As consulting firms face increasing pressure to deliver proposals faster and more efficiently, the RFP workflow has become a critical yet strained process, especially within high-volume environments like Accenture Song. GenAI, and more specifically LLMs, offer new possibilities to streamline repetitive tasks, improve consistency, and reduce workload. Yet, there remains little empirical insight into how these technologies can be meaningfully embedded in real-world consulting workflows without disrupting human collaboration.

This thesis explores how GenAI might support greater efficiency and employee satisfaction within the RFP workflow of Accenture Song’s D&DP team. Combining literature research with qualitative methods, including interviews, process shadowing, and a collaborative mapping session, the study identifies recurring pain points in areas such as proposal development, communication, and feedback loops. These insights are synthesised into key opportunity areas where GenAI could augment, rather than replace, existing work.

Using the Double Diamond framework, a design process was applied to translate findings into actionable interventions. Multiple concept directions were generated, evaluated, and refined through co-creation with the D&DP team. One hybrid solution was developed into a high-fidelity prototype that supports consultants in structuring proposals, maintaining consistency, and reducing rework.

The final solution is positioned across product, process, and strategic layers, offering a practical path for implementation. It shows that GenAI can be a powerful enabler, if introduced with care. Rather than automating away the human element, this project demonstrates how thoughtful design can turn emerging technology into meaningful support for the people doing the work.