Evidence review 010
How does model openness change the governance of marketing AI?
An evidence-led comparison of model openness, transparency and governance responsibilities across deployment arrangements.
18 references · 3 figures · 17 min read
02 Research evidence series
The research library.
Our papers review research on AI in marketing, work and learning. Each one compares study findings, explains the methods and links to the original sources.
30 evidence reviews80 explanatory figures6 research themes
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Evidence review 009
How do shopping agents change consumer choice?
Evidence on shopping-agent selection, recommender concentration, consumer reliance and the limits of commercial inference.
14 references · 1 figure · 16 min read
Evidence review 008
When does AI tutoring support independent learning?
Tutoring experiments and learning-science evidence on scaffolding, feedback, retrieval and independent performance.
17 references · 3 figures · 19 min read
Evidence review 007
Can language-model confidence and self-verification establish factual reliability?
Reviews hallucination measurement, calibrated uncertainty and verification, explaining why repeated agreement cannot establish truth.
14 references · 2 figures · 16 min read
Evidence review 006
What makes a prompt effective, and how robust are the gains?
Examines why prompt gains depend on task, examples and evaluation, with detailed treatment of robustness and instruction density.
16 references · 2 figures · 21 min read
Evidence review 005
What preserves brand recognition when content production scales?
Brand-memory research on identification, salience and distinctive assets, with implications for evaluating scaled content.
13 references · 2 figures · 21 min read
Evidence review 004
How should AI-supported marketing work be evaluated?
A review of benchmark coverage, behavioural testing, factuality and automated judges for evaluating AI-supported marketing work.
22 references · 4 figures · 18 min read
Evidence review 003
When does retrieval grounding improve factual reliability?
Explains retrieval grounding through architectures and benchmarks, separating relevant passages, faithful answers and citation support.
18 references · 3 figures · 19 min read
Evidence review 002
When does algorithmic personalisation improve consumer response?
Studies of personalised advertising and generated messages, separating relevance, trust, response and incremental outcomes.
12 references · 1 figure · 15 min read
Evidence review 001
What establishes incremental marketing impact?
How experiments and modelled counterfactuals identify marketing effects, and why prediction and attribution do not establish incrementality.
12 references · 3 figures · 16 min read
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