AI in RFPs demands accuracy, trust, and governance to protect revenue and ensure compliant enterprise sales outcomes.
Eugene Ho, as Chief Product Officer at Loopio, how has your experience building enterprise software influenced the way you evaluate AI’s role in high-stakes sales processes?
After more than 20 years building enterprise software, including time at Microsoft, I’ve learned to be both excited about AI technology and realistic about where it actually delivers value. In high-stakes sales processes like RFPs, AI can’t just be about speed. The real opportunity is helping teams make better decisions, deliver quality, and avoid costly mistakes. When we built Loopio, we were very intentional about using AI to support teams — surfacing the right information, prioritizing the best answers — while still keeping people firmly in control. That balance is critical when revenue and reputation are on the line.
RFPs impact a significant share of revenue, yet many teams rely on general-purpose AI—what makes RFP responses fundamentally different from other business content AI handles well?
RFPs are very unforgiving. Unlike marketing copy or internal content, there’s very little room for interpretation or error. You’re dealing with precise questions, strict requirements, and often regulatory or legal constraints. A response needs to reflect deep institutional knowledge and be tailored to a specific buyer and use case. That’s where general-purpose AI tends to struggle. It wasn’t designed to work within those constraints or understand the nuances that can make or break a deal. It lacks the embedded context around a company’s past responses, approved language, risk tolerance, and customer-specific nuances that are critical in an RFP. Without that depth of understanding, even small missteps can cost a deal.
Despite its popularity, ChatGPT often falls short in enterprise sales contexts—where do these gaps become most visible during the RFP lifecycle?
The gaps usually show up when accuracy and context really matter. ChatGPT is great at generating language, but it doesn’t inherently understand your company’s latest product details, market positioning, or legal boundaries. During an RFP, that can be risky. The moment you need to tailor a response to a specific customer, industry requirement, or compliance standard, generic AI starts to break down, and that’s exactly where sales teams can’t afford mistakes. On top of that, when teams rely on the same general-purpose models, responses tend to default to safe, familiar phrasing, making it harder to differentiate and easier for vendors to blur together in the buyer’s evaluation.
Many organizations value speed in responding to RFPs—how does prioritizing fast output over accuracy introduce commercial and legal risk? Speed definitely matters in sales, but speed without accuracy can be dangerous. One outdated claim or incorrect specification can undermine trust, stall a deal, or even create legal exposure. We’ve seen situations where moving too fast ends up slowing everything down later. That’s why we focus on helping teams respond quickly and confidently — using AI that’s grounded in trusted, current information rather than just generating something that sounds right.
Speed is critical in sales, but when the focus is solely on delivering a response quickly, accuracy suffers. In RFPs, a single inaccuracy, whether it’s an outdated product specification or a missed legal requirement, can lead to a loss of trust, a compliance violation, or even a legal dispute. Prioritizing speed over accuracy is risky, which is why Loopio’s approach ensures that AI-driven responses are not only fast but focus on delivering quality with thoroughly vetted answers, only pulled from trusted data sources and validated in real time as teams respond.
Hallucinated responses are a growing concern—how does a single incorrect claim in an RFP ripple across trust, compliance, and deal momentum?
In high-stakes sales, a single incorrect claim in an RFP can have a significant impact. Trust is the cornerstone of any sales relationship, and once that’s broken, it’s difficult to regain. An incorrect claim can raise concerns about the integrity of the organization and force buyers to question everything else in the response, effectively derailing months of relationship-building, internal reviews, and stakeholder alignment. The result is not just a stalled deal, but lost momentum and sunk time and resources that are rarely recoverable. From a compliance perspective, making incorrect claims can also expose the company to legal risk, especially if those claims touch on product performance, security standards, or legal obligations. That’s why accuracy isn’t just a technical issue; it’s a trust issue.
RFP work demands coordination across sales, legal, security, and product teams—why does this complexity expose the limits of prompt-based AI tools?
RFPs are inherently complex because they often require input from multiple departments, including sales, legal, and security teams. Each department contributes unique insights, and the integration of these perspectives into a cohesive response requires a high degree of coordination. Prompt-based tools aren’t built to manage that kind of collaboration or accountability. They don’t understand ownership, approvals, or how different teams contribute to a final answer. Without that structure, things can fall through the cracks, which is exactly what teams are trying to avoid during a competitive bid.
Institutional knowledge is critical in competitive bids—how should companies think about protecting and operationalizing this knowledge when using AI?
Institutional knowledge is invaluable, especially in competitive bids where the specifics of previous proposals, customer insights, and product knowledge play a huge role. Companies should think of AI as a tool to organize and protect that knowledge rather than replace it. At Loopio, we focus on building systems that allow AI to access approved, trusted sources of information and incorporate workflows that ensure that only the most up-to-date and accurate data is being used. By protecting institutional knowledge and integrating it into the AI tools, companies can create a more efficient, scalable, and accountable RFP process.
High-performing sales teams are adopting specialized AI—what differentiates these purpose-built tools from open, general models in terms of reliability?
Purpose-built AI is designed with a very specific job in mind. In the RFP world, that means understanding structured questions, compliance requirements, and enterprise workflows. General models are incredibly flexible, but flexibility isn’t always what you want in high-risk scenarios. Specialized tools are built to be reliable—grounded in the right data, aligned with company standards, and integrated into the systems teams already trust.
From a product leadership perspective, how is Loopio approaching AI development to balance automation with governance and accountability?
At Loopio, our philosophy is that automation should never come at the expense of trust. We build AI with governance baked in — from source transparency to validation and security controls. Users should always know where an answer came from and feel confident standing behind it. By pairing automation with clear guardrails, we help teams move faster without sacrificing accountability.
As AI adoption matures in enterprise sales, how do you see organizations shifting from experimentation to systems that actively safeguard revenue and reputation?
We’re already seeing that shift happen. Early experimentation was about curiosity and efficiency. Now, organizations are asking harder questions about risk, governance, and long-term impact. The next phase is about building systems that don’t just save time, but actively protect revenue and reputation. That means treating AI as part of the core sales infrastructure — with the same expectations around accuracy, security, and accountability as any other mission-critical system.
Quote from the author: “AI will revolutionize how sales teams respond to RFPs, but it’s crucial to understand that its value lies not in speed alone, but in the ability to provide accurate, tailored, and compliant responses. At Loopio, we view AI as a tool to streamline workflows and enhance decision-making, not replace human judgment. By integrating AI into an ecosystem that values data integrity and cross-functional collaboration, companies can safeguard their revenue and reputation while empowering their teams to move faster, smarter, and with confidence.” – Eugene Ho, Chief Product Officer, Loopio
Eugene Ho
Chief Product Officer at Loopio