IT Budget Decision-Makers: SaaS vs AI Tools vs Custom Builds
Understand how enterprise IT budget decision-makers are rethinking their software spending allocation across three competing vectors: existing SaaS subscriptions, emerging AI-native tools, and custom-built internal solutions. Explore which SaaS categories are most vulnerable to AI disruption, how the build-vs-buy calculus has shifted, whether AI spend is additive or cannibalising existing budgets, and what the CFO/board narrative is around IT investment in 2026. Surface the signals that enterprise software investors and analysts need to understand demand trajectory, switching behaviour, and wallet share migration.
Research group: 10 US enterprise IT decision-makers (CIO/IT/engineering and systems managers), including regulated (FDA/ISO) and K–12 public-education voices to capture conservative outliers. Budgets are largely flat; AI grew from low-single digits to mid-teens share, funded mainly by SaaS consolidation and seat right-sizing, while “custom” shifted to integrations, data plumbing, governance and lightweight automation. Core systems of record remain; cuts hit meeting transcription/notes, writing/grammar, small surveys/forms, async video, and BI viewer/NLQ overlays, with build‑lite LLM layers (RAG/search, support triage, document intake) expanding where speed-to-value, per-seat vs usage economics, and governance favor in‑house. AI spend is mostly cannibalized from existing lines under 60–90 day, KPI‑gated pilots; procurement now requires no training on customer data, retention/residency controls, SSO/SCIM, prompt/output logs, model/version transparency, and hard spend caps. Board/CFO narrative is “hold flat and reallocate”: prefer suite‑native AI, consolidate vendors, and fund only outcome‑backed deployments (e.g., 10–30% productivity or 20–25% ticket deflection) with offsets. Takeaways: Operators should run immediate seat/SKU audits in vulnerable categories, enable suite AI, enforce KPI‑gated pilots, and sunset overlaps at renewal; vendors win by embedding governed AI that replaces 2–3 point tools with defensible unit economics and clean data terms; investors should track leading indicators of wallet migration-AI share of software approaching ~15–20%, compression in notes/grammar/BI‑viewer seats, pilot pass rates, vendor‑count decline, and renewal‑driven switches fueled by bundling/pricing.
Ryan Maciel
I’m a San Jose tech project manager, husband, and father who makes decisions with quick ROI checks: time saved, durability, proof, and family utility. I stay active and health-conscious, but optimize for capability and low friction, not perfection.
Raymond Navarro
I’m a bilingual tech sales manager in Chino, divorced and mortgage-carrying, with solid income but little margin for avoidable risk. I choose proven, time-saving options that protect independence, budget, and health without adding friction.
Miles Murad
I’m Miles Murad, a 39-year-old Oakland analytics guy, married with three kids and a calendar full of school runs, dashboards, and practical decisions. I trust evidence over hype, keep family life steady, and try to stay active enough to keep up.
Jeremy Miller
I’m a 49-year-old tech sales manager in rural New Jersey, married with one child, balancing a high-income, structured life around family, church, and dependable routines. I’m practical, health-conscious, and willing to pay for quality that clearly saves time.
Abigail Mcclung
I’m Abigail McClung, 42, a rural New York logistics manager who keeps things running smoothly—and personally, I’m very active with good sleep. I drink moderately, never smoke, and rely on birth control, not a stack of prescriptions.
James Cheng
I’m a Boston tech operations manager who optimizes for reliability, transparency, and low friction. I spend on comfort, convenience, and durable value, while managing health and routine with practical, sustainable choices rather than overhaul.
Matthew Hughes
I’m a veteran-turned-systems analyst in Austin: married, mortgage-paying, and allergic to hype. I’d rather compare specs than chase trends, keep weekends part productive and part barbecue, and keep telling myself I’ll finally get back into a healthier routine.
Nicholas Ausbie
I’m a steady-handed CTO in Hollywood, Florida—married, raising two kids, and more impressed by clear plans than shiny promises. My days run on faith, follow-through, decent coffee, and practical habits that keep both my family and health on track.
Aaron Monahan
I’m a 47-year-old CTO in Franklin, Tennessee, balancing executive triage with family life, Hindu practice, and two opinionated kids. I spend pragmatically on quality and convenience, stay skeptical of hype, and take health more seriously after cancer, even...
Apryl Ellison
I’m a 35-year-old computer systems analyst in rural Missouri, balancing full-time tech work, two kids, and a mortgage with a practical, systems-minded approach. I value reliability, clear information, and choices that save time, reduce hassle, and support l...
Ryan Maciel
I’m a San Jose tech project manager, husband, and father who makes decisions with quick ROI checks: time saved, durability, proof, and family utility. I stay active and health-conscious, but optimize for capability and low friction, not perfection.
Raymond Navarro
I’m a bilingual tech sales manager in Chino, divorced and mortgage-carrying, with solid income but little margin for avoidable risk. I choose proven, time-saving options that protect independence, budget, and health without adding friction.
Miles Murad
I’m Miles Murad, a 39-year-old Oakland analytics guy, married with three kids and a calendar full of school runs, dashboards, and practical decisions. I trust evidence over hype, keep family life steady, and try to stay active enough to keep up.
Jeremy Miller
I’m a 49-year-old tech sales manager in rural New Jersey, married with one child, balancing a high-income, structured life around family, church, and dependable routines. I’m practical, health-conscious, and willing to pay for quality that clearly saves time.
Abigail Mcclung
I’m Abigail McClung, 42, a rural New York logistics manager who keeps things running smoothly—and personally, I’m very active with good sleep. I drink moderately, never smoke, and rely on birth control, not a stack of prescriptions.
James Cheng
I’m a Boston tech operations manager who optimizes for reliability, transparency, and low friction. I spend on comfort, convenience, and durable value, while managing health and routine with practical, sustainable choices rather than overhaul.
Matthew Hughes
I’m a veteran-turned-systems analyst in Austin: married, mortgage-paying, and allergic to hype. I’d rather compare specs than chase trends, keep weekends part productive and part barbecue, and keep telling myself I’ll finally get back into a healthier routine.
Nicholas Ausbie
I’m a steady-handed CTO in Hollywood, Florida—married, raising two kids, and more impressed by clear plans than shiny promises. My days run on faith, follow-through, decent coffee, and practical habits that keep both my family and health on track.
Aaron Monahan
I’m a 47-year-old CTO in Franklin, Tennessee, balancing executive triage with family life, Hindu practice, and two opinionated kids. I spend pragmatically on quality and convenience, stay skeptical of hype, and take health more seriously after cancer, even...
Apryl Ellison
I’m a 35-year-old computer systems analyst in rural Missouri, balancing full-time tech work, two kids, and a mortgage with a practical, systems-minded approach. I value reliability, clear information, and choices that save time, reduce hassle, and support l...
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Summary
Themes
| Theme | Count | Example Participant | Example Quote |
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Outliers
| Agent | Snippet | Reason |
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Overview
Key Segments
| Segment | Attributes | Insight | Supporting Agents |
|---|---|---|---|
| Senior technology managers (50–57, ops/IT leadership) |
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These leaders prioritize top‑down consolidation into incumbent suites that offer bundled AI features, require financial offsets for pilots, and drive procurement clauses (no‑default training, logging, model/version transparency). They act as the gatekeepers who translate CFO/board risk posture into vendor selection. | James Cheng, Raymond Navarro, Nicholas Ausbie |
| Mid‑career technical practitioners (35–41, hands‑on engineers/analysts) |
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These practitioners push rapid, measurable pilots and prefer build‑lite (internal LLM orchestration, RAG layers, SQL copilots, triage automation) when TCO is demonstrably lower and iteration cycles are short; they drive switching behavior at the workflow level even when enterprise procurement resists wholesale vendor swaps. | Matthew Hughes, Miles Murad, Ryan Maciel |
| Regulated engineering / quality leaders |
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Demand is dominated by validation, traceability and provenance requirements; these buyers prefer augmentation inside validated systems and will impose extended validation paths that slow adoption and bias toward vendor features that can prove auditability. | Jeremy Miller |
| Public‑sector / K–12 education operators (mid‑career) |
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AI adoption is highly constrained: investments remain SaaS‑centric where privacy and offline resilience are assured; public procurement and grant dynamics (ESSER/BOCES) drive conservative, evidence‑first adoption and block reliance on public LLMs for PII. | Abigail Mcclung |
| Higher‑comp technical managers (>$200k, FinOps-savvy) |
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These managers provide detailed cost guardrails (percent offsets, pilot windows, tagging/true‑ups) and are primary drivers of FinOps controls around API/inference spend; they accelerate pilots only when cost neutrality or clear ROI is demonstrated. | Aaron Monahan, Ryan Maciel |
Shared Mindsets
| Trait | Signal | Agents |
|---|---|---|
| Budget discipline and reallocation | Budgets are largely flat; AI investments must be funded by reallocating from existing SaaS (particularly long‑tail per‑seat tools) or be time‑boxed and self‑funding via short pilots with explicit KPIs. | James Cheng, Raymond Navarro, Ryan Maciel, Miles Murad, Matthew Hughes, Apryl Ellison, Nicholas Ausbie, Aaron Monahan, Jeremy Miller, Abigail Mcclung |
| Vulnerable SaaS categories | Per‑seat, adjunct point tools (meeting transcription, lightweight writing assistants, simple BI viewers, schedulers, niche PM plugins) are most at risk of seat compression or consolidation into larger suites with embedded AI. | Ryan Maciel, James Cheng, Raymond Navarro, Miles Murad, Apryl Ellison, Matthew Hughes |
| Sticky systems of record | CRM, ERP, HRIS, QMS/ALM, SIS and other compliance‑heavy platforms are being augmented rather than replaced; these categories remain high‑friction for net‑new AI vendor substitution. | Jeremy Miller, James Cheng, Matthew Hughes, Nicholas Ausbie, Raymond Navarro |
| Procurement & FinOps tightening | Renewals and new purchases now commonly require AI‑specific gates: 60–90 day pilots, no‑train/no‑reuse guarantees, prompt/output logging, model/version transparency and caps or tagging to control inference costs. | Nicholas Ausbie, Ryan Maciel, Jeremy Miller, Aaron Monahan, Matthew Hughes |
| Build‑lite vs buy heuristic | Teams choose build‑lite when the problem is narrow, low‑risk and short‑payback (weeks->months) and when steady‑state maintenance is small; buy when scale, SLA/validation or broad horizontal coverage are required. | Raymond Navarro, Aaron Monahan, Matthew Hughes, Miles Murad, James Cheng |
| AI as productivity, not open‑ended spend | AI is framed as a productivity lever that must demonstrate measurable outcomes and quick payback; CFO/board narratives emphasize offsets, measurable seat‑reductions or efficiency metrics rather than speculative transformation bets. | Ryan Maciel, Miles Murad, Apryl Ellison, James Cheng, Aaron Monahan |
Divergences
| Segment | Contrast | Agents |
|---|---|---|
| Senior managers (consolidation-first) vs mid‑career practitioners (build‑lite) | Senior managers prefer suite-native bundled AI and top‑down consolidation with contractual protections; mid‑career practitioners more often favor assembling small internal solutions for narrow workflows and drive tactical switching even if enterprise procurement is cautious. | James Cheng, Raymond Navarro, Nicholas Ausbie, Matthew Hughes, Miles Murad, Ryan Maciel |
| Regulated engineering vs general enterprise | Regulated engineering imposes heavier validation, provenance and audit requirements that materially slow vendor swaps and prefer augmentation inside validated systems-contrasting with non‑regulated teams that accept faster pilots and vendor change. | Jeremy Miller, Matthew Hughes, Raymond Navarro |
| Education/public sector vs commercial tech | Education operators remain highly conservative-retaining high SaaS share and deferring AI adoption due to privacy and offline reliability-while commercial tech teams are reallocating SaaS spend into AI pilots and internal tooling. | Abigail Mcclung, Matthew Hughes, Miles Murad, Raymond Navarro |
| Vendor‑driven reallocation vs finance/policy‑driven reallocation | Some respondents emphasize vendor pricing/bundling as the primary reallocation driver (vendors forcing consolidation), whereas others emphasize CFO/FinOps mandates and governance as the dominant force shaping where AI dollars go. | Nicholas Ausbie, Aaron Monahan, James Cheng, Ryan Maciel |
Overview
Quick Wins (next 2–4 weeks)
| # | Action | Why | Owner | Effort | Impact |
|---|---|---|---|---|---|
| 1 | Ship Enterprise AI Governance Addendum + DPA pack | Security/legal are day‑0 gates; a ready pack (no‑train‑by‑default, retention, residency, model pinning) shortens procurement and boosts win rate. | Legal + Security | Low | High |
| 2 | Launch Self‑Funding Pilot Kit (60–90 days) | CFOs require net‑neutral rollouts; provide KPI templates (deflection, time saved), seat-offset plan, and sunset criteria to accelerate decisions. | Sales Ops + Customer Success | Low | High |
| 3 | Publish Consolidation ROI Calculators | Deals close when Claude replaces 2–3 tools (notes, writing, KB overlays) and proves savings at renewal. | Product Marketing | Low | High |
| 4 | Add FinOps Controls (caps, alerts, usage dashboards) | LLM spend must be predictable; hard caps + alerts de‑risk pilots and speed InfoSec/Finance approval. | Engineering + FinOps | Med | High |
| 5 | Prioritize Suite-Native Integrations | Buyers prefer in‑tenant solutions; first‑class connectors to M365/Google/ServiceNow/Salesforce/Atlassian reduce data‑silo risk. | Partnerships + Engineering | Med | High |
| 6 | Offer Co‑term, Ramp, and True‑down Pricing | Procurement wants offset‑friendly terms that mirror renewals and allow staged adoption. | Finance + RevOps | Low | Med |
Initiatives (30–90 days)
| # | Initiative | Description | Owner | Timeline | Dependencies |
|---|---|---|---|---|---|
| 1 | Governance & Auditability Pack | Deliver prompt/output logging with redaction, RBAC/ABAC, SIEM export, model version pinning/notifications, BYOK/KMS, region control, and an AI SBOM. Include a standardized AI addendum for contracts. | Product + Security | Q3–Q4 | Cloud provider KMS/regions, SIEM integrations, Legal templates |
| 2 | Build‑Lite Platform SDK (RAG, Triage, Doc Pipelines) | SDK + templates for enterprise RAG, support triage, and doc classify/extract with evaluations, redaction, and permission-aware retrieval across SharePoint/Confluence/Drive/Jira/ServiceNow. | Platform Engineering | Q3 | Partner APIs, Vector store + policy filters, Eval harness |
| 3 | Suite‑First Consolidation Plays | GTM playbooks targeting categories under seat compression (notes, writing, KB overlays, BI viewers). Propose one‑in/one‑out swaps and co‑sell with suite vendors. | Sales + Product Marketing | Q3–Q1 | Marketplace listings, Integration certifications, ROI calculators |
| 4 | Pilot‑to‑Production Motion | Standardize 6‑week pilots: success metrics library, onboarding runbooks, lift dashboards, cost caps, and a flip plan tied to decommissioning target tools. | Customer Success | Q3 | Telemetry pipelines, Enablement content, RevOps approvals |
| 5 | Regulated/Education Package | Validation artifacts (model cards, change logs), citations, human‑in‑loop, private/US‑only options, and DPAs tuned for FERPA/FDA/ISO contexts. | Compliance + Product | Q4–Q1 | Private deployment path, Vertical counsel review, Reference customers |
| 6 | Pricing Architecture Revamp | Pooled consumption + light seats, co‑term with core platforms, usage guardrails, price locks, and offset clauses to retire overlapping tools. | Finance + RevOps | Q3 | Billing/entitlement systems, Legal terms, BI for usage metering |
KPIs to Track
| # | KPI | Definition | Target | Frequency |
|---|---|---|---|---|
| 1 | Pilot Conversion Rate | Percent of pilots converting to paid within 90 days | >= 55% | Monthly |
| 2 | Consolidation‑sourced ARR | Share of new ARR tied to documented retirement of ≥1 point tool | >= 40% of new ARR | Monthly |
| 3 | Time‑to‑Pilot | Median days from NDA/signature to pilot go‑live | <= 14 days | Weekly |
| 4 | Governance First‑Pass Approval | Percent of enterprise security/legal reviews passed on first cycle | >= 80% | Quarterly |
| 5 | ROI Attainment in 60 Days | Percent of new customers hitting agreed KPIs (e.g., 20–25% deflection or 15–30% time saved) within 60 days | >= 60% | Monthly |
| 6 | Partner‑Attached Wins | Percent of closed‑won deals attached to M365/Salesforce/ServiceNow/Atlassian integrations | >= 35% | Quarterly |
Risks & Mitigations
| # | Risk | Mitigation | Owner |
|---|---|---|---|
| 1 | Suite incumbents bundle good‑enough AI and crowd out point solutions | Differentiate on governance depth, cross‑suite orchestration, faster time‑to‑value, and verifiable ROI; co‑sell where possible. | Product + Partnerships |
| 2 | Procurement stalls due to privacy/compliance gaps | Pre‑negotiated DPA/AI addendum, no‑train‑by‑default, model pinning, SIEM export, and vertical references. | Legal + Security |
| 3 | Unpredictable token spend undermines finance approval | Implement caps, alerts, budget policies, model routing optimization, and monthly showbacks. | Engineering + FinOps |
| 4 | Difficulty proving displacement at renewal | Embed decommission plans in pilots, provide swap guides and data‑backed calculators; align co‑term dates. | Sales Ops + CS |
| 5 | Regulated buyers require validation and traceability | Provide validation packs, citations, change‑notification SLAs, human‑in‑loop, and private/US‑only deployment options. | Compliance |
| 6 | Integration fragility across suites blocks productionization | Certify high‑usage connectors, add health checks and retries, and publish SLOs for key integrations. | Platform Engineering |
Timeline
31–90 days: Ship FinOps controls; first suite integrations GA; standardized pilot motion live; 3 consolidation plays launched.
Q4: Build‑lite SDK GA; additional certified integrations; early regulated/education package beta.
Q1: Regulated package GA; pricing architecture live; expand co‑sell motions and partner‑attached pipeline.
Objective and context
We set out to understand how enterprise IT budget owners are reallocating spend across existing SaaS, emerging AI-native tools, and custom builds; which categories are most exposed to AI; how build-vs-buy has shifted; whether AI spend is additive or cannibalising; and the CFO/board posture for 2026. Insights synthesize seven question areas across IT leaders spanning technology operators, regulated industries, and public education.
How allocations have shifted
Budgets are largely flat, but the mix is changing. AI-native has grown from a tiny base into a meaningful line item, typically mid-teens, funded by SaaS consolidation and seat right-sizing rather than net-new dollars. James Cheng reports AI rising from ~2% to ~19%; Raymond Navarro attributes ~60% of the motion to top‑down consolidation and ROI mandates. Custom/internal work remains material but has pivoted from greenfield apps to integrations, data plumbing, governance, access controls, and lightweight automations. Outliers include education (Abigail Mcclung: SaaS ~93%, AI ~3%) and one org keeping custom spend high but focused on governance.
Build vs buy in an AI era
AI has shifted “buy by default” to pragmatic “build‑lite” for narrow, internal, low‑risk workflows (document intake/extraction, support triage, RAG/knowledge search, analytics helpers). Speed‑to‑value and per‑seat vs usage economics tip decisions toward assembling internal tools; productionization, SLAs/SSO/auditing, and regulatory risk keep core systems with vendors. Raymond Navarro quantifies a posture shift from ~90/10 to ~80/20 (buy/build‑lite). Aaron Monahan demonstrates the upside: a 4‑week internal build replaced a vendor SKU with ~60% unit‑cost reduction at 90–95% accuracy and a small human review lane.
Where AI disrupts (and where it does not)
Teams are pruning edge, single‑purpose SaaS where suite‑native or AI‑native reaches “good enough.” Most frequent cuts: meeting transcription/notes (Ryan Maciel moved to Zoom/Meet AI), writing/grammar (Apryl Ellison cancelled most Grammarly Business as Microsoft Editor + Copilot sufficed), lightweight BI viewers, simple forms/surveys, async video/screencast, and small KB/chat add‑ons. Core systems (ERP, CRM, HRIS, ITSM, QMS/ALM) remain durable due to auditability, change friction, and identity/governance constraints (Ryan Maciel; Jeremy Miller). Education and medical devices are especially conservative (FERPA, FDA/ISO).
Procurement and budget posture
AI is a gating factor in renewals, not a checkbox. Standard practice: 60–90 day, metrics‑driven pilots on first‑party data; consolidation‑first at the edges; and elevated requirements for data‑use guarantees (no default training, retention/residency, BYOK), model governance (version pinning, provenance, change notifications), auditability (prompt/output logs, RBAC, export), and cost predictability (caps, token metering). CFOs treat AI as a conditional investment: net‑neutral unless measurable productivity, ticket deflection, or revenue lift is proven within a quarter or two (Ryan Maciel; Miles Murad). Security/identity/governance are protected spend; LLM usage is FinOps‑managed with tags and caps (Aaron Monahan). Partial fresh allocations appear only in risk/security under board pressure (Nicholas Ausbie).
Persona correlations
- ROI‑first consolidators (Ops/GM: Cheng, Navarro): Drive suite‑native AI, seat reclamation, and one‑in/one‑out funding.
- Platform technologists (Hughes, Maciel, Ellison): Lead build‑lite pilots (RAG, triage, doc intake) under strict governance.
- Regulated engineering (Miller): Require validation, traceability; AI assists augment but do not replace validated systems.
- Education K‑12 (Mcclung): Extreme constraints (FERPA, offline reliability); SaaS share rises, AI remains bundled and minimal.
- Gov/security‑mature IT (Monahan, Ausbie): Maintain high custom for governance; request ML SBOMs and AI incident playbooks.
Recommendations
- Consolidate Wave 1: Target notes/transcription, writing/grammar, BI viewers, forms, async video. Use renewal windows to cancel/downgrade where suite AI is “good enough.”
- Stand up a pilot factory: 60–90 day templates with KPI gates for L0/L1 support deflection, RAG/enterprise search, and doc intake/extraction.
- Codify build‑lite blueprints: Reference architectures with model pinning, eval harnesses, RBAC/ABAC, and rollback.
- Centralize AI governance: LLM gateway with prompt/output logging, DLP, region/retention controls, and BYOK; stream to SIEM.
- Negotiation playbook: Co‑term, consolidation credits, downgrade rights, transparent metering, and export guarantees to neutralize AI upcharges.
Next steps and measurement
- Run a 14‑day seat/SKU audit and publish a renewal triage sheet (consolidation test, displacement test, AI‑upcharge sanity).
- Enable suite‑native AI in the office/conferencing stack; schedule decommissions for overlapping tools.
- Launch the pilot factory with baseline KPIs and a hard kill switch; cap and tag all LLM spend.
- Deploy the LLM gateway and AI DPA addendum before scaling any pilots.
- KPIs: Vendor count reduction in edge categories (target 15–25% in 2 quarters); AI wallet share of software mix (15–20% in 12 months, net‑neutral); pilot pass rate (≥60% meeting KPIs in 90 days); seat/tier savings (8–12% run‑rate reduction in 2 quarters); governance coverage (≥90% of AI flows behind gateway/logging).
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For SaaS contracts coming up for renewal in the next 12 months, indicate the expected distribution of outcomes (expand, maintain, reduce, terminate). Also, what percent of your total SaaS spend does this renewal cohort represent?matrix Quantifies near-term churn/expansion to model vendor revenue and wallet migration.
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Across the next 12 months, how do you expect to allocate spend for each domain across solution types? Allocate 100% per row. Domains: productivity/collaboration; analytics/BI; customer support; automation/RPA. Solution types: suite-native AI add-ons; standalone AI tools; internal/custom; traditional non-AI SaaS.matrix Reveals wallet share shifts among suite add-ons, standalone AI, custom, and traditional SaaS by domain.
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What economic thresholds must an AI tool meet to scale beyond pilot? Provide: required payback period (months); minimum productivity or cost reduction (%); maximum acceptable copilot seat price ($/user/month); maximum acceptable inference cost ($ per 1K tokens) for internal workloads.numeric Sets pricing and ROI guardrails vendors must hit to win expansion.
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By category, what percent of seats/licenses did you reduce in the past 12 months, and what additional percent do you expect to reduce in the next 12 months? Categories: meeting transcription/notes; writing/grammar; lightweight BI viewers/NLQ; async video/screencast; small surveys/forms.matrix Measures realized and expected seat compression in vulnerable categories.
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For each data class, what is your current policy for using third-party AI services? Select one per row. Data classes: public; internal non-sensitive; internal confidential; PII/HR; regulated (HIPAA/PCI/etc.); source code/IP. Options: prohibited; allowed with controls; allowed broadly; internal-only.matrix Defines the addressable scope for external AI by data sensitivity.
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When considering replacing or consolidating a software tool, which factors most constrain switching today? Please complete a MaxDiff on: data migration effort; integration complexity; user retraining/time-to-change; compliance/validation requirements; contract lock-in; performance/quality risk; vendor viability; loss of key features.maxdiff Prioritizes product and CS investments that unblock switching and drive adoption.
Research group: 10 US enterprise IT decision-makers (CIO/IT/engineering and systems managers), including regulated (FDA/ISO) and K–12 public-education voices to capture conservative outliers. Budgets are largely flat; AI grew from low-single digits to mid-teens share, funded mainly by SaaS consolidation and seat right-sizing, while “custom” shifted to integrations, data plumbing, governance and lightweight automation. Core systems of record remain; cuts hit meeting transcription/notes, writing/grammar, small surveys/forms, async video, and BI viewer/NLQ overlays, with build‑lite LLM layers (RAG/search, support triage, document intake) expanding where speed-to-value, per-seat vs usage economics, and governance favor in‑house. AI spend is mostly cannibalized from existing lines under 60–90 day, KPI‑gated pilots; procurement now requires no training on customer data, retention/residency controls, SSO/SCIM, prompt/output logs, model/version transparency, and hard spend caps. Board/CFO narrative is “hold flat and reallocate”: prefer suite‑native AI, consolidate vendors, and fund only outcome‑backed deployments (e.g., 10–30% productivity or 20–25% ticket deflection) with offsets. Takeaways: Operators should run immediate seat/SKU audits in vulnerable categories, enable suite AI, enforce KPI‑gated pilots, and sunset overlaps at renewal; vendors win by embedding governed AI that replaces 2–3 point tools with defensible unit economics and clean data terms; investors should track leading indicators of wallet migration-AI share of software approaching ~15–20%, compression in notes/grammar/BI‑viewer seats, pilot pass rates, vendor‑count decline, and renewal‑driven switches fueled by bundling/pricing.
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