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 Banking to Retail: Learn how companies successfully use Gemini.

JPMorgan Chase

Banking

In the high-stakes world of asset management and wealth management at JPMorgan Chase, advisors faced significant time burdens from manual research, document summarization, and report drafting. Generating investment ideas, market insights, and personalized client reports often took hours or days, limiting time for client interactions and strategic advising. This inefficiency was exacerbated post-ChatGPT, as the bank recognized the need for secure, internal AI to handle vast proprietary data without risking compliance or security breaches. The Private Bank advisors specifically struggled with preparing for client meetings, sifting through research reports, and creating tailored recommendations amid regulatory scrutiny and data silos, hindering productivity and client responsiveness in a competitive landscape.

Lösung

JPMorgan addressed these challenges by developing the LLM Suite, an internal suite of seven fine-tuned large language models (LLMs) powered by generative AI, integrated with secure data infrastructure. This platform enables advisors to draft reports, generate investment ideas, and summarize documents rapidly using proprietary data. A specialized tool, Connect Coach, was created for Private Bank advisors to assist in client preparation, idea generation, and research synthesis. The implementation emphasized governance, risk management, and employee training through AI competitions and 'learn-by-doing' approaches, ensuring safe scaling across the firm. LLM Suite rolled out progressively, starting with proofs-of-concept and expanding firm-wide.

Ergebnisse

  • Users reached: 140,000 employees
  • Use cases developed: 450+ proofs-of-concept
  • Financial upside: Up to $2 billion in AI value
  • Deployment speed: From pilot to 60K users in months
  • Advisor tools: Connect Coach for Private Bank
  • Firm-wide PoCs: Rigorous ROI measurement across 450 initiatives
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NVIDIA

Manufacturing

In semiconductor manufacturing, chip floorplanning—the task of arranging macros and circuitry on a die—is notoriously complex and NP-hard. Even expert engineers spend months iteratively refining layouts to balance power, performance, and area (PPA), navigating trade-offs like wirelength minimization, density constraints, and routability. Traditional tools struggle with the explosive combinatorial search space, especially for modern chips with millions of cells and hundreds of macros, leading to suboptimal designs and delayed time-to-market. NVIDIA faced this acutely while designing high-performance GPUs, where poor floorplans amplify power consumption and hinder AI accelerator efficiency. Manual processes limited scalability for 2.7 million cell designs with 320 macros, risking bottlenecks in their accelerated computing roadmap. Overcoming human-intensive trial-and-error was critical to sustain leadership in AI chips.

Lösung

NVIDIA deployed deep reinforcement learning (DRL) to model floorplanning as a sequential decision process: an agent places macros one-by-one, learning optimal policies via trial and error. Graph neural networks (GNNs) encode the chip as a graph, capturing spatial relationships and predicting placement impacts. The agent uses a policy network trained on benchmarks like MCNC and GSRC, with rewards penalizing half-perimeter wirelength (HPWL), congestion, and overlap. Proximal Policy Optimization (PPO) enables efficient exploration, transferable across designs. This AI-driven approach automates what humans do manually but explores vastly more configurations.

Ergebnisse

  • Design Time: 3 hours for 2.7M cells vs. months manually
  • Chip Scale: 2.7 million cells, 320 macros optimized
  • PPA Improvement: Superior or comparable to human designs
  • Training Efficiency: Under 6 hours total for production layouts
  • Benchmark Success: Outperforms on MCNC/GSRC suites
  • Speedup: 10-30% faster circuits in related RL designs
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Netflix

Streaming Media

With over 17,000 titles and growing, Netflix faced the classic cold start problem and data sparsity in recommendations, where new users or obscure content lacked sufficient interaction data, leading to poor personalization and higher churn rates . Viewers often struggled to discover engaging content among thousands of options, resulting in prolonged browsing times and disengagement—estimated at up to 75% of session time wasted on searching rather than watching . This risked subscriber loss in a competitive streaming market, where retaining users costs far less than acquiring new ones. Scalability was another hurdle: handling 200M+ subscribers generating billions of daily interactions required processing petabytes of data in real-time, while evolving viewer tastes demanded adaptive models beyond traditional collaborative filtering limitations like the popularity bias favoring mainstream hits . Early systems post-Netflix Prize (2006-2009) improved accuracy but struggled with contextual factors like device, time, and mood .

Lösung

Netflix built a hybrid recommendation engine combining collaborative filtering (CF)—starting with FunkSVD and Probabilistic Matrix Factorization from the Netflix Prize—and advanced deep learning models for embeddings and predictions . They consolidated multiple use-case models into a single multi-task neural network, improving performance and maintainability while supporting search, home page, and row recommendations . Key innovations include contextual bandits for exploration-exploitation, A/B testing on thumbnails and metadata, and content-based features from computer vision/audio analysis to mitigate cold starts . Real-time inference on Kubernetes clusters processes 100s of millions of predictions per user session, personalized by viewing history, ratings, pauses, and even search queries . This evolved from 2009 Prize winners to transformer-based architectures by 2023 .

Ergebnisse

  • 80% of viewer hours from recommendations
  • $1B+ annual savings in subscriber retention
  • 75% reduction in content browsing time
  • 10% RMSE improvement from Netflix Prize CF techniques
  • 93% of views from personalized rows
  • Handles billions of daily interactions for 270M subscribers
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Rolls-Royce Holdings

Aerospace

Jet engines are highly complex, operating under extreme conditions with millions of components subject to wear. Airlines faced unexpected failures leading to costly groundings, with unplanned maintenance causing millions in daily losses per aircraft. Traditional scheduled maintenance was inefficient, often resulting in over-maintenance or missed issues, exacerbating downtime and fuel inefficiency. Rolls-Royce needed to predict failures proactively amid vast data from thousands of engines in flight. Challenges included integrating real-time IoT sensor data (hundreds per engine), handling terabytes of telemetry, and ensuring accuracy in predictions to avoid false alarms that could disrupt operations. The aerospace industry's stringent safety regulations added pressure to deliver reliable AI without compromising performance.

Lösung

Rolls-Royce developed the IntelligentEngine platform, combining digital twins—virtual replicas of physical engines—with machine learning models. Sensors stream live data to cloud-based systems, where ML algorithms analyze patterns to predict wear, anomalies, and optimal maintenance windows. Digital twins enable simulation of engine behavior pre- and post-flight, optimizing designs and schedules. Partnerships with Microsoft Azure IoT and Siemens enhanced data processing and VR modeling, scaling AI across Trent series engines like Trent 7000 and 1000. Ethical AI frameworks ensure data security and bias-free predictions.

Ergebnisse

  • 48% increase in time on wing before first removal
  • Doubled Trent 7000 engine time on wing
  • Reduced unplanned downtime by up to 30%
  • Improved fuel efficiency by 1-2% via optimized ops
  • Cut maintenance costs by 20-25% for operators
  • Processed terabytes of real-time data from 1000s of engines
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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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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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