Agentic AI & Enterprise Tech Brand Positioning & CXO Adoption Survey
CXOs and enterprise technology decision-makers evaluate, compare, and choose between agentic AI vendors on capability fit, integration readiness, and organizational risk, so you can sharpen positioning, fix conversion gaps, and benchmark adoption triggers by segment.
CONTEXT & RELEVANCE
Why run this survey now
Most enterprise tech vendors don't lose CXO consideration purely on product capability. They lose it due to unclear agentic AI positioning, misaligned value narratives, low C-suite trust signals, poor segment-level differentiation, and premature adoption messaging, none of which fully show up in win/loss reports or pipeline analytics.
If you are...
- Enterprise AI platform vendor
- CXO-facing SaaS or cloud provider
- Product or platform strategy lead
- Revenue or GTM commercialization head
- Corporate or portfolio strategy director
You're likely facing...
- Agentic AI: hype vs. credibility gap
- CXO adoption stall: pilot to scale
- Brand fit confusion: AI vs. automation
- Segment mismatch: IT vs. business buyer
- Vendor shortlist drop-off: late stage
This will help answer...
- CXO brand preference drivers
- Adoption stall stage and trigger
- Segment-level positioning gaps
- Pricing and value perception tension
- Vendor switch and renewal signals
RESEARCH THEMES
What This Survey Investigates
Eight interconnected research themes that map the complete CXO adoption journey from initial vendor evaluation to enterprise-wide agentic AI deployment.
Awareness & Discovery
- Primary vendor discovery channels
- Agentic AI term recognition rates
Vendor Perception
- Brand recall across CXO segments
- Perceived capability vs. actual deployment
Adoption Triggers
- Board-level mandates vs. bottom-up pilots
- Budget cycle alignment for AI spend
Evaluation Friction
- Proof-of-concept drop-off points
- Procurement and security review delays
Pricing & WTP
- Preferred commercial models by CXO tier
- Willingness-to-pay thresholds per use case
Deployment & Stickiness
- Time-to-value across enterprise functions
- Expansion triggers post-initial deployment
Trust & Governance
- AI governance frameworks in active use
- CXO accountability for agentic AI risk
Competitive Positioning
- Vendor switching intent by contract stage
- Differentiation gaps in current AI stack
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 Agentic AI & Enterprise Tech Brand Positioning & CXO Adoption Survey, we recommend a quant-first design with flexible data-collection modes to balance reach, depth, and verification.
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.
Select Sample Size
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 AI adoption and tech positioning space.
AI vendor messaging fit & CXO perception gaps (India)
OBJECTIVE
WHAT WE DID
DELIVERED
AI vendor messaging fit & CXO perception gaps (India)
Agentic workflow adoption barriers & prioritisation framework (Southeast Asia)
AI vendor messaging fit & CXO perception gaps (India)
OBJECTIVE
A mid-size enterprise software vendor needed to identify how CTO and CISO personas across BFSI and manufacturing segments evaluated AI vendor claims, and which message territories drove shortlisting versus skepticism.
WHAT WE DID
Ran a structured quant survey across 180 senior technology decision-makers in 6 cities, capturing vendor recall , message credibility scores , proof-point preferences , and the specific claim types that triggered or blocked vendor consideration at each shortlisting stage.
DELIVERED
A message territory map ranked by credibility across buyer personas, a claim friction list identifying the 4 vendor assertions that consistently reduced shortlisting probability, and a segment-level positioning corridor for BFSI versus manufacturing audiences.
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 adopters, active evaluators and pilot-stalled enterprises?
How will you measure CXO vendor preference beyond simple ratings?
Will the survey map the full enterprise AI adoption journey and drop-offs?
Can this survey inform product and pricing strategy?
How will findings improve our enterprise pipeline conversion rate?
Still have questions?
Schedule a discovery call to discuss your specific needs and get a custom quote.