Reimagining the RFP Process with AI

Reducing Inefficiencies, Improving Outcomes

The request for proposal (RFP) process promises an efficient way to identify top-tier partners at competitive prices. But behind the curtain, RFPs conceal a maze of coordination and collaboration challenges resulting in wasted time and resources. Like so many business functions, the RFP process is ripe for an AI-driven transformation.

'Swimming in a Sea of RFPs, Oil' - DALLE 2, 2023

At its core, responding to an RFP requires extensive collaboration between departments – a multifaceted dance of questions, answers, reviews, and revisions. Individual professionals invest upwards of 30 hours per RFP response, dedicating around 25 minutes to thoroughly answering each question. They consult an average of 9 cross-functional subject matter experts across departments, which further adds to process complexity and coordination struggles. Despite the inherent benefits of collaborating on RFPs, 51% of reps cite cross-team coordination as their number one obstacle.

This fragmented process also leads to redundant efforts. Teams continuously retrace their steps, piecing together responses from information scattered across multiple platforms, documents, and systems. Without a centralised knowledge base, nearly half of professionals report difficulty consolidating accurate, timely information. The downstream consequences are delayed responses, incorrect data, strained subject-matter experts, and immense inefficiencies as the same ground is covered again and again.

Many organisations attempt stopgap solutions like creating centralised answer banks in tools like Confluence, Notion or GDrive, or employing extra IT and legal support. While these band-aid fixes can help simplify RFP responses for reps, most companies still struggle to sort new information from old. Subject-matter experts remain overworked and overwhelmed, supporting numerous RFP responses in parallel and distracting from their day job. Overall, process inefficiencies persist.

The time has come to leverage AI's unparalleled ability to analyse massive datasets and surface contextual connections from disparate sources. Properly deployed, LLMs can help organisations analyse incoming RFP questions and link them to the most relevant pre-existing responses from across the knowledge base. This could reduce the RFP drafting process by up to 90% – freeing up experts to focus their time on refining and enhancing responses rather than fishing for information or starting from scratch each time.

More streamlined and efficient RFP processes are especially critical for scaling companies inundated with a barrage of RFPs against tight response deadlines and limited organisational bandwidth. AI-powered platforms promise to fundamentally transform RFP management by reducing redundant efforts, centralising institutional knowledge, and enhancing collaboration. But organisations must take care to thoughtfully integrate these solutions into existing workflows and processes so that the benefits compound over time.

Adoption of AI does come with challenges like upfront software and infrastructure costs, change management, and data privacy considerations. But the potential to turn the RFP process from a cumbersome burden into a streamlined competitive advantage makes it well worth exploring. With the right strategy and solution partners, AI could help lift a significant operational burden from RFP teams’ shoulders – improving win rates, professional growth, and strategic decision making.