Rethinking Proposal Management in the Age of AI x Lessons from Ben Hills, CEO of Iris

Published on October 2, 2025

Why Proposal Management Needs a Reset

Proposal teams face an increasingly unforgiving reality. Buying behaviors have shifted, procurement cycles are shorter, and more deals than ever flow through RFPs.

According to Iris CEO Ben Hills, two-thirds of enterprise sales now involve a formal proposal process, and industries from janitorial supplies to transportation are issuing billions of dollars in RFPs each year.

Yet while sales leaders often dismiss RFPs as administrative burdens, Hills argues that they are one of the most strategic levers for revenue growth. “Proposal teams hold the wealthiest set of knowledge in a business because they are where the rubber meets the road on buyer expectations,” he explains.

This shift demands a reframe.

Proposals are not paperwork; they are a strategic conversation with the buyer. And with AI now able to streamline capture, automate compliance checks, and personalize responses at scale, proposal managers have a rare opportunity to step into a leadership role.

This article distills three of Hills’ most important insights:

how buying behavior is evolving
how AI is unlocking new value across the RFP lifecycle
how proposal teams can modernize content management and personalization to win more business

The New Buying Reality with RFPs as the First Sales Conversation

For decades, conventional wisdom (and data) held that if an RFP wasn’t written for you, you had little chance of winning. That assumption is now outdated.

Hills points to several forces changing buyer behavior:

Contract expirations from pandemic-era purchases. As three-year SaaS contracts expire, buyers are issuing structured RFPs to evaluate alternatives.
Buyer efficiency. Faced with dozens of potential vendors, buyers use RFPs to narrow the field without committing to 50 discovery calls.
Generative AI. Tools like ChatGPT make it simple for procurement teams to draft RFPs quickly, lowering the barrier to issuing them.

The result is more competition, more structured evaluation, and higher stakes. “Prospects are genuinely looking for the best solution. Sometimes there’s no sales cycle other than the RFP,” Hills notes.

McKinsey confirms this shift: top-performing B2B companies that embrace digital buying journeys and automation grow revenue nearly twice as fast as peers (McKinsey, Next-Gen B2B Sales).

Action point: Sales leaders must treat every RFP as a live sales opportunity, not an afterthought. Assume the buyer is open to switching vendors and position responses accordingly.

Four High-Impact AI Applications for Proposal Teams

Most teams view AI as a shortcut for drafting. Hills warns that this misses its true potential. He highlights four areas where AI delivers transformative value:

Capture & Qualification. AI can extract every requirement in an RFP and cross-check against institutional knowledge, cutting the go/no-go process from five hours to 30 minutes.
Customer Success Handoff. AI can automatically generate implementation checklists based on commitments made in proposals, eliminating manual post-win debriefs.
Trend Analysis. By aggregating buyer questions across proposals, AI helps executives see where expectations are shifting, like on security, compliance, or product features.
Project Management. Agentic AI can handle nudges, reminders, and deadline notifications, saving sales teams up to 10 hours per week.

“AI doesn’t feel bad sending reminders. Putting AI in the middle reduces friction between teams.” —Ben Hills

This isn’t theoretical. Gong’s research shows AI-driven automation in sales saves reps 2–3 hours per week and improves deal conversion when tied to personalization (Gong Labs, We Measured the ROI of AI in Sales).

Action point: Proposal leaders should build an AI playbook that spans capture through implementation, not just drafting.

Fixing Content Management

Knowledge bases are notorious for collapsing after six months, leaving teams with outdated boilerplate. Hills advocates for a “primary source content” model:

Source content where it lives. Product roadmaps in engineering tools, case studies in marketing platforms, and release notes in Confluence.
Create a content ownership map. Assign accountability for each integration so updates flow directly into proposals.
Rely on AI for contradiction detection. Modern tools can flag inconsistencies (e.g., two different customer counts) before they reach buyers.

McKinsey reinforces the urgency: B2B companies that fail to personalize buyer journeys with accurate, dynamic content lose ground to competitors who treat content as a live system, not a static repository (McKinsey, Future of B2B Sales: The Big Reframe).

Action point: Conduct a whiteboard exercise to map every critical source of proposal content, assign owners, and integrate them into your proposal system.

Context Engineering is Personalization at Scale?!

With AI, the risk of generic, one-size-fits-all responses grows.

The antidote is called context engineering, which is feeding AI the right buyer data to shape narratives.

For example:

A state university versus a liberal arts college requires different ROI framing and case studies.
Buyers nervous about adoption should see detailed implementation support, while tech-savvy buyers may want deep feature roadmaps.

Proposal leaders can build repeatable prompting frameworks that automatically inject CRM data, call transcripts, and competitor insights into draft responses.

HBR highlights this same trend: successful sales teams are those that harness AI agents to deliver hyper-contextual insights to buyers at scale (HBR, How Successful Sales Teams Are Embracing Agentic AI).

Action point: Develop a “context pack” for each proposal, including buyer goals, industry benchmarks, competitor positioning, and prior interactions. Feed this into AI tools before drafting.

Checklist: Building AI-Enabled Proposal Excellence

Reframe proposals as revenue strategy, not admin.
Redesign capture with AI-powered requirement analysis.
Integrate handoff automation into customer success workflows.
Modernize content with primary-source connections.
Engineer context to personalize responses at scale.
Measure pipeline coverage to expand proposal team influence across 100% of deals.

FAQs

Q1: Are RFPs still worth responding to if we haven’t met the buyer? Yes. Buying behaviors have shifted, and many RFPs now begin without a prior sales cycle. Enter with a winning narrative, not a defeatist mindset. (This hurts me to write.)

Q2: What’s the biggest mistake proposal teams make today? Treating RFPs as predetermined losses. AI and buyer trends prove otherwise; personalization and quality responses can secure unexpected wins.

Q3: How can proposal teams balance speed with quality? Set expectations with leadership. Fast but shallow responses raise red flags. Use AI to compress timelines while preserving personalization.

Q4: What’s the future of proposal management roles? Proposal leaders will expand from influencing 5% of deals to covering 100% of pipeline by scaling expertise with AI.

Q5. Why are RFPs important for businesses?

RFPs are critical because they represent a large share of revenue opportunities. More than 50% of private sector revenue and two-thirds of enterprise sales involve proposals. Winning RFPs directly impacts pipeline growth and customer acquisition.

Q6. How is AI changing proposal management?

AI is transforming proposal management by:

Automating go/no-go decisions based on requirements.
Accelerating drafting and personalization of responses.
Powering customer success handoffs by extracting commitments.
Aggregating buyer trends from multiple RFPs to inform strategy.
Acting as an agentic project manager to handle reminders and follow-ups.

Q7. What is context engineering in RFP responses?

Context engineering is the practice of feeding AI systems with the most relevant buyer intelligence, such as industry, role, goals, competitors, and sales call transcripts, before generating content. It produces far more personalized and persuasive RFP responses than generic drafting.

Q8. How can proposal teams balance speed with quality?

The best approach is to compress, not cut. AI can shorten personalization timelines from weeks to days, ensuring responses are both fast and deeply tailored. Personalization should always trump boilerplate speed, as rushed answers can raise security red flags and lower win rates.

Q9. How can I improve my proposal win rate right now?

Start with these immediate actions:

Audit your current process to spot manual bottlenecks.
Map knowledge sources and assign content owners.
Develop context packs for each RFP to improve personalization.
Use AI tools not just for drafting, but for qualification, handoff, and analysis.
Share buyer trend reports with leadership to elevate your team’s impact.

Conclusion & Call to Action

The future of proposals belongs to teams that stop treating RFPs as burdens and start treating them as strategic sales conversations. With AI, proposal managers can qualify faster, personalize deeper, and influence company strategy in unprecedented ways.

As Hills puts it, “If we could put our best foot forward in 100% of our proposals, we’d win more business and have more happy customers”.

Explore how Stargazy helps proposal leaders navigate these shifts, and connect with Iris to see how AI-enabled proposal tools are redefining the landscape.

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