The rapid evolution of generative AI has created both excitement and uncertainty for brand leaders. Research from related scientific areas points towards GenAI’s potential to revolutionise brand strategy. For example, it could boost process efficiency or streamline complex tasks. The problem is that empirical guidance for its integration has been absent — until now.

A 2025 study by Accenture and the Stuttgart Media University finally bridges this gap in research. It offers a scientific framework to harness GenAI’s strengths for strategic brand management while mitigating risks. This article distills the key insights from the research, explains the most valuable insights and explores the implications for brands. Crucially, it provides actionable strategies for modern brand leaders.

The Research Process: Bridging Theory and Practice

To address the lack of scientific guidance, the study applied the iterative Design Science Research Method, combining theoretical rigour with real-world testing. The research goal was to create a standardized guideline that would help brand leaders integrate GenAI into strategic brand management, and to validate said guideline through a case study.

Over the course of six months, the lead author of the study, Roman Kugler, analysed 180+ publications across brand theory, AI research, and human-AI collaboration. He evaluated over 50 GenAI tools, and engineered 46 specialised prompts. This led to a draft guideline, which was then applied in a real-world case study involving the repositioning of a fast-moving consumer goods brand to create a GenAI-based brand strategy. Finally, the resulting brand strategy was evaluated through an expert review and a conceptual analysis, and the guideline was adapted accordingly.

The GenAI Brand Strategy Guideline: Structure and Outcomes

The resulting guideline structures GenAI usage into seven theory-proven core phases of strategic brand management. These are brand analysis, goals, identity, positioning, architecture, evolution, and budgeting.

It emphasises collaboration between humans and GenAI, with multiple Large Language Models handling different logical, conceptual, and data-driven tasks. The actions of these models are fed in, considering recent best cases from prompt engineering, and enhanced with brand theory and aspects. A human component steers creativity, monitors quality and makes adaptations, if necessary.

The practical application of the guideline within a case study shed light on several key insights:

• Output Quality: Within an expert assessment, the overall quality of the AI-generated strategy was satisfactory. It achieved high marks for brand relevance, brand authenticity and brand continuity.
• Human Comparability: Results were generally comparable to those produced by human-only efforts.
• Increased Work Speed: GenAI reduced time spent on 22 out of 24 subtasks within the seven core phases to roughly 8 hours in total. That’s significantly less than the human-only process.
• Efficiency Gains: Support and streamlining effects from GenAI application minimized needed resources like size of human workforce, financial costs, number of stakeholders involved, and time needed.

GenAI’s Strengths and Limitations in Practice

Notably, the study also revealed clear patterns in which GenAI excelled and human oversight remained crucial. Through the practical application of the guideline, GenAI proved to be especially impactful in the phases of defining brand goals, architecture, evolution and budgeting. It showed considerable suitability to support the development of the brand identity and positioning. Generative AI displayed advantages for these strategic brand management tasks by effectively supporting logical and ideation tasks, rapidly structuring complex information like market segmentation or competing brand positionings. Furthermore, the technology showed strengths in enhancing the process’s speed and scalability. It did this by quickly producing multiple brand architecture models or budget scenarios and significantly enhancing the information basis for strategic decision-making.

However, GenAI also displayed a number of relevant weaknesses. Most notably, originality and creative excellence fell behind human quality levels in the expert review. Moreover, the risk of AI-hallucinations remained a problem, producing inaccurate or misleading information. Additionally, a high dependency on data quality and availability made the systems used unsuitable for deducing a strategically sound, holistic brand analysis over all concerning aspects. The discovered limitations and risks underlined the need for careful human review and a structural approach when utilizing GenAI in the strategic brand process.

Strategic Implications for Brand Leaders

By putting the research into context, brand leaders can deduce relevant learnings for their role and approach regarding GenAI. First and foremost, full automation of the brand strategy process remains out of reach, and might not even be realistic in the foreseeable future. Human judgement remains essential to provide strategic direction, creative excellence, as well as emotional and cultural nuances and contexts. Quality control and monitoring further prove to be an important task for the human component to mitigate AI hallucinations, ethical challenges and technical limitations. However, GenAI has significant potential to enhance and support human work along nearly all major process steps of strategic brand management.

The research underscores that brand strategists and leaders alike can follow best practices to avoid risking brand integrity:

1. Adopt a Systematic Approach
   A systematic, theory-led, practically tested approach grounded in empirical guidance is needed to integrate AI meaningfully into brand strategy. Using a standardized, repeatable process enables continuous iteration, which is much needed in the hyperdynamic tech environment.
2. Prioritize Human-AI Collaboration
   Balance AI’s analytical power with human judgment and create collaborative workflows that utilize individual strengths and minimize risks. Implementing recent findings from prompt engineering and using multiple GenAI tools for one task further increases collaboration effectiveness.
3. Invest in Training and Iteration
   Build team proficiency in relevant technological competencies while establishing close connections to organizational data teams. Continuous evaluation and adaptation of GenAI systems and integration cases are crucial as technology evolves.
4. Mitigate Risks Proactively
   Implement checks for AI bias and hallucinations while prioritizing ethical, data privacy and transparency considerations.

Conclusion: Balancing the GenAI Innovation with Human Ingenuity

GenAI is proving not to be a replacement for human brand strategists, but a catalyst for data-driven, resource-efficient brand strategy. As the empirical research illustrates, GenAI can enhance brand strategic decision-making when applied systematically and with thorough human oversight. But it can also streamline processes, and drive significant resource efficiencies. And it can do this without compromising the quality or distinctiveness of brand outputs. For brand leaders, the path forward lies in adopting a balanced approach. It leverages AI’s computational power for logic and deduction. Yet, this approach also preserves the creative excellence and irreplaceable insights that only human judgment can provide. This dual strategy not only mitigates the risks associated with AI but also unlocks a potent competitive advantage in a rapidly digitalizing market.

As we navigate this frontier, forward-thinking organizations will view GenAI not as a replacement for human creativity and strategic insight. Instead, they will view it as a transformative tool. When integrated with care and expertise, it will propel brands into a new era of innovation and customer-centric excellence. And, happily, it will help you gain market traction in the process.

---

Image by Hongwei FAN / Unsplash.