What is synthetic research?

Synthetic research uses AI-powered personas that simulate how real buyers, customers, or market segments would respond to your research questions. It produces directional signal — insights to sharpen your hypotheses — before you invest in real primary fieldwork. Gather Synthetic runs AI-moderated interviews across any audience archetype in minutes, not weeks.

How is synthetic research different from real research?

Synthetic research generates directional signal using AI personas, while real primary research collects data from actual people. Synthetic pre-research is faster (minutes vs. weeks), cheaper, and ideal for hypothesis generation and stakeholder alignment. It is not statistically valid and should be used to focus and prioritize real fieldwork, not replace it. Gather offers both synthetic pre-research and real AI-moderated interviews with actual respondents.

What types of research can Gather Synthetic run?

Gather Synthetic supports 14 study types: Brand Perception, Product Feedback, Concept Testing, Competitive Intelligence, CX/NPS Deep Dive, Messaging & Claims Testing, Win/Loss Analysis, Market Segmentation, Pricing Research, Ad & Creative Testing, Customer Journey Mapping, Feature Prioritization, Churn & Retention Analysis, and Audience Discovery.

How much does synthetic research cost?

The first 5 synthetic interviews are free. After that, B2C audience interviews cost $0.50 each and B2B audience interviews cost $5.00 each. There is no subscription required.

How accurate is synthetic research?

Synthetic research is directional, not statistically accurate. Confidence scores reflect internal AI response consistency, not statistical significance. Quantitative projections carry a ±15–20% margin of error. Treat findings as educated hypotheses to validate with real primary research, not as definitive measurements.

← All brands

Creative AI & content operations · PUBLIC RESEARCH BRIEF

AdobeMore assets, or more approved assets?

Adobe describes Firefly Creative Production as reusable creative workflows and Firefly Custom Models as models trained on a brand’s assets. That creates a practical question for marketing teams: does faster generation help if the work still needs extensive revision? This is a proposed research question, not an observed customer problem or a completed study.

Sources checked 2026-09-19 · Simulation results not yet generated

Test a question in Gather ↗Explore fictional demo →

CHANGE ONE THING. LEARN WHAT MATTERS.

Three questions for the GTM team.

For Adobe Firefly, would a demonstration centered on more creative variations or one centered on fewer approval revisions earn a stronger pilot discussion? Hold the brief and final quality bar constant. Ask designers and brand approvers separately, and treat either benefit as something to prove.

Set up this study →

What would a brand team need to see before piloting Firefly Custom Models on its own approved assets? Compare a narrowly scoped campaign with a cross-campaign rollout. Identify the content, evaluation rubric and human review needed without assuming the generated assets meet brand standards.

Set up this study →

For a team evaluating Adobe Firefly, would centralizing generation with creative specialists or letting more marketers create drafts make a better operating model? Treat these as proposed workflows, keep final approval human-led, and examine where training or rework could offset any convenience.

Set up this study →

PROPOSED AUDIENCE

Who should weigh in?

North American brand, creative operations and marketing production leaders at companies with 200 or more employees. Include designers who create assets, brand owners who approve them and operations leaders who manage delivery. Recruit both Adobe users and teams evaluating alternatives. These are proposed research roles, not measured Adobe customer segments.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, test pilot interest with matched demonstrations, then run the same authorized creative brief through the current workflow and a proposed Firefly workflow. Record first-pass approval, revision reasons and time to an approved asset. Generating more drafts is not itself a successful outcome.

Longer term · 3–12 months

Over 3–12 months, investigate whether teams keep using the workflow across new campaigns and changing brand guidance. Follow approver trust, model-maintenance effort and onboarding of new creators. Do not extrapolate a one-campaign trial into a company-wide productivity claim.

What would make the result actionable?

Predefine a brand-quality rubric and use reviewers who do not know which workflow produced each asset where practical. Capture total human review time, rejected drafts, software and setup costs, and completed campaign outputs. Any cost estimate needs these observed inputs; simulated persona preferences cannot establish savings or campaign lift.

A Gather simulation returns hypothetical customer reactions. Quantifying revenue, traffic or retention needs actual business inputs and validation against observed behavior.

Public sources

Adobe Firefly enterprise products and workflows ↗Current product page; checked 2026-09-19