AITech Interview with Eugene Ho, Chief Product Officer, Loopio

AI in RFPs calls for accuracy, belief, and governance to guard income and guarantee compliant enterprise gross sales outcomes.

Eugene Ho, as Chief Product Officer at Loopio, how has your expertise constructing enterprise software program influenced the way in which you consider AI’s position in high-stakes gross sales processes?

After greater than 20 years constructing enterprise software program, together with time at Microsoft, I’ve discovered to be each enthusiastic about AI expertise and real looking about the place it truly delivers worth. In high-stakes gross sales processes like RFPs, AI can’t simply be about velocity. The actual alternative helps groups make higher selections, ship high quality, and keep away from expensive errors. When we constructed Loopio, we had been very intentional about utilizing AI to help groups — surfacing the best data, prioritizing the very best solutions — whereas nonetheless conserving individuals firmly in management. That stability is important when income and status are on the road.

RFPs impression a big share of income, but many groups depend on general-purpose AI—what makes RFP responses basically completely different from different enterprise content material AI handles nicely?

RFPs are very unforgiving. Unlike advertising and marketing copy or inside content material, there’s little or no room for interpretation or error. You’re dealing with exact questions, strict necessities, and sometimes regulatory or authorized constraints. A response must replicate deep institutional data and be tailor-made to a particular purchaser and use case. That’s the place general-purpose AI tends to battle. It wasn’t designed to work inside these constraints or perceive the nuances that may make or break a deal. It lacks the embedded context round an organization’s previous responses, accepted language, danger tolerance, and customer-specific nuances which are important in an RFP. Without that depth of understanding, even small missteps can value a deal.

Despite its reputation, ChatGPT usually falls quick in enterprise gross sales contexts—the place do these gaps develop into most seen through the RFP lifecycle?

The gaps often present up when accuracy and context actually matter. ChatGPT is nice at producing language, however it doesn’t inherently perceive your organization’s newest product particulars, market positioning, or authorized boundaries. During an RFP, that may be dangerous. The second it’s essential to tailor a response to a particular buyer, trade requirement, or compliance customary, generic AI begins to interrupt down, and that’s precisely the place gross sales groups can’t afford errors. On prime of that, when groups depend on the identical general-purpose fashions, responses are inclined to default to secure, acquainted phrasing, making it tougher to distinguish and simpler for distributors to blur collectively within the purchaser’s analysis.

Many organizations worth velocity in responding to RFPs—how does prioritizing quick output over accuracy introduce industrial and authorized danger? Speed positively issues in gross sales, however velocity with out accuracy might be harmful. One outdated declare or incorrect specification can undermine belief, stall a deal, and even create authorized publicity. We’ve seen conditions the place transferring too quick finally ends up slowing every little thing down later. That’s why we deal with serving to groups reply rapidly and confidently — utilizing AI that’s grounded in trusted, present data fairly than simply producing one thing that sounds proper.

Speed is important in gross sales, however when the main focus is solely on delivering a response rapidly, accuracy suffers. In RFPs, a single inaccuracy, whether or not it’s an outdated product specification or a missed authorized requirement, can result in a lack of belief, a compliance violation, or perhaps a authorized dispute. Prioritizing velocity over accuracy is dangerous, which is why Loopio’s method ensures that AI-driven responses will not be solely quick however deal with delivering high quality with completely vetted solutions, solely pulled from trusted information sources and validated in actual time as groups reply.

Hallucinated responses are a rising concern—how does a single incorrect declare in an RFP ripple throughout belief, compliance, and deal momentum?

In high-stakes gross sales, a single incorrect declare in an RFP can have a big impression. Trust is the cornerstone of any gross sales relationship, and as soon as that’s damaged, it’s troublesome to regain. An incorrect declare can elevate issues concerning the integrity of the group and drive consumers to query every little thing else within the response, successfully derailing months of relationship-building, inside evaluations, and stakeholder alignment. The end result isn’t just a stalled deal, however misplaced momentum and sunk time and sources which are not often recoverable. From a compliance perspective, making incorrect claims may also expose the corporate to authorized danger, particularly if these claims contact on product efficiency, safety requirements, or authorized obligations. That’s why accuracy isn’t only a technical subject; it’s a belief subject.

RFP work calls for coordination throughout gross sales, authorized, safety, and product groups—why does this complexity expose the boundaries of prompt-based AI instruments?

RFPs are inherently complex because they often require input from multiple departments, including sales, legal, and security teams. Each division contributes distinctive insights, and the combination of those views right into a cohesive response requires a excessive diploma of coordination. Prompt-based instruments aren’t constructed to handle that form of collaboration or accountability. They don’t perceive possession, approvals, or how completely different groups contribute to a ultimate reply. Without that construction, issues can fall by way of the cracks, which is precisely what groups are attempting to keep away from throughout a aggressive bid.

Institutional data is important in aggressive bids—how ought to corporations take into consideration defending and operationalizing this information when utilizing AI?

Institutional data is invaluable, particularly in aggressive bids the place the specifics of earlier proposals, buyer insights, and product data play an enormous position. Companies ought to consider AI as a device to arrange and shield that data fairly than exchange it. At Loopio, we deal with constructing methods that enable AI to entry accepted, trusted sources of data and incorporate workflows that be certain that solely essentially the most up-to-date and correct information is getting used. By defending institutional data and integrating it into the AI instruments, corporations can create a extra environment friendly, scalable, and accountable RFP course of.

High-performing gross sales groups are adopting specialised AI—what differentiates these purpose-built instruments from open, normal fashions when it comes to reliability?

Purpose-built AI is designed with a really particular job in thoughts. In the RFP world, which means understanding structured questions, compliance necessities, and enterprise workflows. General fashions are extremely versatile, however flexibility isn’t at all times what you need in high-risk situations. Specialized instruments are constructed to be dependable—grounded in the best information, aligned with firm requirements, and built-in into the methods groups already belief.

From a product management perspective, how is Loopio approaching AI growth to stability automation with governance and accountability?

At Loopio, our philosophy is that automation ought to by no means come on the expense of belief. We construct AI with governance baked in — from supply transparency to validation and safety controls. Users ought to at all times know the place a solution got here from and really feel assured standing behind it. By pairing automation with clear guardrails, we assist groups transfer quicker with out sacrificing accountability.

As AI adoption matures in enterprise gross sales, how do you see organizations shifting from experimentation to methods that actively safeguard income and status?

We’re already seeing that shift occur. Early experimentation was about curiosity and effectivity. Now, organizations are asking tougher questions on danger, governance, and long-term impression. The subsequent part is about constructing methods that don’t simply save time, however actively shield income and status. That means treating AI as a part of the core gross sales infrastructure — with the identical expectations round accuracy, safety, and accountability as every other mission-critical system.

Quote from the creator: “AI will revolutionize how gross sales groups reply to RFPs, however it’s essential to know that its worth lies not in velocity alone, however within the means to supply correct, tailor-made, and compliant responses. At Loopio, we view AI as a device to streamline workflows and improve decision-making, not exchange human judgment. By integrating AI into an ecosystem that values information integrity and cross-functional collaboration, corporations can safeguard their income and status whereas empowering their groups to maneuver quicker, smarter, and with confidence.” – Eugene Ho, Chief Product Officer, Loopio