Editor's note:Shankha Sen is the Chief Financial Officer of Responsive, a company based in Beaverton, Oregon, that uses AI to help businesses manage proposals and bid responses. The views expressed in this article are solely those of the author.

Throughout my decades-long career in finance, I estimate that I have helped various employers respond to over 1,000 Requests for Proposals (RFPs) and similar buyer evaluations. Today, responding to buyer information requests remains a critical part of generating revenue for businesses, with successful bids often accounting for a significant portion of overall company sales.

But experience has taught me that not every seemingly perfect proposal guarantees a long-term win. Mistakes in the proposal process, gaps in institutional knowledge, product flaws, and other factors can quickly turn a "win" into a contract that erodes the seller's profit margins and often leads to a disappointing customer experience.

In my view, addressing the challenge of deal profitability relies in part on robust data governance, advanced AI tools for sales and proposal teams, and rigorous processes. However, proactive collaboration between the CFO and partners like the Chief Revenue Officer (CRO) is irreplaceable in helping win large deals and ensuring their profitability.

Does this deal make sense?

CFOs and finance teams are uniquely positioned to ensure that deal structures are aligned with profitability. Years ago, I was responsible for pricing a massive nine-figure IT infrastructure deal. Our proposal included absorbing a large portion of the client's internal IT staff, but their cost and benefit structures were vastly different from ours. From that perspective, the deal simply didn't make sense.

Ultimately, we made the deal profitable over the customer's lifecycle by pulling other financial levers—but this was only possible through deep analysis and close collaboration with sales, which revealed the core issues and led us to a solution.

This case highlights the critical role of finance in the sales process. Financial leaders need to be deeply involved in the process and deliberately cultivate one of the most important relationships in business: the CFO-CRO partnership. Here are some practical steps to follow:

  • Regularly attend sales forecast meetings and quarterly business reviews, and review the company's top five to ten customer plans at least annually.
  • Communicate frequently with senior sales leadership to ensure they view you and your team as a resource.
  • When engaging with sales, avoid starting with "no" to prevent hindering ongoing collaboration on revenue generation.
  • Advocate for a data-driven approach to profitability analysis.
  • Analyze deals from multiple angles to assess opportunities to enhance profitability over the entire contract lifecycle.

For large enterprises, situations can quickly become complex, especially when engineering and product teams are in one country, sales in another, and delivery in a third. I have been involved in such deals and witnessed regional general managers negotiating with each other. In these scenarios, CFO leadership is crucial.

Deal design in the age of AI

But collaboration alone is not enough. Without data and intelligent tools, even the strongest CFO-CRO alliance can be flying blind. Fortunately, the tools and technologies available to modern CFOs are far more advanced than they were a decade ago.

Years ago when I first started helping respond to RFPs, we had to copy and paste massive spreadsheets with tabs for finance, product, legal terms, office locations, and more. We sent documents globally via email, struggled with version control, and worked to meet tight deadlines with significant potential business at stake. There was simply no efficient way to ensure content was up-to-date or accurate.

With today's tools—from real-time data to AI platforms—we are better equipped than ever to help sales win profitable business. Of course, introducing AI into similar processes always carries risks. The outputs of large language models often seem like a black box, lacking transparency into how conclusions are reached, and occasionally producing severely inaccurate results.

AI can cause significant issues, especially when revenue is involved, so it is essential to establish best practices for risk mitigation when deploying the technology.