Ten US enterprise IT budget owners — CIO, IT, engineering and systems managers, including a regulated FDA/ISO voice and a K-12 public-education operator — on how software spend is being reallocated across existing SaaS, AI-native tools, and custom builds. Framed for the software investors and analysts who need to read demand trajectory, switching behaviour, and wallet-share migration.
AI is not a new budget line — it is a reallocation. Watch the leading indicators of wallet migration: AI approaching ~15–20% of the software mix, seat compression in notes / grammar / BI-viewer categories, pilot pass rates, vendor-count decline, and renewal-driven switches fuelled by bundling.
The panel converges hard on "hold flat and reallocate" — but splits where the seniority and sector fault lines meet: has AI actually changed the build-versus-buy calculus? Four say consolidate into the suite; four have become build-lite converts; two are frozen by regulation and privacy.
Q3 of 7: "Has AI changed the build-versus-buy calculus for your team? Two years ago, building a custom tool meant…" · Position split analyst-classified from the study's segment and divergence analysis — see methodology.
Hover any respondent to read their position, as reported in the study analysis. Colour follows the three camps above.
Suite-native AI with contractual protections, one-in / one-out — top-down consolidation and ROI mandates drive ~60% of the budget motion.
A four-week internal build replaced the vendor SKU at ~60% lower unit cost, 90–95% accuracy, with a small human-review lane.
Both poles share the same discipline: narrow, short-payback, governed. Build-lite wins where the problem is small and maintenance is light; the suite wins where scale, SLA, validation or broad coverage are required. The systems of record — ERP, CRM, HRIS, QMS/ALM — stay with vendors in every account.
One row per respondent, one column per condition the study ties them to. Column totals rank the market's demands — every dollar of AI now passes a KPI-gated pilot.
| Respondent | Data-use guarantees |
KPI-gated pilot + ROI offset |
Cost predictability (caps, FinOps) |
Suite-native / consolidation |
Validation & auditability |
|---|---|---|---|---|---|
| Ryan Macieltech PM · San Jose | |||||
| Raymond Navarrogatekeeper · Chino | |||||
| Miles Muradops research · Oakland | |||||
| Jeremy Millerregulated · rural NJ | |||||
| Abigail McclungK-12 ed · rural NY | |||||
| James Chengops GM · Boston | |||||
| Matthew Hughessystems analyst · Austin | |||||
| Nicholas AusbieCTO · Hollywood FL | |||||
| Aaron MonahanCTO · Franklin TN | |||||
| Apryl Ellisonsystems analyst · rural MO | |||||
| demanded by | 6/10 | 10/10 | 4/10 | 7/10 | 7/10 |
Six of the seven questions converge hard. Build-versus-buy is the one real fault line. Splits are analyst-classified from the study's segment and divergence analysis, not from per-response coding.
10 respondents recruited from a census-grounded synthetic population of 340,000 US residents, with a regulated FDA/ISO voice and a K-12 operator recruited deliberately to capture conservative outliers.
Ten US enterprise IT budget owners describe flat budgets with AI grown from low-single-digits to mid-teens share, funded by SaaS consolidation and seat right-sizing. The panel converges on "hold flat and reallocate," and splits only on whether AI has changed the build-versus-buy calculus.
Signals to track: AI at ~15–20% of software mix, seat compression in notes/grammar/BI-viewer categories, pilot pass rates, vendor-count decline, and renewal-driven switches.
FishDog · Research without respondents · Fielded May 27, 2026 · 10 respondents · 7 questions · 70 responses · v2.0, rebuilt 2026-07-11. Positions and demographics are from the study record; the build-vs-buy split is analyst-classified from the study's divergence analysis. How this report is structured.