Shankha Sen is the CFO of Responsive, a Beaverton, Oregon-based software company that uses AI to help companies manage their proposals and bid responses. Opinions are those of the author.
Over my decades-long career in finance, I estimate that I have helped my various employers respond to over 1,000 similar RFPs and buyer reviews.
Today, responding to buyers’ requests for information continues to be essential to generating company revenue, with successful bids often representing a significant portion of a company’s overall sales.
But as I’ve learned, not every seemingly great proposition is a winner in the long run. Mistakes during the proposal process, gaps in institutional knowledge, product defects, and other factors can quickly turn a “win” into a margin-eroding commitment for the seller – and often into a disappointing experience for the customer.
In my view, meeting the deal profitability challenge relies in part on robust data governance and advanced AI-driven tools for sales and proposition teams, as well as disciplined processes. But thenothing replaces the Proactive engagement of the CFO with partners such as the Chief Revenue Officer to help close large deals and ensure profitability.
Does the deal make sense?
CFOs and finance teams are uniquely positioned to ensure that deals are structured in a way that makes sense from a profitability perspective.
Years ago, I was responsible for pricing a massive nine-figure IT infrastructure contract. Our offer called for absorbing a large portion of the prospect’s internal IT staff, but their cost and benefit structure was radically different from ours. Seen in this light, the deal simply made no sense.
Ultimately, we were able to use other financial levers to make the deal profitable over the life of the account, but it was only through deeper analysis and close collaboration with sales that we uncovered the core problem and found a path forward.
This scenario highlights the critical role of finance in the sales process. Financial leaders must remain deeply engaged in the process ando work intentionally to build one of the most critical relationships in business: the CFO-CRO partnership. How should this manifest itself in practice? Consider the following steps:
- Participate in sales forecast calls and quarterly business reviews regularly and review the company’s top five to ten account plans at least once a year.
- Interact regularly with key sales leaders to ensure they view you and your team as a resource.
- Never start a conversation with sales with a “no,” as this only discourages continued collaboration on revenue generation.
- Promote a data-driven approach to profitability analysis.
- Analyze deals from multiple perspectives to assess opportunities to increase profitability over the life of the deal.
For large companies, things can quickly become complex, especially when engineering and product teams are based in one country, sales take place in another, and delivery takes place in a third. I have participated in such transactions and seen regional deals general managers negotiate against each other. The leadership of the CFO in situations like these is essential.
Designing deals in the age of AI
But collaboration alone is not enough. Without data and smart tools, even the strongest CFO-CRO alliance risks flying blind.
Fortunately, modern cystic fibrosisOhWe have access to tools and technologies that are light years ahead of what we had just ten years ago.
When I started many years ago I helped answer Calls for tenderswe were copying and pasting between overloaded spreadsheets with tabs on finances, products, legal language, office locations and much more. We emailed documents around the world and fought version control battles while trying to meet tight response deadlines with lots of potential business on the line.There was simply no effective way to ensure the content was up to date or correct.
With today’s tools – from real-time data to AI-powered platforms – we are more equipped than ever to help sales drive profitable business.
Of course, there are always risks whenever AI is introduced into a process like this. The results of large language models often remain a black box, providing little transparency into how conclusions are drawn. They are also known to occasionally produce wildly inaccurate results.
AI can cause significant problems, especially where the income is at stake, which is why it is crucial to implement best risk mitigation practices when deploying technology.
