The Challenge: Slow Proposal Creation

Sales leaders know the pattern: a rep has a great call, the buyer is engaged, and then everything stalls while the proposal is being created. Proposals and tailored emails often mean copying an old deck, hunting for the latest pricing, rewriting benefits, and double-checking legal wording. By the time the proposal is ready, the buyer’s urgency has dropped and your team has burned hours on manual work instead of selling.

Traditional approaches to proposal creation no longer keep up with today’s buying cycles. Static templates in shared drives, manual copy-paste from CRM, and individual reps maintaining their own “version” of the pitch all create friction. Even when you try to standardize with rigid templates, salespeople still need to manually adapt scope, pricing options, customer language, and next steps. The result: slow turnaround times, inconsistent quality, and a process that doesn’t scale as pipeline grows.

The business impact is significant. Every extra day to send a proposal increases the risk that competitors get in first, stakeholders lose momentum, or priorities shift. Slow proposal creation leads to lost opportunities, lower win rates, and higher customer acquisition costs. It also locks expensive sales talent into low-value admin tasks — summarizing emails, updating slides, retyping the same paragraphs — instead of running discovery, negotiation, and expansion conversations.

The good news: this is a solvable problem. With the right use of AI copilots in sales, proposals don’t need to be handcrafted from scratch every time. Gemini’s deep integration with Google Workspace makes it possible to generate proposals directly in Docs and Slides from CRM data or deal briefs, while still leaving room for human judgment where it matters. At Reruption, we’ve seen how targeted AI automations can remove entire layers of manual work; in the rest of this page you’ll find concrete, non-theoretical guidance on how to do the same for your sales proposals.

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Our Assessment

A strategic assessment of the challenge and high-level tips how to tackle it.

From Reruption’s work building AI-first internal tools and copilots for commercial teams, we’ve seen that the real value of Gemini for sales proposal automation is not just faster drafting — it’s turning fragmented sales knowledge into a repeatable, adaptable system. Because Gemini sits directly in Google Docs, Slides, Gmail, and Sheets, you can connect it to your CRM data and deal notes to generate tailored proposals that stay on-brand, on-price, and on-message without creating yet another standalone tool for reps to learn.

Think in Systems, Not One-Off Prompts

The biggest mistake with AI for sales proposals is treating Gemini as a clever typing assistant instead of a component in a broader proposal system. If each rep invents their own prompts and workflows, you’ll end up with inconsistent messaging and hidden compliance risks. Instead, design a system where your core narrative, pricing logic, and legal constraints are captured once and reused across every proposal.

At a strategic level, that means defining what should be standardized versus what should stay flexible. Standardize your value propositions, proof points, and structural elements of proposals; keep discovery insights, customer language, and recommended next steps flexible. Gemini then becomes the engine that pulls these pieces together consistently, instead of a tool for ad-hoc text generation.

Start with a Narrow, High-Impact Proposal Use Case

Trying to automate every proposal scenario at once is a recipe for stalled projects. Choose a specific, repeatable deal type — for example, mid-market new business deals in a core product line — and design your first Gemini-powered proposal flow around it. This gives you clear boundaries: which data points to pull, which sections to generate, and what “good” looks like.

By focusing on one slice of the funnel, you can prove value quickly (e.g. cutting time-to-proposal by 40–60%) and gather feedback from a contained group of sellers. Once that works, you can expand to renewals, upsell motions, and more complex enterprise deals, informed by real usage data instead of assumptions.

Design Around the Sales Team’s Daily Tools and Habits

AI initiatives fail when they force reps to change their working environment. The advantage of Gemini in Google Workspace is that it lives where sellers already write emails, take notes, and create decks. Strategically, your goal is to remove steps from their current flow, not add new ones. For instance, a rep should be able to turn a discovery call summary into a first-draft proposal without leaving Docs or Gmail.

Map the current proposal workflow step by step: from discovery notes and email threads, to internal approvals, to the final PDF. Then identify the specific friction points where Gemini can take over (summarizing requirements, generating pricing options text, drafting next steps), while preserving the checkpoints that require human review. This alignment with existing behaviors is key to adoption and sustained productivity gains.

Set Clear Guardrails for Pricing, Compliance, and Brand Voice

Strategic use of AI in sales demands strong guardrails. Proposal content touches pricing, contractual language, and claims that may have legal implications. Before scaling Gemini usage, define what the model is allowed to generate freely and what must be sourced from controlled templates or human review. For example, free-form value narratives might be fine, while specific commercial terms must come from a pre-approved library.

Document these boundaries in internal guidelines and embed them into your Gemini prompts and templates. Encourage sales, legal, and brand teams to collaborate on a shared set of “building blocks” — approved benefit statements, reference architectures, and objection-handling language — that Gemini can reuse. This reduces risk while still giving reps the speed they need.

Prepare the Organization for Data-Driven Continuous Improvement

Implementing Gemini for sales proposal creation is not a one-time rollout; it’s the start of a continuous optimization loop. Strategically, you should plan from day one how you will capture data on proposal cycle time, win rates, and content patterns. Link these metrics back to specific Gemini workflows so you can see what actually moves the needle.

Make this a cross-functional initiative: sales ops tracks the KPIs, revenue leadership defines success thresholds, and enablement collects qualitative feedback from reps. Treat Gemini as a product inside your sales organization — something you iterate on based on performance and user feedback — rather than a static IT project that is “done” once deployed.

Used strategically, Gemini in Google Workspace can turn slow, manual proposal drafting into a fast, data-driven process that still reflects your unique value and guardrails. The teams that benefit most are those that treat Gemini as part of a designed sales system, not a novelty add-on. Reruption brings both the AI engineering depth and the commercial pragmatism to help you define that system, pilot it safely, and scale what works — if you’re exploring how to fix slow proposal creation with Gemini, we’re happy to pressure-test your ideas and turn them into a concrete plan.

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Real-World Case Studies

From E-commerce to Investment Banking: Learn how companies successfully use Gemini.

Forever 21

E-commerce

Forever 21, a leading fast-fashion retailer, faced significant hurdles in online product discovery. Customers struggled with text-based searches that couldn't capture subtle visual details like fabric textures, color variations, or exact styles amid a vast catalog of millions of SKUs. This led to high bounce rates exceeding 50% on search pages and frustrated shoppers abandoning carts. The fashion industry's visual-centric nature amplified these issues. Descriptive keywords often mismatched inventory due to subjective terms (e.g., 'boho dress' vs. specific patterns), resulting in poor user experiences and lost sales opportunities. Pre-AI, Forever 21's search relied on basic keyword matching, limiting personalization and efficiency in a competitive e-commerce landscape. Implementation challenges included scaling for high-traffic mobile users and handling diverse image inputs like user photos or screenshots.

Lösung

To address this, Forever 21 deployed an AI-powered visual search feature across its app and website, enabling users to upload images for similar item matching. Leveraging computer vision techniques, the system extracts features using pre-trained CNN models like VGG16, computes embeddings, and ranks products via cosine similarity or Euclidean distance metrics. The solution integrated seamlessly with existing infrastructure, processing queries in real-time. Forever 21 likely partnered with providers like ViSenze or built in-house, training on proprietary catalog data for fashion-specific accuracy. This overcame text limitations by focusing on visual semantics, supporting features like style, color, and pattern matching. Overcoming challenges involved fine-tuning models for diverse lighting/user images and A/B testing for UX optimization.

Ergebnisse

  • 25% increase in conversion rates from visual searches
  • 35% reduction in average search time
  • 40% higher engagement (pages per session)
  • 18% growth in average order value
  • 92% matching accuracy for similar items
  • 50% decrease in bounce rate on search pages
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Mayo Clinic

Healthcare

As a leading academic medical center, Mayo Clinic manages millions of patient records annually, but early detection of heart failure remains elusive. Traditional echocardiography detects low left ventricular ejection fraction (LVEF <50%) only when symptomatic, missing asymptomatic cases that account for up to 50% of heart failure risks. Clinicians struggle with vast unstructured data, slowing retrieval of patient-specific insights and delaying decisions in high-stakes cardiology. Additionally, workforce shortages and rising costs exacerbate challenges, with cardiovascular diseases causing 17.9M deaths yearly globally. Manual ECG interpretation misses subtle patterns predictive of low EF, and sifting through electronic health records (EHRs) takes hours, hindering personalized medicine. Mayo needed scalable AI to transform reactive care into proactive prediction.

Lösung

Mayo Clinic deployed a deep learning ECG algorithm trained on over 1 million ECGs, identifying low LVEF from routine 10-second traces with high accuracy. This ML model extracts features invisible to humans, validated internally and externally. In parallel, a generative AI search tool via Google Cloud partnership accelerates EHR queries. Launched in 2023, it uses large language models (LLMs) for natural language searches, surfacing clinical insights instantly. Integrated into Mayo Clinic Platform, it supports 200+ AI initiatives. These solutions overcome data silos through federated learning and secure cloud infrastructure.

Ergebnisse

  • ECG AI AUC: 0.93 (internal), 0.92 (external validation)
  • Low EF detection sensitivity: 82% at 90% specificity
  • Asymptomatic low EF identified: 1.5% prevalence in screened population
  • GenAI search speed: 40% reduction in query time for clinicians
  • Model trained on: 1.1M ECGs from 44K patients
  • Deployment reach: Integrated in Mayo cardiology workflows since 2021
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Rapid Flow Technologies (Surtrac)

Transportation

Pittsburgh's East Liberty neighborhood faced severe urban traffic congestion, with fixed-time traffic signals causing long waits and inefficient flow. Traditional systems operated on preset schedules, ignoring real-time variations like peak hours or accidents, leading to 25-40% excess travel time and higher emissions. The city's irregular grid and unpredictable traffic patterns amplified issues, frustrating drivers and hindering economic activity. City officials sought a scalable solution beyond costly infrastructure overhauls. Sensors existed but lacked intelligent processing; data silos prevented coordination across intersections, resulting in wave-like backups. Emissions rose with idling vehicles, conflicting with sustainability goals.

Lösung

Rapid Flow Technologies developed Surtrac, a decentralized AI system using machine learning for real-time traffic prediction and signal optimization. Connected sensors detect vehicles, feeding data into ML models that forecast flows seconds ahead, adjusting greens dynamically. Unlike centralized systems, Surtrac's peer-to-peer coordination lets intersections 'talk,' prioritizing platoons for smoother progression. This optimization engine balances equity and efficiency, adapting every cycle. Spun from Carnegie Mellon, it integrated seamlessly with existing hardware.

Ergebnisse

  • 25% reduction in travel times
  • 40% decrease in wait/idle times
  • 21% cut in emissions
  • 16% improvement in progression
  • 50% more vehicles per hour in some corridors
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Wells Fargo

Banking

Wells Fargo, serving 70 million customers across 35 countries, faced intense demand for 24/7 customer service in its mobile banking app, where users needed instant support for transactions like transfers and bill payments. Traditional systems struggled with high interaction volumes, long wait times, and the need for rapid responses via voice and text, especially as customer expectations shifted toward seamless digital experiences. Regulatory pressures in banking amplified challenges, requiring strict data privacy to prevent PII exposure while scaling AI without human intervention. Additionally, most large banks were stuck in proof-of-concept stages for generative AI, lacking production-ready solutions that balanced innovation with compliance. Wells Fargo needed a virtual assistant capable of handling complex queries autonomously, providing spending insights, and continuously improving without compromising security or efficiency.

Lösung

Wells Fargo developed Fargo, a generative AI virtual assistant integrated into its banking app, leveraging Google Cloud AI including Dialogflow for conversational flow and PaLM 2/Flash 2.0 LLMs for natural language understanding. This model-agnostic architecture enabled privacy-forward orchestration, routing queries without sending PII to external models. Launched in March 2023 after a 2022 announcement, Fargo supports voice/text interactions for tasks like transfers, bill pay, and spending analysis. Continuous updates added AI-driven insights, agentic capabilities via Google Agentspace, ensuring zero human handoffs and scalability for regulated industries. The approach overcame challenges by focusing on secure, efficient AI deployment.

Ergebnisse

  • 245 million interactions in 2024
  • 20 million interactions by Jan 2024 since March 2023 launch
  • Projected 100 million interactions annually (2024 forecast)
  • Zero human handoffs across all interactions
  • Zero PII exposed to LLMs
  • Average 2.7 interactions per user session
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NatWest

Banking

NatWest Group, a leading UK bank serving over 19 million customers, grappled with escalating demands for digital customer service. Traditional systems like the original Cora chatbot handled routine queries effectively but struggled with complex, nuanced interactions, often escalating 80-90% of cases to human agents. This led to delays, higher operational costs, and risks to customer satisfaction amid rising expectations for instant, personalized support . Simultaneously, the surge in financial fraud posed a critical threat, requiring seamless fraud reporting and detection within chat interfaces without compromising security or user trust. Regulatory compliance, data privacy under UK GDPR, and ethical AI deployment added layers of complexity, as the bank aimed to scale support while minimizing errors in high-stakes banking scenarios . Balancing innovation with reliability was paramount; poor AI performance could erode trust in a sector where customer satisfaction directly impacts retention and revenue .

Lösung

Cora+, launched in June 2024, marked NatWest's first major upgrade using generative AI to enable proactive, intuitive responses for complex queries, reducing escalations and enhancing self-service . This built on Cora's established platform, which already managed millions of interactions monthly. In a pioneering move, NatWest partnered with OpenAI in March 2025—becoming the first UK-headquartered bank to do so—integrating LLMs into both customer-facing Cora and internal tool Ask Archie. This allowed natural language processing for fraud reports, personalized advice, and process simplification while embedding safeguards for compliance and bias mitigation . The approach emphasized ethical AI, with rigorous testing, human oversight, and continuous monitoring to ensure safe, accurate interactions in fraud detection and service delivery .

Ergebnisse

  • 150% increase in Cora customer satisfaction scores (2024)
  • Proactive resolution of complex queries without human intervention
  • First UK bank OpenAI partnership, accelerating AI adoption
  • Enhanced fraud detection via real-time chat analysis
  • Millions of monthly interactions handled autonomously
  • Significant reduction in agent escalation rates
Read case study →

Best Practices

Successful implementations follow proven patterns. Have a look at our tactical advice to get started.

Turn Discovery Notes into Structured Deal Briefs for Gemini

Gemini performs best when it receives structured, complete context about the opportunity. Instead of feeding it raw bullet points, define a consistent deal brief format that reps or your call summarization tool can fill in after each discovery call. Store this in a Google Doc or Sheet linked from the CRM opportunity.

Example deal brief structure for Gemini input:

Customer: <Company name>
Industry: <Industry>
Stakeholders: <Names, roles, priorities>
Current situation: <Short description of how they work today>
Pain points: <Top 3 pains in their own words>
Desired outcomes: <What success looks like>
Products/services in scope: <List>
Constraints: <Budget, timing, technical, legal>
Next meeting: <Date, goal>

Once this is in place, you can use Gemini in Docs to generate targeted proposal sections. The consistency of the brief is what enables you to standardize prompts and avoid “hallucinated” or irrelevant content.

Use Gemini in Docs to Generate First-Draft Proposals from Templates

Start by creating a master Google Docs template that reflects your ideal proposal structure: executive summary, customer situation, proposed solution, pricing options, implementation plan, and next steps. Mark the sections that should be generated dynamically by Gemini versus sections that stay static (e.g. legal boilerplate).

Example Gemini prompt in Google Docs:

"You are a senior B2B sales consultant. Using the deal brief below and the proposal template above, generate a tailored proposal draft.

Focus on:
- Mirroring the customer's language about their pain points
- Selecting only the most relevant product capabilities
- Proposing 2-3 pricing/package options with clear trade-offs
- Ending with clear next steps tailored to their buying process

Deal brief:
<Paste structured deal brief here>"

Reps can run this directly inside Docs, then spend their time refining and validating rather than writing from scratch. This alone typically cuts first-draft creation time from hours to minutes.

Automate Proposal Slide Creation with Gemini in Google Slides

Many buyers still expect a slide deck alongside the written proposal. Use a standardized Slides template with placeholders for problem, vision, solution, ROI, and roadmap. Gemini can then transform your written proposal or deal brief into a sales-ready slide deck aligned with your brand.

Example Gemini prompt in Slides:

"Convert the following proposal summary into a concise 10-slide sales presentation.

Rules:
- Slide 1: Customer situation & urgency
- Slides 2-3: Problem and impact using the customer's own words
- Slides 4-6: Proposed solution and key differentiators
- Slide 7: Implementation plan (phases, timeline)
- Slide 8: Pricing options (high level, no detailed numbers)
- Slide 9: Expected outcomes & ROI drivers
- Slide 10: Clear next steps

Proposal summary:
<Paste executive summary from the Doc>"

This keeps messaging consistent across formats and removes yet another manual translation task from your sales process.

Use Gemini in Gmail to Draft Follow-Up and Handover Emails

Gemini’s email capabilities can substantially reduce the time reps spend on follow-ups while increasing quality. After a proposal is sent, reps can use Gemini in Gmail to draft a personalized follow-up, referencing specific concerns and next steps from the email thread and call notes.

Example Gemini prompt in Gmail:

"Draft a concise follow-up email to <Contact Name> regarding the proposal I just sent.

Goals:
- Acknowledge their key challenges as discussed
- Briefly restate the main outcome of our proposal in their language
- Suggest 2 specific time slots for a decision-making call
- Keep it under 180 words, clear and professional, no hype

Use the email thread below and the attached proposal for context."

You can take this further by having Gemini generate internal handover emails to pre-sales or delivery teams, summarizing scope, risks, and expectations directly from the proposal document.

Connect CRM Data to Google Workspace for Auto-Filled Sections

Much of the repetitive work in proposals is filling in factual data: company name, region, product SKUs, high-level pricing ranges, contract lengths. Use your CRM’s integration with Google Workspace (or simple exports) so that Gemini can reference accurate, up-to-date data instead of relying on manual input.

For example, create a Google Sheet synced with your CRM that contains product descriptions, standard packages, reference architectures, and list prices. In your Gemini prompts, instruct the model to use only this sheet when describing products or listing components.

Prompt snippet for controlled product descriptions:

"When describing products or services, use only the information from the 'Product Catalog' Google Sheet linked here. Do not invent features or technical details. If information is missing, leave a clearly marked placeholder instead of guessing."

This combination — Gemini plus a curated data source — gives you speed without losing control over facts and pricing frameworks.

Define KPIs and Build a Simple Feedback Loop into Every Proposal

To move beyond experimentation, track concrete metrics for your Gemini-powered proposal workflow. At minimum, measure: time from opportunity qualification to proposal sent, number of proposal iterations per deal, and win rate for deals using AI-generated first drafts versus the old process.

Add a short internal feedback block at the end of each proposal document for the rep to fill in after sending: what worked, what didn’t, and which sections required the most manual editing. This can be as simple as a three-question form embedded in the template. Periodically review this with sales ops and adjust prompts, templates, and guardrails accordingly.

Expected outcomes for teams that implement these practices in a focused scope are realistic but meaningful: 40–60% reduction in time-to-first-draft, 20–30% fewer back-and-forth edits per proposal, and more consistent messaging across the team — all without increasing headcount.

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Frequently Asked Questions

Gemini accelerates proposal creation by working inside the tools your sales team already uses — Google Docs, Slides, and Gmail. Instead of starting from a blank page, reps feed Gemini a structured deal brief or call summary, and it generates a tailored proposal draft based on your templates and messaging guidelines.

Concretely, Gemini can summarize long email threads into a clear requirements section, adapt standardized value propositions to the customer’s language, propose structured pricing option descriptions, and draft follow-up emails. Reps then review and refine, turning hours of manual copy-paste work into minutes of targeted editing.

You don’t need a large data science team to start using Gemini for sales proposals, but you do need a few key roles. Someone from sales or sales operations who understands the current proposal process, someone from enablement or marketing who owns messaging and templates, and a technically minded person who can configure integrations between your CRM and Google Workspace.

From a skills perspective, focus on: designing clear prompts, organizing deal information into consistent briefs, and defining guardrails for pricing and legal content. Reruption typically helps clients set up this foundation, so that internal teams can own and evolve the system without heavy external dependency.

For a focused use case (e.g. one core product line and a defined deal type), you can see tangible results in 4–8 weeks. In the first 1–2 weeks, you define the target workflow, templates, and guardrails. The next 2–3 weeks are used to build and test Gemini prompts in Docs, Slides, and Gmail with a small group of reps.

By week 4, most teams have a working flow that reduces first-draft proposal time significantly. Over the following weeks, you refine prompts and templates based on real deals and begin tracking KPIs like time-to-proposal and win rate. Reruption’s AI PoC format is specifically designed to compress this timeline and prove whether the approach works in your environment before you scale.

ROI primarily comes from two levers: reduced manual effort and improved deal velocity. When reps spend less time drafting and formatting, they can handle more opportunities or invest more time in higher-value activities like discovery and negotiation. Cutting proposal creation time by 40–60% effectively increases your selling capacity without increasing headcount.

On the revenue side, faster and more consistent proposals help you respond within the buyer’s window of attention, which tends to improve conversion. While exact numbers depend on your context, many organizations see enough efficiency gains within a few months to comfortably justify the investment in configuration and training. The key is to define clear baseline metrics before you start, so you can attribute improvements to the new Gemini workflows.

Reruption works as a Co-Preneur inside your organization: we don’t just advise on AI, we build and ship working solutions with your team. For slow proposal creation, we typically start with our AI PoC offering (9,900€), where we define a concrete proposal use case, prototype a Gemini-powered workflow in Google Workspace, and validate that it actually reduces time and maintains quality on real deals.

From there, we help you harden the solution: refining prompts and templates, connecting to your CRM, setting up security and compliance guardrails, and enabling your sales team to adopt the new process. Because we operate with entrepreneurial ownership and technical depth, our goal is not to create slide decks, but to leave you with a functioning, measurable system that your teams can run and evolve.

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