AI for RFP responses — Lincoln
First-draft proposals, qualifications statements, and capabilities decks built from your prior wins and project sheets — drafted by AI, refined by the partner or principal who owns the relationship.
Text Rosey · Schedule a call →The workflow, end to end
What goes in, what the AI does, what comes out, what your team gets back.
- Input
- RFP requirements + prior winning proposals + project sheets + firm bio + team qualifications
- Work
- Map requirements to capabilities; draft response in firm voice; pull project experience; flag unmet requirements
- Output
- First-draft proposal in the firm's voice, ready for partner or principal review and tailoring
- Saved
- 3–5 hours per proposal
What this looks like in production
RFP responses are the canonical assemble-and-tailor workflow: most content is reusable from prior proposals; the value is in tailoring to the specific RFP and the relationship. AI handles assembly; the partner handles tailoring.
At a Lincoln mid-market firm, the workflow follows McKinsey's high-performer pattern: workflow redesign rather than typing acceleration. AI maps RFP requirements to your capabilities, drafts the response in your voice, pulls relevant project experience, and flags any requirements where the firm doesn't yet have a clean match.
The principal reviews, refines, adds relationship context, and signs. The 3–5 hours saved per proposal goes back into the work that AI can't do.
How we run it
- Build the proposal corpus — last 3–5 years of winning responses.
- Build the project-experience library with team-attribution metadata.
- Map RFP requirements to capabilities.
- Draft in firm voice — AI uses your tone, structure, section headings.
- Partner or principal review — refine voice, add relationship context, sign.
- Audit trail — every proposal archived with corpus elements used.
Common questions
- Will RFP issuers detect AI-drafted responses?
- Only if it's AI-only. Partner refinement makes the response voiced, tailored, relationship-specific.
- Should we disclose AI use?
- Best practice is yes when the RFP asks. Silent use carries risk if discovered later.
- What about firm voice — won't AI flatten it?
- Will if used as one-shot generator. Won't if you build a corpus of your firm's prior writing.
- Federal RFPs (GSA, GovCon)?
- Yes, with stricter discipline. AI drafts the structure; a proposal specialist ensures verbiage matches federal conventions.
- What's the win-rate impact?
- Hard to attribute cleanly. The honest claim is reallocation of partner time, not AI writing quality.
Sources
- AI high performers are nearly 3x as likely as others to say their organizations have fundamentally redesigned individual workflows — The state of AI in 2025: Agents, innovation, and transformation, McKinsey & Company (QuantumBlack, AI by McKinsey), 2025
- About 6% of organizations qualify as 'AI high performers' — those attributing 5%+ EBIT impact to AI — The state of AI in 2025: Agents, innovation, and transformation, McKinsey & Company (QuantumBlack, AI by McKinsey), 2025
- 74% of respondents say their most advanced GenAI initiative is meeting or exceeding ROI expectations (43% meeting, 31% exceeding) — Now decides next: Generating a new future — State of Generative AI in the Enterprise Quarter four, Deloitte AI Institute, 2025
- 73% of directors and above report creativity improvements from AI vs. 65% of individual contributors — The State of AI in HR 2026, SHRM (Society for Human Resource Management), 2026
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