Independent Field Research on AI Workflow Adoption
Meridian Research Institute conducts systematic field research documenting how mid-market organizations are actually adopting, integrating, and governing AI within operational workflows. The institute produces empirically grounded benchmarks through rigorous mixed-methods research designed to reduce epistemic risk for senior decision-makers.
Current Study
A triangulated research program examining AI tool adoption, workflow integration, documentation practices, and governance patterns in marketing and creative agencies with 10–200 employees. The study applies Convergent Parallel Mixed-Methods design, combining executive interviews with staff-level validation to distinguish reported strategy from operational reality.
Research Agenda
Existing data on AI workflow adoption suffers from systematic biases: vendor-sponsored research inflates adoption rates, executive self-reporting obscures implementation gaps, and case studies select for visible success. Meridian Research Institute addresses this gap by collecting primary field data from operational environments, triangulating leadership claims against staff-level observation, and publishing aggregate findings without commercial incentive.
The institute does not provide consulting services, implementation support, or optimization advice. Research outputs document patterns, establish benchmarks, and surface structural dynamics that are otherwise obscured by anecdotal reporting.
Methodological Foundation
All research is conducted under a Convergent Parallel Mixed-Methods framework. Quantitative and qualitative data streams are collected independently, analyzed separately, and integrated only after internal validation. Findings are recorded only when independent data sources converge. Contradictions are documented explicitly rather than resolved for narrative convenience.
Participant data is anonymized at collection. No individual organization or staff member is identifiable in published outputs. Aggregation thresholds prevent reverse attribution.