We seek theoretical, empirical, and methodological contributions about validating and integrating AI in marketing analytics. We also encourage industry-focused submissions—including case studies and applied perspectives.  

• The application is free and open to MSI and ARF members and non-members.
• All submissions are due by May 15 and must be submitted via the Call for Content portal.
• Download the extended abstract template here.

1) Validation and Performance Assessment

A central theme of the conference will be methods for validating AI-enabled analytics, including:

• Validating AI-generated estimates and forecasts against traditional methods
• Assessing the quality and representativeness of training data
• Stress testing models under shifting market conditions
• Evaluating AI outputs generated from or trained on synthetic data
• Techniques to audit and pressure-test generative AI or AI-assisted search results
• Detecting hallucinations, inconsistencies, or factual errors in AI-generated outputs
• Aligning validation rigor with decision risk and consequence

We will examine how leading organizations and thought leaders are building evidence to support (or challenge) AI-driven decisions, grounded in empirical research and real implementations.

2) Human + AI: Designing Productive Collaboration

As AI tools become embedded in modeling, forecasting, and decision support, firms must clarify the role of human expertise. Sessions will explore:

• When AI meaningfully augments analyst performance, and when it does not
• Human-in-the-loop approaches to improve accuracy and reduce blind spots
• Designing workflows that preserve institutional knowledge and judgment
• Organizational implications: skill shifts, incentives, and governance

The goal is to move beyond automation toward thoughtful integration.

3) Transparency, Data Context, and Trust

As AI reshapes how data is generated, accessed, and interpreted, new transparency challenges emerge:

• Building organizational trust in AI-enabled analytics across diverse data environments
• Risks and opportunities associated with synthetic data
• Implications of AI-mediated search and discovery for measurement and forecasting
• Implications of advertising-supported AI search for credibility and trust
• Communicating uncertainty and limitations to internal stakeholders
• Transparency challenges in proprietary AI systems

This discussion will prioritize practical experience over theory, focusing on how firms maintain credibility and accountability.

4) Evidence from the Field: Marketing Use Cases

Throughout the program, sessions will be grounded in ongoing empirical work and concrete use cases across marketing domains and verticals. We aim to highlight:

• Where AI has demonstrably improved decision quality or forecast accuracy
• Where results have been mixed or context-dependent
• What differentiates successful implementations from stalled pilots
• Applications beyond sales forecasting, including product development, innovation planning, and market entry decisions

By grounding the conversation in evidence, the conference will help participants calibrate expectations and identify areas requiring further experimentation.

5) Emerging Research in AI and Marketing Analytics

Ongoing research continues to expand understanding of AI-enabled marketing analytics, decision systems, and data-driven marketing more broadly.

• Advances in machine learning, econometric, or computational methods for marketing analytics
• Experimental or observational research on AI-enabled recommendations, search, or content generation
• Consumer or market responses to AI-mediated marketing systems
• Measurement, attribution, and experimentation in AI-enabled marketing environments
• Benchmarking studies, simulations, or methodological comparisons relevant to marketing analytics

This work will help connect emerging research insights with evolving challenges in AI-enabled analytics and forecasting.