You’ve heard the buzz—“AI will change everything.” Yet if your proposal or RFP process still feels manual, scattered, and slow you’re not alone. According to McKinsey & Company’s 2025 “State of AI” report, nearly 90% of organizations say they’re using AI in at least one business function. The gap isn’t adoption anymore—it’s value capture and end-to-end transformation.

For sales, proposal and rev-ops teams, that gap lightly whispers one thing: AI for RFP automation isn’t optional—it’s becoming essential.

Why the “State of AI 2025” matters for proposal and RFP automation

The McKinsey report gives three clear signals:

  • Broad AI deployment, but few firms redesigning workflows to capture strategic value.
  • A rise in “agentic” AI systems (multi-step automation) especially in knowledge-intensive areas.
  • Governance, risk, and leadership now separate winners from the also-rans.

For proposal teams whose world revolves around RFP intake, drafting, review, submission and reuse, those signals map directly to your daily pain points. The keyword here: AI for RFP automation.

Lesson 1: Workflow redesign is the secret sauce

McKinsey found that among dozens of variables, workflow redesign had the strongest correlation with AI-driven bottom-line impact—far more than simply piloting a new tool.

What that means for your RFP team: Deploying an AI drafting tool is great—but the real lift comes when you restructure how proposals move:

  • Intake → auto-assign → draft → review → submission → reuse.
  • Identify hand-offs, delays, missing logic, and content gaps.
  • Insert automation and AI at the right points—not just “one more tool.”

Actionable tips:

  • Map your current RFP end-to-end process and highlight bottlenecks (time to draft, review repeats, version chaos).
  • Identify where AI can step in (content retrieval, first-draft generation, routing).
  • Redesign your workflow to include those automated steps and define new roles/ hand-offs.
  • Measure: track cycle time, error rate, reuse rate.

Lesson 2: Knowledge-intensive workflows + agentic AI = opportunity

The report highlights the growing use of “agentic AI” — systems that execute multi-step workflows, not just single tasks.

Proposal operations are inherently knowledge-intensive: you reuse past answers, align with compliance, tailor messaging, manage versions.

The opportunity: Instead of using AI solely for paragraph writing, you can build an AI-enabled workflow that:

  • Automatically pulls relevant past responses from your content library.
  • Generates a tailored draft aligned to scope, brand and compliance.
  • Routes the draft to the proper SME/reviewer based on content type or risk.
  • Captures reviewer feedback and refines future drafts (continuous improvement).

Actionable tip: Start a pilot “mini-agent” workflow: automate draft generation + routing + tagging → human review → lessons-learned. Track metrics: drafting time, review time, reuse rate, quality score.

Lesson 3: Governance, trust & risk matter

McKinsey reports that 51% of organizations using AI experienced at least one negative consequence (inaccuracy, IP leak, privacy breach). Governance is now a key discipline for leading AI adopters.
In the proposal/RFP world, risks include: outdated responses, non-compliant language, auditing gaps, version drift, IP exposure.

Actionable tips:

  • Clearly assign content owners and define update cycles for your library.
  • Embed approval gates for AI-generated responses (human-in-loop).
  • Maintain audit logs for changes, who made them, when and why.
  • Regularly test your process for accuracy, compliance, brand-alignment.
  • Use metrics: number of compliance errors found post-submission, percentage of responses revised for non-compliance.

Lesson 4: The AI-gap is growing — proposals are a battleground

The McKinsey report identifies a fast-emerging divide: organizations that invest in transformation (workflow redesign + governance + leadership) are pulling ahead of those still experimenting.
For proposal teams, this means: your ability to turn RFPs into a repeatable, efficient, high-quality machine will become a competitive edge. The moment your rival automates and you don’t, you risk losing time, margin and even deals.

Actionable tips:

  • Measure your baseline: average time to respond, reuse percentage, win-rate trend, cost per proposal.
  • Build a roadmap: pilot → cross-team → enterprise.
  • Communicate results: show savings in time, improved win rates and reusable assets.
  • Tie automation gains to revenue and margin metrics so leadership can see ROI.

How Settle helps you act now

Here’s how Settle aligns with each of the four lessons above:

  • Workflow redesign: Settle supports the full end-to-end process—intake, content library tagging, automated draft generation, review routing, submission tracking.
  • Agentic/knowledge workflows: Settle uses AI to map questions → past answers → generate drafts aligned with your brand, compliance and risk profile.
  • Governance & audit: Settle provides version control, content-owner tracking, approval flows and audit logs—so you scale safely.
  • Scaling value: Settle gives dashboards on reuse rate, drafting turnaround time, win-rate impact—so you move from pilot to enterprise value.

We designed Settle so teams can stop responding to RFPs like one-offs, and start treating them like repeatable operating rhythms. If you’re ready to move beyond experimentation and build a proposal machine built for the future, Settle is the platform built for the next wave of proposition teams.

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Don’t let your team slip into the “AI waiting room.” The McKinsey 2025 report shows a clear path—and the cost of standing still is real. Book a demo of Settle today and start automating your RFP process to move faster, win more, and scale smarter.