Research & TechnicalPublications

Abstract

This paper examines the critical distinction between AI-driven automation and human strategic judgment in marketing contexts. While generative AI excels at pattern replication and high-volume content generation, it systematically fails at the meta-cognitive tasks required for brand positioning and competitive differentiation. The phenomenon of Strategic Dilution occurs when organizations over-rely on AI outputs, leading to homogenized messaging that converges toward category-average language. The paper proposes a framework for identifying decision boundaries where human judgment remains irreplaceable, particularly in contexts involving organizational memory, stakeholder politics, and long-term brand equity. This paper precedes and conceptually informs Generative AI and Organizational Sensemaking (AIMRI-WP-2026-01), which formalizes several constructs introduced here.

Key Findings

• →AI-only strategy workflows tend to converge toward sector-average positioning within 6–10 generation cycles.
• →In internal benchmarking, shifts from human-led to AI-only strategic drafting corresponded to roughly 20–30% declines in brand differentiation and recall metrics.
• →Human–AI collaborative models, guided by a Growth Architect, outperformed pure automation by approximately 2.5–3.5× on strategic clarity indices.

Abstract

This paper examines the paradox of Narrative Entropy in AI-augmented enterprises: as generative systems accelerate content production, collective understanding often declines. Individually, AI-generated assets look polished; collectively, they erode narrative ownership, fragment brand storytelling, and reduce alignment on what the organization stands for. Drawing on internal pilots and behavioral analysis, the paper shows how over-reliance on AI for strategic content leads to systemic fragmentation—and how Provenance Architecture can restore narrative coherence without abandoning AI scale.

Canonical source: https://kristinashrider.com/research/aimri-wp-2026-01

Key Findings

• →Narrative ownership erosion becomes clearly visible when AI-generated or AI-substantially-edited content exceeds roughly 60% of strategic asset production.
• →High-AI-adoption teams show around 40% lower alignment on core brand narrative markers compared with primarily human-led teams.
• →In enterprise pilots, implementing Provenance Architecture restored a substantial portion (approximately 70–80%) of perceived narrative coherence relative to a human-only baseline.

Abstract

This paper identifies structural algorithmic deprioritization patterns where visibility decays by 60–80% once AI content exceeds approximately 60% of output volume. It extends the Marketing Agent Decay Model (MAD-M™) to model multi-channel visibility collapse across social feeds, organic search, and AI-mediated discovery surfaces. Findings indicate deprioritization is a structural feature of platform architecture in 2026, requiring Provenance Architecture to maintain strategic coherence.

Key Findings

• →The 60% Inflection: Visibility decay accelerates sharply once AI-generated content exceeds 60% of total output volume.
• →Reach Penalties: Creators report 60–80% declines in impressions for content flagged as AI-generated compared to human-led baselines.
• →Search Exclusion: Generic AI content is systematically excluded from high-authority citation contexts like Google AI Overviews and LLM responses.
• →Governance Advantage: Implementation of Provenance Architecture (originality markers and reasoning traces) reduces detection probability and maintains reach.