briefs | 701 responses | The prospect's own description of the outcome or process they wanted. |
| Revenue disclosures | 273 responses | Self-reported revenue bands used for the segment comparisons below. |
| Inquiry-call review | 50 calls; 41 substantive | Directional pain-point coding across real sales conversations. |
| Delivery evidence | Selected verified cases within 60+ businesses | Before-and-after operational and commercial outcomes. These are examples, not portfolio averages. |
This is not a random market survey. These people found Reprise AI and considered buying AI services, so the sample over-represents organizations already interested in implementation. Revenue is self-reported. Blank answers are excluded rather than treated as a no. One person can submit more than once because the export is counted by submission, not deduplicated individual.
The public files contain aggregates only; the raw export is not published because it contains names, emails, websites, and commercially sensitive free text. You can download the anonymized inquiry aggregates and selected case-outcome table.
Finding 1: company size changes what buyers ask for
Across all 949 respondents who selected a reason, the most common answer was developing a custom AI solution or hiring an AI engineer. That is what an early market looks like: buyers frame the problem as a build.
| Selected reason | All respondents | $500K+ revenue | $1M+ revenue |
|---|---|---|---|
| AI Transformation | 147 of 949 (15.5%) | 53 of 108 (49.1%) | 39 of 73 (53.4%) |
| Custom AI solution / AI engineer | 458 of 949 (48.3%) | 40 of 108 (37.0%) | 25 of 73 (34.2%) |
| Re-selling / white-label | 167 of 949 (17.6%) | 13 of 108 (12.0%) | 7 of 73 (9.6%) |
| Educating the team | 61 of 949 (6.4%) | 2 of 108 (1.9%) | 2 of 73 (2.7%) |
The pattern reverses as revenue rises. AI Transformation becomes the majority choice among $1M+ respondents, while the isolated-build and reseller categories shrink. This does not prove that revenue causes a different buying preference. It does show that larger operators arrive with a different problem definition.
Finding 2: the bottleneck is operational, not model access
The separate call review points to the same diagnosis from another angle. Across 41 substantive conversations, the coded pain points were approximately:
| Pain point | Share of substantive calls |
|---|---|
| Manual or repetitive processes hurting productivity | Approximately 70% |
| Poor lead follow-up, missed calls, or low show rates | Approximately 45% |
| Disconnected systems or no unified data | Approximately 35% |
| No internal AI capability or implementation experience | Approximately 30% |
| CRM or ERP integration challenges | Approximately 25% |
| Team training or adoption gap | Approximately 25% |
These are not model problems. They are workflow, data, and adoption problems. A more capable model does not fix a lead sitting in three systems, a close process nobody has documented, or a team that cannot tell what the agent is allowed to do.
That is why implementation order matters more than the tool list. The practical scoring system is covered in what to automate first, and the common architecture failures are documented in why AI projects fail.
Finding 3: the largest gains came from capacity, not software savings
The implementation side of the report is deliberately narrower. The table below uses only cases marked safe to present as delivered Reprise AI work. It excludes unverified website cases, a competitor-contaminated insurance example, and a second property-management study that still needs identity confirmation.
| Business | Operational change | Commercial result |
|---|---|---|
| Tax and advisory firm | Senior repetitive time fell from 50% to 18%; about 11 hours per senior per week recovered; lead response fell from two days to under 60 seconds. | Approximately $210K in added first-year advisory revenue, with the same team. |
| Recruiting firm | Admin time fell from 13 to 3 hours per week; time-to-fill fell from 40 to 26 days. | About three additional placements per recruiter per year and approximately $340K in added first-year revenue. |
| Advertising agency | Automated reporting and analysis freed roughly 25% of strategist capacity. | Approximately $504K in additional annual profit within 90 days. |
| Property-management firm | Lead response fell from 3–4 hours to 18 seconds. | $99,840 first-year value on $81K spend, reported as 123% ROI. |
| Heir-hunting firm | Lead-processing capacity rose from 70 to 250 leads per week; more than 500 hours per year saved. | Four additional deals attributed to the expanded capacity. |
The recurring economic mechanism is not a cheaper software license. It is recovered capacity converted into faster response, more client work, more placements, or more deals. Time saved matters only when the business knows what higher-value work will absorb it.
Finding 4: transformation still starts with one workflow
The demand data says larger companies want transformation. The delivery evidence says the safest way to get there is almost the opposite of a transformation program: start narrow.
- 1Measure one workflow before touching it. Capture current volume, handling time, error rate, response time, and the business outcome attached to it.
- 2Choose high-volume, low-judgment work. A frequent 20-minute task is usually a better first build than an impressive three-hour task done once a month.
- 3Draft before acting. Agents propose; people approve. Expand autonomy only after the exception rate is known.
- 4Prove adoption, not just technical success. A system that runs correctly but is bypassed by the team is not deployed.
- 5Expand from the data spine. Once one workflow is trusted, connect the next adjacent process rather than starting another isolated tool.
The accounting implementation shows the pattern cleanly: transaction categorization, client-communication drafting, and lead response were installed one at a time. The result was not three demos. It was capacity that showed up in advisory revenue.
What leaders should measure
AI programs get vague when the scoreboard is vague. Before the first build, define the metrics that can move:
- Cycle time: hours or days from trigger to completed work.
- Human handling time: minutes of staff effort per unit, separated from elapsed time.
- Exception rate: the share of work routed to a person because confidence or policy thresholds were not met.
- Response time: time from a lead or customer action to a useful reply.
- Capacity converted: added clients, placements, reports, closes, or cases completed with the same team.
- Commercial outcome: revenue captured, margin added, churn prevented, or cost avoided — with the attribution rule written down before launch.
Methodology and reproducibility
- Inquiry dataset: one row per form submission, November 19, 2024 through July 10, 2026. Blank optional fields were excluded from that field's denominator.
- Revenue segmentation: $500K+ includes $500K–$1M, $1M–$5M, $6M–$20M, and above $20M. $1M+ excludes the $500K–$1M band.
- Reason comparison: only rows containing both a revenue band and a selected call reason were included. That produces 108 records at $500K+ and 73 records at $1M+.
- Call review: 50 recorded inquiry meetings were coded; 41 contained substantive discussion and nine were silent or no-shows. Pain-point categories overlap and are rounded.
- Delivery evidence: the business count is the canonical company-level count used by Reprise AI. The outcome table is a selected set of verified cases, not a statistical summary of all 60+ businesses.
- Privacy: public downloads contain aggregates and anonymized business categories only. Raw personally identifiable and commercially sensitive form data is not released.
Questions about the methodology or corrections can be sent through the Reprise AI contact page. This report will be updated when the next complete data export is reviewed.