Over the course of my decades-long career in finance, I estimate that I’ve supported my various employers in responding to more than 1,000 requests for proposal and similar buyer assessments.  

Today, responding to buyer information requests continues to be vital to enterprise revenue generation, with successful bids often representing a significant share of a company’s overall sales.

But as I’ve learned, not every seemingly great proposal is guaranteed to be a win over the long haul. Mistakes during the proposal process, gaps in institutional knowledge, product shortcomings and other factors can quickly turn a “win” into a margin-eroding engagement for the seller — and often, a disappointing experience for the customer. 

In my view, addressing the challenge of deal profitability relies partly on robust data governance and advanced artificial intelligence-powered tools for sales and proposal teams — as well as disciplined processes. But there’s no substitute for the CFO’s proactive engagement with partners such as the chief revenue officer to help land large deals and ensure their profitability. 

Does the deal make sense?

CFOs and finance teams are uniquely positioned to help ensure deals are structured in a way that makes sense from a profitability standpoint.

Years ago, I was responsible for pricing a massive, nine-figure IT infrastructure deal. Our bid included absorbing many of the prospect’s in-house IT staff but their cost and benefits structure was drastically different from ours. Viewed through that lens, the deal just didn’t make sense.

In the end, we were able to pull other financial levers to make the deal profitable over the account’s lifetime — but it was only through deeper analysis and close collaboration with sales that we uncovered the core issue and found a path forward.

This scenario highlights the critical role of finance in the sales process. Finance leaders need to stay deeply engaged in the process and to work intentionally to build one of the most critical relationships in business: the CFO-CRO partnership. How should this manifest in practice? Consider the following steps:

• Join sales forecast calls and quarterly business reviews regularly, and review the top five to 10 account plans across the company at least once per year.
• Engage regularly with senior sales leaders to ensure they view you and your team as a resource.
• Never start a conversation with sales with a “no,” which only discourages ongoing collaboration on revenue generation.
• Champion a data-driven approach to profitability analysis.
• Analyze deals from multiple angles to assess opportunities to boost profitability over the entire lifetime of the deal.

For large enterprises, things can quickly become complex, especially when engineering and product teams are based in one country, sales occur in another, and delivery happens in a third. I’ve been involved in such deals and seen regional general managers negotiating against each other. CFO leadership in situations like these is critical. 

Crafting deals in the AI age

But collaboration alone isn’t enough. Without data and smart tools, even the strongest CFO-CRO alliance risks flying blind. 

Fortunately, modern CFOs have access to tools and technologies that are light years ahead of what we had just a decade ago. 

When I started out many years ago helping answer RFPs, we were cutting and pasting between bloated spreadsheets with tabs on financials, products, legal language, office locations and more. We emailed documents around the world and fought version control battles while trying to meet tight response deadlines with a lot of potential business at stake.There was simply no efficient way to ensure content was current or correct. 

With today’s tools — from real-time data to AI-powered platforms — we’re more equipped than ever to help sales land profitable business.

Of course, there are always risks whenever AI is introduced into a process like this one. Large language model outputs often remain a black box, offering little transparency into how conclusions are reached. They’re also known for producing wildly inaccurate results occasionally.

AI can lead to significant problems, particularly where revenue is at stake, which is why it’s crucial to have risk mitigation best practices in place while deploying the technology.