Enterprise AI Adoption Decision Study
Map how enterprise technology leaders evaluate, compare, and navigate AI vendor selection, deployment readiness, and internal buy-in, so you can sharpen positioning, convert high-intent accounts, and benchmark adoption barriers by segment.
CONTEXT & RELEVANCE
Why run this survey now
Most enterprise technology leaders don't stall AI adoption purely on budget. They stall due to unclear use-case prioritization, misaligned vendor expectations, fragmented data readiness, unresolved governance gaps, and inconsistent executive sponsorship, none of which fully show up in IT spend reports or project management dashboards.
If you are...
- Enterprise AI product or platform lead
- AI vendor vs in-house build decision
- Chief Digital or Technology Officer
- Revenue or commercial transformation head
- Corporate strategy or innovation director
You're likely facing...
- Use-case prioritization: pilot vs scale
- Build vs buy vs partner tension
- AI = strategic/slow executive perception
- Budget drop-off: proof-of-concept stage
- Governance gaps slowing deployment cycles
This will help answer...
- Adoption drivers beyond cost reduction
- Decision drop-off stage and trigger
- Segment preference: vendor vs in-house
- Pricing model and ROI threshold
- Expansion, renewal, and switching triggers
RESEARCH THEMES
What This Survey Investigates
Eight interconnected research themes that map the complete enterprise AI journey from initial evaluation to scaled deployment.
Discovery & Triggers
- First AI use case identified
- Internal vs. vendor-led initiation
Vendor Selection
- Shortlisting criteria, proof-of-concept weight
- Build vs. buy decision factors
Budget & Approval
- Budget ownership, approval chain length
- CapEx vs. OpEx classification preference
Deployment Friction
- Integration barriers, legacy system conflicts
- Timeline slippage, scope creep patterns
Pricing & ROI
- Pricing model preference, contract structure
- ROI measurement timeline, metrics tracked
Adoption & Stickiness
- End-user adoption rate, training investment
- Feature utilization depth post-deployment
Risk & Governance
- AI policy ownership, ethics review process
- Regulatory compliance gaps, audit readiness
Scale & Roadmap
- Expansion plans, next use case pipeline
- Consolidation vs. multi-vendor strategy
SAMPLING STRATEGY
Tell us about your ideal sample
Help us understand your target respondent profile. Select what applies, we'll design the optimal sample plan based on your inputs.
METHODOLOGY
Survey approach
For the Enterprise AI Adoption Decision Study, we recommend a quant-first design with flexible data-collection modes to balance reach, depth, and verification across enterprise buying committees and IT decision-makers.
EXECUTION PROCESS
How we execute
A proven 9-step process from scoping to delivery, designed to ensure quality, speed, and actionable insights.
Define the decision frame
Confirm objectives, target cohorts, geographies, and reporting cuts
Define the decision frame
Design the instrument
Build workstream modules mapped to outputs (drivers, friction, pricing, retention, trust)
Design the instrument
Lock the questionnaire
Review wording, sequencing, LOI, and competitive context; approve final version
Lock the questionnaire
Pilot and calibrate
Test comprehension and ease quality; refine quotas and remove friction where needed
Pilot and calibrate
Run fieldwork
Execute collection with active quota management and feasibility controls
Run fieldwork
Assure quality
Dedupe, attention checks, speed/consistency rules, removals with audit trail
Assure quality
Prepare the dataset
Clean data and deliver codebook/variable definitions
Prepare the dataset
Analyse and synthesise
Driver ranking, leakage diagnostics, pricing bands, segment insights
Analyse and synthesise
Deliver and align
Executive deck (optional dashboard) and leadership readout with recommendations
Deliver and align
COMMERCIAL TERMS
Request a Commercial Proposal
Pricing depends on cohort, geography, sample size, approach, LOI, and deliverables. Configure below for an indicative estimate.
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Geography
Select Mode of Survey
Length of the Interview
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+ applicable taxes
Cost Breakdown
Note: Estimate is indicative only. Final pricing is subject to scope finalization after discovery call.
REFERENCE CASELETS
Reference
Real-world examples of survey work in the enterprise technology adoption space.
AI tooling preference & procurement friction among mid-market IT buyers (India)
OBJECTIVE
WHAT WE DID
DELIVERED
AI tooling preference & procurement friction among mid-market IT buyers (India)
AI readiness perception & messaging resonance among enterprise C-suite (Southeast Asia)
AI tooling preference & procurement friction among mid-market IT buyers (India)
OBJECTIVE
Map how mid-market IT buyers across BFSI, manufacturing, and logistics shortlist and reject AI tooling vendors, and isolate the specific procurement blockers and internal approval triggers that stall or accelerate purchase decisions.
WHAT WE DID
Ran a structured quant survey across 320 respondents in 6 cities, capturing vendor shortlist criteria, budget authority thresholds, IT-business alignment gaps, and pilot-to-purchase conversion barriers segmented by company size and sector.
DELIVERED
A vendor preference map by segment, a ranked procurement friction list by decision stage, and a set of channel levers tied to the specific moments where shortlist decisions crystallise or collapse.
FREQUENTLY ASKED QUESTIONS
Common Questions
Answers to frequently asked questions about this survey mandate.
What decisions will this survey enable?
Who is the buyer vs who are the respondents?
Can we see differences between early-stage evaluators, active pilot runners and full-scale deployers?
How will you measure AI vendor selection beyond simple ratings?
Will the survey map the full enterprise AI procurement journey and drop-offs?
Can this survey inform product and pricing strategy?
How will findings improve our pipeline conversion and enterprise sales velocity?
Still have questions?
Schedule a discovery call to discuss your specific needs and get a custom quote.