Proposal Teams need A.I. (for more than generating content)
Generative AI is all the rage. Add source documents to your platform, point the computer at your intranet, and feed it the original proposal request and “Presto, chango!” - you’ve got potential responses for your SMEs to review and bless.
Don’t get me wrong, this is fantastic! No one hates a blank page more than an RFP writer. Getting started is half the battle. Rough draft content to shape and mold is a dream. And, as more development is done by more proposal software vendors, AI capabilities will get closer to the mark and do some pretty heavy lifting for RFP teams.
Now, I could go down the “AI can’t replace human writers and project managers” road here. But it seems that much of the proposal community has moved past the fear that technology will replace people. So instead, I’ll cut to the chase.
Proposal teams need more than content generation. RFP teams are desperate for impactful metrics. Numbers that reflect the project work that a high-quality, data-intensive proposal demands. I think AI can help meet this need.
Most RFP managers that I speak to are looking to quantify an organization’s cost to complete a proposal/RFI/DDQ on a per project basis. The cost of a proposal is time. Quantifying the level of effort (LOE), a complete picture of the time spent on a project, is the key. Whose time? RFP team members, of course. But the real organizational costs and gains aren’t found there.
Let’s look at an asset management mandate for a product that’s just gained it’s seven-year track record. It is performing well against competitors and is a focus project for the back -half of 2024. Marketing materials are just launching and the distribution team is pumped.
RFP comes in the door - who’s tapped to contribute to the proposal?
• RFP Manager
• RFP Senior Writer
• Portfolio Specialist/Client Portfolio Manager
• Portfolio Manager
• Product Manager
• Asset Class Leadership
• Data Tteam Llead
• Sales Manager
• Sales Leadership
• Compliance
• Legal
Now this assumes that there is no existing RFP content and that the organization has hands-on leadership that is involved in the proposal process. Having PMs, CPMs, managers and leadership on the team is where the “billable hours” really add up.
Some vendors track user activity in their platform, primarily RFP writers in their system. Why not track on a per project basis? Why not track all user activity related to a project - assigned writers, reviewers and other contributors? If there is a way to calculate hours spent by the whole team in the system on a project basis, then it only takes a couple of salary assumptions and simple math to come up with an organizational cost ballpark.
But why limit the possibilities to a project effort report? If we task AI with mining system data, we should also be able to get to this information - an RFP manager wish list:
• Knowledge/Content Library responses vs. net new content
• Contributor assignment deadlines met vs. unmet
• Material vs. Editorial editorial cChanges
• Content updates done in proposal review vs. done in the Llibrary
• Proposal compliance to requirements or questions asked
• Regulatory compliance issues flagged and addressed
• Content changes made by non-content owners
• Content changes approved/rejected by content owners
• Whether or not content is noticeably repeated within the document
• Analysis of evaluation or client goals alignment with questions asked or responses given
• Content change drivers - corrected mistakes, updates, extraneous information deleted, additional information to fully answer requirement, etc.c…
I think the proposal process needs some self-examination. It’s an ecosystem that reflects how an organization is performing. So let’s get AI to take a look under the hood and see what is working and not working. Should we bid on this business? Are we draining resources on recurring requests that aren’t covering the costs in client fees? Are our business partners helping or harming the proposal process? How can we improve?
It may not be as shiny as the content creation magic. But, I think AI can provide us with data and analysis that usefully interprets the controlled chaos that is the proposal process.
