Can AI transform the RFP process?
If you’ve ever dealt with an RFP, you’re probably aware that a big chunk, around 80–85%, consists of standardized content that doesn’t change much. The remaining 15–20% is where you have the chance to tailor your proposal to meet the specific needs of the customer. If you’re handling RFPs manually, you may end up spending more time searching for past responses than actually crafting valuable, personalized content.
There is no secret that you can simply use ChatGPT to summarize lengthy documents and extract the key requirements quickly. There are also tools that could help to centralize your RFPs into a library and help with searching through that library. With all this, you can minimize repetitive tasks, but can we now, with Large Language Models, do more?
In this blog post, we’ll explore the ‘Should I respond to the RFP?’ dilemma, a key issue we’ve touched on in previous posts, to illustrate the potential benefits AI can bring to the RFP process. To address this seemingly straightforward question, teams must quickly determine: (i) whether the product can handle the listed requests, (ii) if the team has the capability to deliver, and (iii) the risks and benefits of pursuing.
Instant Capability Check
Imagine you’re a chef tasked with preparing a multi-course meal for a prestigious event. Instead of manually checking each recipe against your pantry items, imagine if a system could instantly scan every ingredient in your kitchen, cross-referencing them with the dishes you need to make. Within moments, it tells you which dishes you’re fully prepared for, which ones need a slight twist, and which ones you simply can’t make with the current ingredients. That’s precisely how AI can enhance specific facets of the RFP process.
AI can cross-reference the information from the RFP with the company’s knowledge base, wiki pages, recent product releases and the product roadmap to quickly determine which requirements are already met, which ones require customization, and which ones the product can not support. Understanding unstructured data of various forms and formats is a significant advantage, making such an assessment feasible. As a result, nearly 80% of the RFP can be addressed, allowing the team to focus on the remaining 20%, to tailor the response to a customer’s specific needs.
Predictive Analytics
How do you select the right project while minimizing risks? AI can offer predictive analytics by analyzing a company’s historical RFP responses and outcomes. This could include data on which RFPs were won, lost, or abandoned, as well as the specific characteristics of those RFPs. Once trained, the AI model can assign a Probability Score to new or upcoming RFPs. This score represents the likelihood of winning the RFP based on the historical patterns associated with similar past RFPs. The probability score can be informed by a wide range of data considered relevant by the company, including internal customer scoring, sales forecasts, or publicly accessible information on a customer’s values and working culture.
Delivery Plans and Resource Optimization — the never-ending question of whether your company has the appropriate resources to undertake a project and meet its delivery schedule. AI simplifies this challenge by analyzing your existing resources, skill sets, assignments, and availability. With these insights, AI can accurately determine the feasibility of taking on a new project. Additionally, AI can use historical project data to estimate the completion time for similar projects. This information helps with setting realistic project timelines and expectations, reducing the chances of delays.
Revenue Targets — How can you confidently determine whether an RFP is a sound investment? It’s no secret that financial objectives are central to every company’s strategy. By assessing projected revenues against estimated costs, AI can provide you with a data-driven perspective promptly. This approach empowers you to make informed decisions about which projects align with your financial objectives, ensuring you stay on track to meet your revenue targets.
Competitive edge — how can you be sure you’re making choices that will give you an advantage in the market? By analyzing market trends, competitor intelligence, and historical data, AI could provide you with a holistic view of the competitive landscape. This information not only allows you to make well-informed decisions but also enables you to identify projects that leverage your strengths, ultimately granting you a solid competitive advantage.
In today’s dynamic business environment, adaptability is the key. AI doesn’t just make predictions, it adapts them in real-time. As RFPs evolve, AI continually refines its recommendations based on live data, market shifts, and feedback.
Bringing it All Together: AI and the RFP Process
If you’re in the trenches with RFPs, you know the routine all too well. The skepticism around AI is out there, and sure, it’s got its learning curve. But let’s be real, the edge it gives in understanding big data and keeping everyone’s heads above water when deadlines approach can’t be denied.
Think AI is just about cutting corners? Not quite. It’s about giving a team the breathing room to think bigger and do better, without the usual chaos. And this isn’t just for the big players. Whether you’re a startup or a seasoned enterprise, embracing AI now will bring real, tangible benefits.
Today, in November 2023, AI is no longer just a buzzword. It’s a practical tool that can reshape how we handle RFPs. It’s is important to make it work for you, your processes and your team dynamics. Integrating AI isn’t about following a trend — it’s about leveraging technology to work smarter, not harder.