Generative artificial intelligence (AI) is being explored for its potential applications throughout the sourcing and procurement sector. Potential uses for the technology range from improved compliance to threat modelling and supplier relationship management.
However, the most impactful application of generative AI—not to mention the one with a good deal of potential to be applied now, not in some indeterminate amount of time when the technology matures—could be to the request for proposal (RFP) process.
What is an RFP?
An RFP is a formal document than procurement teams issue to potential vendors. The issuer details the product or service they are looking to acquire and vendors place bids in order to secure a contract.
An RFP takes the form of a questionnaire-style form requiring potential vendors to enter data about the product or service they can provide. This allows procurement teams to more effectively gather and analyse data from multiple potential vendors in order to make an informed decision.
RFP pain points
In both the public and private procurement sector, RFPs are a central element of the procurement process. As such, the manual RFP creation process consumes significant time and resources for procurement departments.
Delays can derail sourcing cycles and disrupt supply chains. As with any repetitive manual process, RFP writing is also an error-prone process. The consequences can range from an improperly sourced service to a dangerous and expensive breach in compliance.
Also, the quality of the RFP can affect the quality of vendors who respond to it. As a data gathering tool, a poorly constructed RFP will also produce poor quality data, which can lead to hiring a poor quality vendor.
Generative AI and the RFP process
The ability for generative AI to rapidly analyse and synthesise information could not only automate and standardise the RFP creation process, but qualitatively improve the design of the RFPs themselves.
According to McKinsey, generative AI can serve as an invaluable tool when prioritising categories and suppliers based on market development, spend analysis, and supplier leverage. “This analysis prioritises spending with the highest potential to drive value for the organisation, while deprioritizing categories or suppliers where value will be more challenging to obtain,” their analysts note.
The client team behind the report developed this generative AI powered “RFP engine” to use anonymised and sanitised RFP templates and cost drivers from “more than 10,000 RFPs and their responses” in order to identify and replicate the “best of best” analyses in a fraction of the time. “It also learned what drove winning bids and redesigned future RFPs for optimal bid structure and cost granularity. Finally, it predicted, and prevented, omissions and mistakes in the bids,” note Aasheesh Mittal and Jennifer Spaulding Schmidt, McKinsey analysts.
By intaking vast amounts of data in the form of successful and unsuccessful RFPs, generative AI could potentially allow procurement teams to both automate and enrich their RFP generation processes.
