The Challenge: Slow Draft Creation

Marketing teams are under pressure to ship campaigns faster, across more channels, with tighter resources. Yet a huge amount of time is still lost on slow draft creation: turning briefs, product sheets, and stakeholder inputs into first drafts for blogs, email sequences, landing pages, social posts, and ads. What should be a fast translation of intent into words often becomes a multi-day task.

Traditional approaches depend heavily on individual copywriters and manual processes. Marketers start from a blank page, copy-paste information from different sources, search for past assets to reuse, and manually adapt tone and messaging for each channel. Style guides and brand books live in separate documents, so consistency relies on memory and vigilance. As content volume grows, these methods simply do not scale.

The business impact is real. Campaign launches slip because initial drafts are not ready. Performance experiments are delayed because it is too slow to generate enough variants to A/B test. Strategy work gets squeezed out by operational writing tasks. Bottlenecks appear whenever a single copywriter is overloaded, and other teams are left waiting for “the first draft” to move design, approvals, and execution forward. Over time, this leads to missed revenue opportunities, under-tested campaigns, and a competitive disadvantage against brands that have already industrialised content production.

The good news: this bottleneck is solvable. Generative AI tools like Gemini, especially when integrated directly in Google Docs, Slides, and Gmail, can turn structured inputs into high-quality first drafts within minutes. At Reruption, we have seen how the right AI workflows free marketers from repetitive drafting so they can focus on strategy, creative direction, and performance optimisation. In the rest of this page, you will find practical guidance on how to use Gemini to fix slow draft creation in a way that actually works in a real marketing organisation.

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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 workflows inside organisations, we know that the real value of Gemini for marketing is not in writing entire campaigns autonomously, but in compressing the painful “blank-page” phase into minutes. When you treat Gemini as a structured collaborator embedded in Google Workspace, you can standardise how first drafts are created, ensure brand consistency, and remove the copy bottleneck without losing human control over messaging and positioning.

Clarify Where Gemini Should Help in Your Content Workflow

Before turning on any AI tool, define precisely where slow draft creation is hurting you most: is it long-form blog posts, first versions of landing pages, email flows, or social/ad variants? Mapping the end-to-end content workflow—from brief to published asset—helps you see where Gemini can create leverage and where human expertise must stay in the lead.

For most marketing teams, the highest value is using Gemini to own the first 60–70% of the draft, then letting marketers focus on refinement, positioning, and compliance. Rushing to “AI everything” without this clarity leads to mismatched expectations and frustration. Strategically, the goal is to redesign the workflow so that humans steer the message and AI does the heavy lifting on structure, volume, and adaptation.

Treat Brand Voice as a System, Not a Preference

Gemini will only produce consistent marketing drafts if your brand voice is explicit, documented, and fed into the system. Many teams rely on implicit knowledge—what the senior copywriter “just knows”. That does not translate into scalable AI usage.

From a strategic perspective, invest time in translating your brand into clear rules: tone descriptors, phrasing do’s and don’ts, examples of “great” vs. “off-brand” copy. Then standardise how these rules are used with Gemini prompts across the team. This turns voice from a personal preference into a reusable asset that every marketer—and Gemini—can apply to drafts, across blogs, ads, emails, and landing pages.

Design for Collaboration Between Humans and Gemini, Not Substitution

Using Gemini for content creation works best when it augments marketers instead of trying to replace them. Strategically, your teams need to understand that Gemini is there to speed up thinking and drafting, while humans remain responsible for narrative, positioning, and compliance.

Set clear expectations: Gemini drafts are starting points, not final copy. Encourage a workflow where marketers provide structured inputs (briefs, key messages, objections to address) and then iterate with Gemini instead of rewriting from scratch. This keeps accountability clear and protects quality while still compressing timelines for first drafts dramatically.

Prepare Data and Assets So Gemini Has Something Smart to Work With

Even the best AI will produce generic output if you feed it generic inputs. Strategically, part of becoming an AI-ready marketing team is curating the right product data, messaging frameworks, personas, and past high-performing campaigns so Gemini can use them as context.

Decide which sources are “authoritative” for Gemini: product sheets, FAQ docs, persona descriptions, USP matrices, and performance data about winning campaigns. Then design standard ways to reference or paste these into Gemini prompts in Docs or Gmail. This moves you from random experimentation to a repeatable, high-quality drafting system.

Manage Risks Around Compliance, Bias, and Confidentiality

When scaling AI-assisted content creation, you need a deliberate view on risk. Marketing copy often touches regulated topics, sensitive claims, and brand-sensitive messaging. A purely ad-hoc use of Gemini can create legal or reputational exposure if not governed.

Define which content categories are allowed for AI-generated drafts (e.g. educational blog content, non-regulated product marketing) and which require extra review. Create lightweight review steps for fact-checking and legal-sensitive statements. Work with IT and security to configure access and usage policies for Gemini inside Google Workspace so that confidential information is handled correctly. This protects the organisation while still letting teams benefit from faster draft creation.

Used with the right workflow and guardrails, Gemini can remove the slow-draft bottleneck in marketing by turning structured inputs into high-quality first versions inside the tools you already use. The real unlock is combining Gemini with clear brand voice rules, curated product data, and a human-in-the-loop review process. Reruption specialises in building exactly these AI-first workflows inside organisations—if you want help turning Gemini from an experiment into a reliable content engine, we are ready to design, prototype, and implement it with you.

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Real-World Case Studies

From Automotive to Manufacturing: Learn how companies successfully use Gemini.

Tesla, Inc.

Automotive

The automotive industry faces a staggering 94% of traffic accidents attributed to human error, including distraction, fatigue, and poor judgment, resulting in over 1.3 million global road deaths annually. In the US alone, NHTSA data shows an average of one crash per 670,000 miles driven, highlighting the urgent need for advanced driver assistance systems (ADAS) to enhance safety and reduce fatalities. Tesla encountered specific hurdles in scaling vision-only autonomy, ditching radar and lidar for camera-based systems reliant on AI to mimic human perception. Challenges included variable AI performance in diverse conditions like fog, night, or construction zones, regulatory scrutiny over misleading Level 2 labeling despite Level 4-like demos, and ensuring robust driver monitoring to prevent over-reliance. Past incidents and studies criticized inconsistent computer vision reliability.

Lösung

Tesla's Autopilot and Full Self-Driving (FSD) Supervised leverage end-to-end deep learning neural networks trained on billions of real-world miles, processing camera feeds for perception, prediction, and control without modular rules. Transitioning from HydraNet (multi-task learning for 30+ outputs) to pure end-to-end models, FSD v14 achieves door-to-door driving via video-based imitation learning. Overcoming challenges, Tesla scaled data collection from its fleet of 6M+ vehicles, using Dojo supercomputers for training on petabytes of video. Vision-only approach cuts costs vs. lidar rivals, with recent upgrades like new cameras addressing edge cases. Regulatory pushes target unsupervised FSD by end-2025, with China approval eyed for 2026.

Ergebnisse

  • Autopilot Crash Rate: 1 per 6.36M miles (Q3 2025)
  • Safety Multiple: 9x safer than US average (670K miles/crash)
  • Fleet Data: Billions of miles for training
  • FSD v14: Door-to-door autonomy achieved
  • Q2 2025: 1 crash per 6.69M miles
  • 2024 Q4 Record: 5.94M miles between accidents
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John Deere

Agriculture

In conventional agriculture, farmers rely on blanket spraying of herbicides across entire fields, leading to significant waste. This approach applies chemicals indiscriminately to crops and weeds alike, resulting in high costs for inputs—herbicides can account for 10-20% of variable farming expenses—and environmental harm through soil contamination, water runoff, and accelerated weed resistance . Globally, weeds cause up to 34% yield losses, but overuse of herbicides exacerbates resistance in over 500 species, threatening food security . For row crops like cotton, corn, and soybeans, distinguishing weeds from crops is particularly challenging due to visual similarities, varying field conditions (light, dust, speed), and the need for real-time decisions at 15 mph spraying speeds. Labor shortages and rising chemical prices in 2025 further pressured farmers, with U.S. herbicide costs exceeding $6B annually . Traditional methods failed to balance efficacy, cost, and sustainability.

Lösung

See & Spray revolutionizes weed control by integrating high-resolution cameras, AI-powered computer vision, and precision nozzles on sprayers. The system captures images every few inches, uses object detection models to identify weeds (over 77 species) versus crops in milliseconds, and activates sprays only on targets—reducing blanket application . John Deere acquired Blue River Technology in 2017 to accelerate development, training models on millions of annotated images for robust performance across conditions. Available in Premium (high-density) and Select (affordable retrofit) versions, it integrates with existing John Deere equipment via edge computing for real-time inference without cloud dependency . This robotic precision minimizes drift and overlap, aligning with sustainability goals.

Ergebnisse

  • 5 million acres treated in 2025
  • 31 million gallons of herbicide mix saved
  • Nearly 50% reduction in non-residual herbicide use
  • 77+ weed species detected accurately
  • Up to 90% less chemical in clean crop areas
  • ROI within 1-2 seasons for adopters
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Duolingo

EdTech

Duolingo, a leader in gamified language learning, faced key limitations in providing real-world conversational practice and in-depth feedback. While its bite-sized lessons built vocabulary and basics effectively, users craved immersive dialogues simulating everyday scenarios, which static exercises couldn't deliver . This gap hindered progression to fluency, as learners lacked opportunities for free-form speaking and nuanced grammar explanations without expensive human tutors. Additionally, content creation was a bottleneck. Human experts manually crafted lessons, slowing the rollout of new courses and languages amid rapid user growth. Scaling personalized experiences across 40+ languages demanded innovation to maintain engagement without proportional resource increases . These challenges risked user churn and limited monetization in a competitive EdTech market.

Lösung

Duolingo launched Duolingo Max in March 2023, a premium subscription powered by GPT-4, introducing Roleplay for dynamic conversations and Explain My Answer for contextual feedback . Roleplay simulates real-life interactions like ordering coffee or planning vacations with AI characters, adapting in real-time to user inputs. Explain My Answer provides detailed breakdowns of correct/incorrect responses, enhancing comprehension. Complementing this, Duolingo's Birdbrain LLM (fine-tuned on proprietary data) automates lesson generation, allowing experts to create content 10x faster . This hybrid human-AI approach ensured quality while scaling rapidly, integrated seamlessly into the app for all skill levels .

Ergebnisse

  • DAU Growth: +59% YoY to 34.1M (Q2 2024)
  • DAU Growth: +54% YoY to 31.4M (Q1 2024)
  • Revenue Growth: +41% YoY to $178.3M (Q2 2024)
  • Adjusted EBITDA Margin: 27.0% (Q2 2024)
  • Lesson Creation Speed: 10x faster with AI
  • User Self-Efficacy: Significant increase post-AI use (2025 study)
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bunq

Banking

As bunq experienced rapid growth as the second-largest neobank in Europe, scaling customer support became a critical challenge. With millions of users demanding personalized banking information on accounts, spending patterns, and financial advice on demand, the company faced pressure to deliver instant responses without proportionally expanding its human support teams, which would increase costs and slow operations. Traditional search functions in the app were insufficient for complex, contextual queries, leading to inefficiencies and user frustration. Additionally, ensuring data privacy and accuracy in a highly regulated fintech environment posed risks. bunq needed a solution that could handle nuanced conversations while complying with EU banking regulations, avoiding hallucinations common in early GenAI models, and integrating seamlessly without disrupting app performance. The goal was to offload routine inquiries, allowing human agents to focus on high-value issues.

Lösung

bunq addressed these challenges by developing Finn, a proprietary GenAI platform integrated directly into its mobile app, replacing the traditional search function with a conversational AI chatbot. After hiring over a dozen data specialists in the prior year, the team built Finn to query user-specific financial data securely, answer questions on balances, transactions, budgets, and even provide general advice while remembering conversation context across sessions. Launched as Europe's first AI-powered bank assistant in December 2023 following a beta, Finn evolved rapidly. By May 2024, it became fully conversational, enabling natural back-and-forth interactions. This retrieval-augmented generation (RAG) approach grounded responses in real-time user data, minimizing errors and enhancing personalization.

Ergebnisse

  • 100,000+ questions answered within months post-beta (end-2023)
  • 40% of user queries fully resolved autonomously by mid-2024
  • 35% of queries assisted, totaling 75% immediate support coverage
  • Hired 12+ data specialists pre-launch for data infrastructure
  • Second-largest neobank in Europe by user base (1M+ users)
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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.

Standardise a Gemini Brief Template in Google Docs

To avoid random results, give every Gemini draft the same structured inputs. Create a Google Docs template that marketers fill out before asking Gemini to generate copy. This keeps outputs consistent and makes it easier for new team members to get good results quickly.

Your template might include: target persona, stage in funnel, primary message, supporting proof points, tone guidelines, CTAs, and links or pasted text for product details. Once this is filled, you can invoke Gemini directly in Docs to turn the brief into a first draft.

Example prompt inside Google Docs:

You are an expert B2B marketing copywriter.
Use the brief below to draft a first version of a landing page.

Brief:
- Audience: <paste from template>
- Problem we solve: <paste from template>
- Product details: <paste from template or product sheet>
- Tone of voice: <brand tone rules>
- CTA: <desired action>

Structure the page with:
- Hero section (headline, subheadline, CTA)
- 3 benefit sections with supporting bullets
- Social proof section
- Final CTA section.

Expected outcome: first landing-page drafts created in minutes instead of hours, with all key information already included and structured.

Create a Reusable Brand Voice Prompt “Block”

Gemini can mimic your brand voice reliably if you provide a stable description plus examples. Turn this into a reusable block that marketers paste into any prompt when generating blogs, emails, or ads.

First, document 3–5 short examples of “perfect” brand copy from past campaigns, plus rules on tone, sentence length, jargon, and words to avoid. Then embed that into a prompt segment like the one below and encourage everyone to keep it updated as the brand evolves.

Brand voice block to append to prompts:

Follow this brand voice:
- Tone: <e.g. clear, confident, helpful, no hype>
- We say: <preferred phrases>
- We never say: <banned phrases>
- Style: short sentences, no buzzwords, focus on outcomes.

Here are 3 examples of on-brand copy. Match their style:
1) <example 1>
2) <example 2>
3) <example 3>

Expected outcome: higher brand consistency across all Gemini-generated drafts and less time spent manually “fixing the tone”.

Use Gemini in Gmail to Accelerate Campaign and Stakeholder Emails

Slow draft creation is not only about public-facing content. Internal and external emails—campaign approvals, partner updates, and nurture sequences—also eat into marketing time. With Gemini in Gmail, you can generate structured email drafts directly where you send them.

When replying to a thread, highlight key details or paste a short bullet-point brief, then ask Gemini to turn it into a clear, on-brand message. For campaign flows, you can draft the entire sequence from a single brief, then refine each email manually.

Example prompt in Gmail:

Turn these bullets into a concise, on-brand campaign update email
for our sales team:

- Campaign: Q3 product launch for <product name>
- Audience: existing customers in <regions>
- Launch date: <date>
- What sales should know: <3-5 bullets>
- Call to action: share launch materials with top 50 accounts.

Use a clear subject line and a scannable structure with bullets.

Expected outcome: faster, clearer communication around campaigns, reducing friction and back-and-forth between teams.

Automate Blog Drafts from Product Docs and Briefs

Blog posts often start from product specifications or dense internal documents. Instead of rewriting everything manually, use Gemini in Google Docs to turn those inputs into structured, reader-friendly posts.

Paste the relevant product documentation into a Doc, then create a short blog brief above it. Ask Gemini to ignore internal details that customers do not need and to focus on benefits, use cases, and practical examples.

Example prompt in Google Docs:

You are a content marketer. Based on the product documentation below,
write a 1,200-word blog post for <target persona>.

Focus on:
- The business problem this product solves
- 3-4 key benefits in plain language
- 2 concrete use cases
- A soft CTA to book a demo or talk to sales.

Avoid internal jargon and implementation details that are not
relevant to the buyer.

Product documentation:
<paste>

Expected outcome: first blog drafts produced 50–70% faster, with marketers spending their time on refining positioning instead of manual summarisation.

Generate and Test Multiple Ad and Social Variants at Once

Advertising and social campaigns suffer when you only have one or two creative variants. Gemini is ideal for quickly generating multiple angles while staying within character-count and platform constraints. This directly addresses the bottleneck where no one has time to manually write 10–20 options per ad set.

In a Google Doc, define your core message, offer, target platform, and any mandatory phrases. Then ask Gemini to output structured sets of variants you can paste into your ad manager or social scheduler.

Example prompt for ad variants:

Create 10 ad copy variants for LinkedIn.

Input:
- Offer: <offer>
- Audience: <persona>
- Key benefit: <benefit>
- Tone: <brand tone>

Constraints:
- Max 150 characters per ad
- Include one of these CTAs: "Learn more", "Get the guide", "Talk to us".

Group the output in a table: Variant #, Copy, Angle (e.g. risk, speed, ROI).

Expected outcome: a richer set of testable variants, driving better campaign performance without adding copywriting headcount.

Define KPIs and Track Time Saved on Draft Creation

To prove the value of Gemini in marketing, measure the impact on your drafting process. Before rollout, run a baseline: track how long it takes to create first drafts for a representative set of assets (e.g. 5 blog posts, 3 landing pages, 10 ads, 5 emails).

After implementing the workflows above, repeat the measurement. Capture both time-to-first-draft and the number of iterations required to reach “publishable”. Combine this with performance metrics from your campaigns (CTR, conversion rate, time-to-launch). Realistic outcomes we see are 40–70% time reduction for first drafts, 20–30% more variants per campaign, and significantly shorter campaign lead times when approvals are not waiting on copy.

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Frequently Asked Questions

Gemini speeds up slow draft creation by turning structured inputs—briefs, product docs, persona descriptions—into solid first versions directly inside Google Docs, Slides, and Gmail. Instead of starting from a blank page, marketers feed Gemini a clear brief and brand voice rules, and receive a structured blog post, landing page, email sequence, or ad variants in minutes.

This does not replace human copywriters; it compresses the manual drafting phase so teams can spend their time on refinement, positioning, and performance optimisation instead of basic wording and structure.

You don’t need a data science team to benefit from Gemini for content creation. The core requirements are: access to Gemini within Google Workspace, marketers who are comfortable working in Docs/Gmail/Slides, and a few well-designed prompt templates.

The main skills are strategic rather than technical: the ability to write clear briefs, define brand voice guidelines, and review AI outputs critically. Reruption typically helps teams by setting up templates, prompt libraries, and simple governance rules so marketers can be productive with Gemini within days.

For most marketing teams, the impact is visible within a few weeks. Once basic workflows are set up (brief templates, brand voice block, example prompts), you can usually reduce time-to-first-draft for common assets by 40–70% almost immediately.

Over 4–8 weeks, as prompts are refined and teams get used to collaborating with Gemini, you can expect smoother approvals, more test variants per campaign, and shorter campaign lead times. The key is to start with a focused pilot—e.g. landing pages and email sequences—measure time savings, and then expand to other content types.

Gemini is typically licensed as part of your Google Workspace environment, so the direct cost per seat is predictable. The more important question is ROI: how much time and opportunity cost do you recover by removing the slow-draft bottleneck?

In practice, reducing drafting time by even 50% across blogs, emails, and landing pages can free up dozens of hours per marketer per month. That time can be reallocated to higher-value activities—campaign strategy, creative experimentation, deeper analysis—which directly impacts revenue. ROI comes from both efficiency (fewer hours spent drafting) and effectiveness (more and better variants, faster testing, fewer delayed launches).

Reruption supports organisations end-to-end in turning Gemini into a reliable content engine. We start with a focused AI PoC (9,900€) to prove that your specific use cases—such as blog drafts, landing pages, or email flows—work in a real prototype, not just on slides. This covers use-case scoping, model and architecture choices, and a working draft-generation workflow inside your existing tools.

With our Co-Preneur approach, we then embed with your team to design prompts, templates, and governance, integrate Gemini cleanly into Google Workspace, and train marketers to use it effectively. The goal is not a one-off workshop, but a sustainable, AI-first content production system that genuinely removes the slow-draft bottleneck.

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