Why LLM Readability Matters for Your Proposals – and How to Rebuild Them

What does LLM readability of proposals mean?

LLM readability describes how completely and accurately a language model such as ChatGPT, Microsoft Copilot or Claude captures and reproduces the key messages of your proposal. The background: according to Forrester, 94% of B2B buyers use AI in the buying process. So after the meeting, your pitch deck very likely ends up in an AI tool. The buying committee then reads the summary instead of your slides.

A proposal is LLM-readable when that summary contains three things: what sets you apart, the concrete benefit for the client and the reason why you in particular should win the work. If one of them is missing, you lose the comparison against competitors without ever finding out.

For agencies and consultancies this is especially critical. Their strongest argument often sits in a graphic, a mood board or in the presentation itself. Exactly this content barely reaches a language model, if at all. This article shows why that is, what the consequences are and how to rebuild your proposal so that it wins in every AI summary.

How do buying committees read proposals and pitch decks today?

Buying committees increasingly read proposals through AI summaries instead of slide by slide. The numbers show how quickly this has changed:

• 94% of B2B buyers use AI in the buying process, up from 89% a year earlier (Forrester Buyers’ Journey Survey 2025).
• 61% use AI tools provided by their own company (ibid.).
• 57% of German companies with 20 or more employees use AI, up from 20% two years earlier (Bitkom, 2026).
• 61% of B2B buyers prefer a buying process without any sales contact at all (Gartner, 2025).

The typical sequence after your presentation therefore looks like this: someone in marketing uploads three agency decks to Copilot. They ask for a summary and a comparison. Management, procurement and IT then read only that result. Your deck is no longer read by people but pre-sorted by a machine.

Why does this hit agencies and consultants particularly hard?

Agencies and consultancies don’t sell products with a data sheet. They sell thinking, creativity and methodology. In their pitches, these qualities rarely appear in running text. They live in key visuals, campaign mock-ups, process graphics and above all in the talk itself. The presentation depends on the person giving it.

For a long time that was an advantage. Whoever convinced the room won the account. Today, however, the room is just one of several stations. After the meeting the deck is passed around internally, often to people who never sat in the pitch. According to Gartner, 69% of B2B buyers also report inconsistencies between a vendor’s website and what sales tells them. Every gap in the deck becomes a risk.

Consultancies face a similar problem with different material. Their methodology often sits in frameworks and matrices. To a language model, that is frequently just a collection of loose terms without context.

What happens to your differentiator in the AI summary?

In most cases it disappears. This is down to two well-documented weaknesses of language models.

First, models struggle to interpret graphics. In the CharXiv benchmark (a research team led by Princeton University, NeurIPS 2024), the best models read titles and labels of real-world charts correctly a good 84% of the time. When they had to combine information from several chart elements, however, the best model reached only 60.2%, compared with 80.5% for humans. Whatever exists only as a graphic therefore easily arrives distorted in an AI summary, or not at all.

Second, models overlook information in the middle of long documents. Researchers led by Stanford University showed in “Lost in the Middle” that performance is highest when relevant content appears at the beginning or end. In the middle it drops significantly.

Yet that is exactly where your differentiator usually sits: on slide 14, as a graphic, without an explanatory sentence. Instead, the AI summarises what every vendor says. Your pitch turns into “experienced agency with a creative approach”. That makes you interchangeable, and price decides.

Which mistakes make proposals unreadable for ChatGPT and Copilot?

It is almost always the same five patterns. They arise because decks are built for the talk, not for reading afterwards.

1. Key message only in the image: The differentiator sits in a key visual, an infographic or a screenshot. Text embedded in images is often not extracted, or extracted incorrectly.
2. Argument only in the talk: The slide shows three keywords, the actual reasoning was given verbally. Without speaker notes it is completely missing from the document.
3. Interchangeable wording: Terms like “holistic”, “tailored” or “data-driven” are used by every competitor. The AI summarises them as standard.
4. Wrong order: The strongest point sits in the middle of the deck, after the agency introduction and the collection of case studies.
5. Missing evidence: Claims without a number, client name or result look like marketing noise to a model.

Each of these mistakes leads to the same outcome. The AI summary describes you in generic terms, while a competitor with clearer text is reproduced precisely.

How can you tell whether your proposal is LLM-readable?

You can tell with a simple self-test that takes less than ten minutes. Upload your latest pitch deck as a PDF to ChatGPT or Copilot. Then ask exactly the questions a buying committee would ask:

• “Summarise this proposal in five sentences.”
• “What sets this vendor apart from other agencies?”
• “What measurable benefit does the proposal promise?”
• “What risks or open points do you see?”

Compare the answers with what you actually meant to say in the pitch. Does your differentiator appear, literally or in substance, in the answer to the second question? If not, your proposal has a readability problem.

The comparison test is even more revealing. Upload your deck together with a publicly available competitor document and ask for a recommendation. This shows you how a decision-maker perceives you in a direct comparison. In our work with agencies, the result is rarely what the pitch team expects.

How do you build an LLM-readable proposal?

An LLM-readable proposal works for two readers at once: for people in the pitch and for machines afterwards. The rebuild follows four principles.

1. The key message belongs at the start. Use the first two slides for a written-out summary: the client’s problem, your approach, what makes you different, the expected result. This gets around the “Lost in the Middle” effect.

2. Every graphic gets a sentence. Below every key visual and every framework, write a complete sentence that states the message. The image convinces people, the sentence informs the AI.

3. The talk moves into the document. Whatever you add verbally belongs in the speaker notes or in an accompanying leave-behind document.

4. Evidence instead of adjectives. Replace “innovative” with a number, a client name or a result. Research on Generative Engine Optimization (Aggarwal et al., KDD 2024) shows that citations, quotations and statistics increase visibility in AI answers by 30 to 40%. What applies to websites applies to your deck as well.

Which steps belong in rebuilding a pitch deck?

Rebuilding an existing deck takes six steps. You can run through this checklist before every send-out:

• Add an executive summary: Slide 2 contains the four key messages as full sentences, not as keywords.
• Spell out your differentiation: What makes you different is stated in one sentence that no competitor could say in the same way.
• Caption images: Every graphic, every mock-up and every framework has a line of text with the actual message.
• Fill in speaker notes: The verbal reasoning is in the document, not just in your head.
• Add evidence: At least three numbers, client results or references support your key message.
• Repeat the AI test: The rebuilt deck passes the self-test from the previous section.

One thing matters here: LLM readability does not make a deck more text-heavy in the talk. The visual layer stays, it simply gets a second, machine-readable layer. Many teams therefore send two versions: the presentation deck for the meeting and a readable PDF for internal circulation at the client.

Frequently asked questions about LLM readability of proposals

Is LLM readability the same as GEO?

No. Generative Engine Optimization targets public content such as websites, so that AI search engines cite you. LLM readability concerns confidential documents that your client uploads to an AI tool themselves. The principles are similar, but the lever lies in the proposal rather than on the website.

Does this only affect large tenders?

No. With mid-sized budgets in particular, there is often no time internally to read every deck in full. That is where summaries are used most often.

Will this make our decks more boring?

No. The visual layer stays unchanged. You simply add text elements that barely stand out in the talk.

How much effort does the rebuild take?

For an existing standard deck, expect a few hours. Once it is set up as a template, every further proposal is LLM-readable automatically.

Who is already talking about this problem?

In the DACH region the topic has hardly been discussed so far. Teams that switch now gain a real edge in the pitch.

How does a proposal diagnostic with PitchDepartment work?

With the proposal diagnostic, we check whether your pitch deck wins or goes under in an AI summary. The result is a concrete rebuild plan for your proposal.

1. Analysis: You send us a current pitch deck or proposal. We test it with ChatGPT, Microsoft Copilot and Claude, the way your client would.
2. Diagnosis: You receive a report showing which of your key messages come through in the summaries and which get lost.
3. Rebuild: We rewrite the executive summary, differentiation and image statements and, on request, build a reusable template for your team.

The result is a deck that convinces in the room and is reproduced correctly in any AI tool afterwards. Your differentiator also reaches the decision-makers who never sat in your pitch.

Sources

• Forrester: B2B Buyers Make Zero-Click Buying Number One (Buyers’ Journey Survey 2025)
• Gartner: 61 % of B2B Buyers Prefer a Rep-Free Buying Experience (2025)
• Bitkom: Erstmals nutzt Mehrheit der Unternehmen KI (2026, in German)
• Wang et al.: CharXiv – Charting Gaps in Realistic Chart Understanding in Multimodal LLMs (NeurIPS 2024)
• Liu et al.: Lost in the Middle – How Language Models Use Long Contexts (TACL 2024)
• Aggarwal et al.: GEO – Generative Engine Optimization (KDD 2024)