Should We Call it AI? : Investigating Brand Building in AI-Native Startups

Sammanfattning

This thesis investigates how AI-native startups—whose value proposition is dependent on AI—build their brands by framing their dependence on artificial intelligence when communicating with external stakeholders. Drawing on brand-building theory and framing theory, we conducted a qualitative multi-case study of seven early-stage AI- native ventures and two domain experts. Cases were selected purposively and through snowball sampling to capture sector diversity. Primary data comprised nine semi‑structured interviews, supplemented by material from websites and social‑media channels. Inductive thematic analysis produced 59 first‑order codes, nine second‑order themes and three aggregate dimensions that illuminate the interplay between branding activity and framing mechanisms.First, Establishing AI-Centric Value shows how startups achieve strategic differentiation through niche positioning, cultivate an AI-centric identity, and emphasize functional-efficiency benefits—thereby loading their brands with distinctive,solution-oriented associations. Second, Fostering Trust & Credibility reveals that founders practice selective transparency, communicate risks reactively, and publish authority-building content to offset black-box skepticism and bolster perceived reliability. Third, Cultivating Stakeholder Relations & Resonance demonstrates how ventures tailor messages to diverse audiences, combine digital and in-person channels, and embed themselves in collaborative ecosystems to sustain visibility and legitimacy. Across these dimensions, the mechanisms of distinctiveness, coherence, and resonance translate complex AI technology into stakeholder-relevant meaning, directly reinforcing two of Aaker’s brand equity drivers—brand awareness and brand associations. The study contributes to branding and framing literature by showing that effective AI branding relies less on technical exposition than on outcome-focused narratives, measured transparency, and human-centered engagement. Managerially, the findings highlight the importance of focusing on outcomes (not technology), “white-box readiness”, expert-led facilitation of adoption of AI, and strategic partnerships. Limitations include the small sample size, the rapidly evolving context, and dependence on self-reporting;future research should track framing dynamics longitudinally, compare AI-native with AI-augmented and non-high-tech ventures, and cross-cultural studies of AI-native startups. Keywords: AI-native startups, brand building, framing mechanisms, brand equity