How Startups Use Generative AI to Supercharge Growth Strategies

Can generative AI fuel explosive startup growth? This study examines how emerging ventures are utilizing large language models to expedite product development, tailor sales, and expand into new markets, while navigating competitive and ethical challenges.

Research: Generative AI for growth hacking: How startups use generative AI in their growth strategies. Image Credit: Collagery / Shutterstock

A new study in the Journal of Business Research explores how startups and scaleups in Europe and the US use generative AI in their go-to-market strategies across three growth approaches: product-led, sales-led, and operational efficiency-driven growth. Through interviews with 20 cases spanning pre-seed to Series E funding stages, we 1) analyze generative AI's role in growth strategies, 2) identify large language model (LLM) use cases for tackling growth challenges such as over-reliance on paid marketing and customer churn, and 3) develop a dual-framework approach, the AI Wheel and AI Capabilities Framework, to guide managers in building, refining, and critically assessing their knowledge of using generative AI for growth hacking.

Key findings include the implications of generative AI for technical and non-technical content creation (e.g., coding assistants, documentation, user stories, and acceptance criteria) in product-led growth, SEO-optimized promotional content, multi-channel content repurposing tools, and hyper-personalized sales outreach using custom-trained AI chatbots in sales-led growth, and AI-powered ideation, market-entry planning, market research, and event-driven customer engagement in operational efficiency-driven growth.

The study introduces the AI Wheel, a visual framework mapping how generative AI supports activities such as content generation, customer activation, and product development throughout a startup's growth journey. The AI Capabilities Framework complements this by outlining the stages through which organizations must build, refine, and reflect on their AI knowledge and practices, including prompt engineering and human-in-the-loop validation, to effectively scale AI adoption.

Importantly, the study also identifies five key growth challenges, including customer churn, product differentiation, and limited fundraising capacity, and maps these to corresponding solutions using generative AI. For instance, LLMs are used to generate investor outreach content, identify ideal customer profiles, and automate personalized retention strategies.

While the benefits are substantial, the paper also highlights a range of organizational, competitive, and societal risks, including bias in training data, opaque model behavior, overdependence on external AI providers, and increased energy consumption resulting from AI usage. Startups are advised to proactively manage these unintended consequences through measures such as ethical oversight committees, fine-tuning AI models with in-house data, and transparent communication strategies.

Overall, the findings empower managers to develop scalable, AI-enhanced growth strategies while navigating the complex implications of deploying generative AI across early-stage innovation environments.