In 2026, AI is transforming both sides of the RFP process. On the vendor side, tools like DeepRFP, Inventive AI, and Responsive automate RFP response drafting — parsing requirements, suggesting content from proposal libraries, generating first drafts, and routing to subject matter experts. On the buyer side, Scale.com's RFP Evaluation Assistant "analyzes large volumes of submissions, extracts data matching key criteria, and generates detailed comparative insights."

And VisibleThread sits in the middle, using NLP to "flag issues such as unclear requirements, vague terminology, inconsistent phrasing, passive voice, and missing commitments that often lead to evaluator confusion or post-award disputes."

The fact that a tool like VisibleThread exists is the market signal: RFP responses have a readability problem, and it is costing vendors deals. When an evaluator cannot read the proposal, the proposal does not get scored well — regardless of how good the solution is.

The RFP readability problem

RFP responses are reviewed by evaluation committees — groups of stakeholders who score proposals against weighted criteria under deadline pressure. A typical evaluator reviews 5-20 proposals, each 50-200 pages long, within a 2-4 week evaluation window. Their job is to score each proposal on: understanding of requirements, technical approach, methodology, past performance, and value.

When proposals are unreadable, the evaluation fails:

1. Evaluator confusion

ContraVault's 2026 guide identifies the specific problems VisibleThread flags: "unclear requirements, vague terminology, inconsistent phrasing, passive voice, and missing commitments." These are readability problems. An evaluator who encounters vague terminology in a technical approach section does not know whether the vendor understands the requirement. An evaluator who encounters passive voice in a compliance response does not know whether the vendor is committing to the requirement. Confusion leads to lower scores.

2. Scoring penalties

Evaluators score what they can understand. If a proposal's technical approach is written at a college reading level with domain jargon that the evaluator — who may be a procurement professional, not a technical expert — cannot follow, the technical score is lower. The proposal may have the best technical solution, but if the evaluator cannot read it, the score does not reflect the quality.

3. Post-award disputes

ContraVault notes that readability problems "lead to evaluator confusion or post-award disputes." A proposal with vague commitments and unclear language creates ambiguity about what the vendor promised. When the vendor delivers something different from what the evaluator thought they read, the dispute begins. Readable proposals reduce disputes because the commitments are clear.

4. AI evaluation adds a new layer

Scale.com's RFP Evaluation Assistant uses AI to analyze submissions and extract comparative data. If the proposal is written in complex language, the AI evaluator may extract inaccurate information — just as an AI reading a 10-K filing makes errors on complex narrative sections. A proposal that is readable by AI is more accurately evaluated by AI, which means the human evaluators receive better comparative insights.

Why AI-generated RFP responses are hard to read

AI tools that generate RFP responses face the same readability challenges as AI in every other domain:

1. Source material complexity

RFP responses are generated from proposal libraries, past proposals, and technical documentation — all written at a high reading level. AI reproduces the register of the source material.

2. Completeness over clarity

AI tools optimize for answering every requirement in the RFP. This produces comprehensive responses that cover every point but are dense and hard to scan. Datrick's 2026 guide warns that an RFP "should not reward the longest list of models, tools, accelerators, and generic capabilities." The same applies to responses: the longest response is not the best response.

3. Vague terminology

VisibleThread specifically flags "vague terminology" as a problem in RFP responses. AI tends to use generic language — "robust solution," "comprehensive approach," "industry-leading capabilities" — that evaluators cannot score because they do not mean anything specific.

4. Passive voice

VisibleThread flags passive voice because it obscures commitment. "The deliverable will be completed" does not tell the evaluator who completes it, when, or how. "Our team will complete the deliverable by Q3 using the agile methodology" is a clear commitment. AI tends to default to passive voice, which weakens the proposal.

The fix: add a readability layer to the RFP pipeline

The fix is to add a readability layer to the AI RFP response pipeline:

1. AI generates the RFP response from the proposal library and

RFP requirements

1. Run the response through VisibleThread (or similar) to flag

readability issues — vague terminology, passive voice, unclear requirements

1. **Run the flagged sections through

ELI5 AI** to rewrite them at a 5th-grade reading level with four levels side-by-side

1. Use the appropriate level for each section:

• Adult level for the technical approach (detailed but clear)
• Teenager level for the executive summary and methodology

(scannable by non-technical evaluators)

• 10-year-old level for the compliance matrix descriptions

(direct commitments, not vague language)

• 5-year-old level for the key commitments box that every

evaluator reads first

This ensures the proposal is both technically complete and evaluator-readable — the combination that wins RFPs.

How ELI5 AI helps proposal teams

ELI5 AI is a free, no-login text simplifier that takes any RFP response section and rewrites it at a 5th-grade reading level with four levels side-by-side: 5-year-old, 10-year-old, teenager, and adult.

For proposal teams, the workflow is:

1. AI generates the response draft (DeepRFP, Inventive AI,

Responsive, or ChatGPT/Claude)

1. Paste each section into ELI5 AI — get a plain-language

version at four reading levels

1. Select the appropriate level for the evaluator audience:

• Technical evaluators get the adult level (precise but clear)
• Procurement evaluators get the teenager level (scannable)
• Executive evaluators get the 10-year-old level (summary-level)

1. Check for vague terminology and passive voice — ELI5 AI's

simplified output naturally replaces vague language with specific statements and passive voice with active voice

1. Verify compliance — ensure the simplified response still

addresses every RFP requirement. The simplification should change the reading level, not the content

What proposal teams should do now

• Audit your last 3 RFP responses for readability. Measure the

reading level of the executive summary, technical approach, and compliance matrix. If they score above grade 12, evaluators are struggling

• Simplify the executive summary first. The executive summary is

the most-read section. Every evaluator reads it. It should be at grade 8-10 so every evaluator — technical or not — can understand the proposal's value in 60 seconds

• Fix vague terminology. Replace "robust solution" with what the

solution actually does. Replace "comprehensive approach" with the specific steps. Use ELI5 AI to identify and simplify vague language

• Convert passive voice to active voice. "The system will be

configured" becomes "Our team will configure the system." Active voice creates commitment; passive voice creates ambiguity

• Track win rate against readability. Compare the reading level

of won vs. lost proposals. If won proposals are consistently more readable, you have proven the ROI of readability in RFP responses

• Prepare for AI evaluation. Scale.com's RFP Evaluation

Assistant and similar tools are now part of the evaluation process. Proposals that are readable by AI will be more accurately scored by AI evaluators — which means better comparative insights for the human decision-makers

The bottom line

RFP responses are scored by evaluators who read dozens of proposals under deadline pressure. The proposal they can read quickly and understand accurately scores higher than the proposal that is technically superior but unreadable. VisibleThread exists because the market recognizes this — and the market is growing because AI- generated proposals are making the readability problem worse.

The proposal teams that build a readability layer into their AI RFP response process will produce proposals that evaluators can read, understand, and score fairly. The proposal teams that do not will keep submitting comprehensive but incomprehensible proposals — and losing to vendors who say the same thing more clearly.

An RFP response the evaluator cannot read is not a proposal. It is a document that takes itself out of the competition. Plain language puts it back in.

Try it: paste any RFP response section into ELI5 AI and get a plain-English version that evaluators can read, understand, and score fairly — four reading levels, no account required.