How to Leverage AI for RFP Responses

5 min read

Dialect Team

Dialect Team

The Challenge with RFPs:

Responding to RFPs is a critical step in the sales process. You want to put your best foot forward in the submission, but still want to preserve time to work directly with prospects and customers on understanding their needs and selling on value. But completing RFPs that span hundreds of questions can be extremely time consuming, involving painful searching through past answers and trying to align multiple stakeholders on a response.

Benefits of Using AI for RFP Responses:

Enhanced Efficiency: AI can be a copilot for proposal and RFP response teams to come up with a default answer to many boilerplate questions.

Quality of Answers: AI can help find the right content for a given answer without painful searching.

Tailored Responses: AI will be effective at customizing answers. Say an answer was given for customer X before. AI can help take that same answer, and make the minor modifications needed to customize the answer for a new customer.

Competitive Advantage: Adopting AI in the RFP process enables a team to respond more quickly in a competitive RFP, write more thorough answers, and then bid on more business - ultimately leading to higher win rates and growth.

Techniques for using AI for RFP Responses

The latest improvements of generative AI make it much easier to respond to RFP questions. Large language models like GPT-4 are able to produce high quality information, with a highly configurable tone, quality, and coherence when given the right context about a company or its offering. Here are some tips to consider when using AI For RFP response:

Managing the content library: for RFP answers, Large Language Models only are as good as the content they are able to understand. Through techniques like retrieval augmented generation, one can provide "context" to a Large Language Model on the different features of a product, pricing, SLAs and more. This content can be sourced from varying formats, like help centers, past documentation, and past RFPs.

Reviewing citations: LLM responses should be able to cite their sources. Review the citations and references to existing material when viewing an AI response.

Maintain the company's voice: when using AI, it's important that the outputs still reflect the company's branding and voice. Leverage AI solutions that are able to do this.

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