The Challenge: Irrelevant Value Propositions

Most sales teams know they should "speak the customer’s language"—in reality, many emails, call scripts, and proposals still sound generic. Reps repeat the same list of product benefits regardless of whether a prospect cares more about cost savings, faster deployment, compliance, or user adoption. The result: value propositions that feel off-key and fail to connect with what individual stakeholders truly need.

Traditional approaches rely on static messaging frameworks, persona decks, and manual research. A motivated rep might spend half an hour digging through CRM notes, old emails, and LinkedIn profiles to tailor an outreach—then still default to generic pitches under time pressure. As buying committees grow and touchpoints spread across email, meetings, and shared documents, it becomes almost impossible for humans alone to track every signal and adjust the message in real time.

The business impact is significant. Misaligned messaging forces extra clarification calls, prolongs sales cycles, and increases the risk that a competitor articulates the customer’s problem better than you do. Response rates drop because prospects don’t recognize themselves in your pitch. Opportunities become “no decision” because the internal champion doesn’t have a sharp, tailored story to sell you internally. Over time, this shows up as lower win rates, higher customer acquisition costs, and a widening gap to more data-driven competitors.

The good news: this problem is very solvable with the right combination of data and AI. By using tools like Gemini for sales personalization, you can turn scattered interaction history into concrete, role-specific value propositions that resonate with each stakeholder—without asking your reps to become full-time analysts. At Reruption, we’ve helped organisations build AI-powered workflows that sit directly in their existing toolstack, and below we’ll outline practical steps to bring this kind of intelligent personalization into your own sales process.

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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 sales and communication workflows, we’ve seen that Gemini is especially strong when it can tap into both CRM context and Google Workspace interaction data. Instead of treating AI as a fancy text generator, we treat Gemini as a reasoning layer over your emails, docs, sheets, and deal history—so it can infer what each prospect really cares about and suggest value propositions that cut through the noise.

Anchor Gemini in a Clear Value Proposition Framework

Before connecting Gemini to your CRM and Gmail, you need a clear internal map of your own value drivers. For example: cost savings, revenue growth, operational efficiency, compliance, security, user experience, and speed of implementation. Without this shared framework, Gemini will generate nice-sounding messages, but they won’t systematically reinforce the positioning that makes you win deals.

Define 6–10 core value themes, link them to typical buyer roles (CFO, CIO, business owners, end users), and capture proof points and examples for each. Then use this as the backbone for your Gemini prompts and system instructions, so the model learns to translate raw user signals into your standardized, high-impact value stories.

Treat Gemini as a Co-Pilot, Not a Script Engine

The fastest way to fail with AI in sales outreach is to try to fully automate every email or call script. That leads to generic, over-polished content and removes the rep’s judgment from the loop. Instead, think of Gemini as a co-pilot that prepares drafts, highlight reels, and tailored talking points, which the rep then adapts.

This mindset also reduces adoption resistance. Reps keep control of the conversation while Gemini handles the heavy lifting: summarizing prospect activity, surfacing key pain points, and suggesting value propositions and objections that fit the context. Strategically, this positions AI as augmentation, not replacement, which makes it easier to roll out across regions and seniority levels.

Design Around the Sales Workflow, Not Around the Model

A common mistake is to start with "What can Gemini do?" instead of "Where do our reps lose the most time or relevance?" For irrelevant value propositions in sales, the critical moments are often: first outbound touch, post-discovery follow-up, multi-stakeholder alignment emails, and proposal introductions.

Map these key moments and then design where Gemini shows up: a sidebar inside Gmail suggesting tailored intros, a Chrome extension summarizing last interactions, or a document template that auto-fills value arguments based on CRM fields. By embedding Gemini into existing tools like Gmail, Docs, and your CRM, you minimize change management and ensure the AI actually gets used.

Invest Early in Data Quality and Labeling

Gemini can only personalize against what it sees. If your CRM doesn’t distinguish between cost-driven and innovation-driven deals, or if "industry" and "role" fields are inconsistent, the model will struggle to infer the right value proposition. Strategically, this means that improving sales data hygiene is not an admin exercise, but a prerequisite for high-quality personalization.

Start by standardizing a small set of fields that matter most for value alignment: industry, role, primary objective (e.g., save costs/grow revenue/improve reliability), and buying stage. Then make it effortless for reps to keep this up to date—ideally by having Gemini propose values based on email and call notes that the rep only confirms. This turns data quality into a byproduct of the workflow, not a separate chore.

Define Guardrails to Protect Brand and Compliance

When you scale AI-generated outreach with Gemini, you also scale risk if you don’t define boundaries. Strategic guardrails should cover what Gemini is allowed to promise (e.g., no specific ROI percentages without references), how it addresses competitors, and which regulated topics require manual review.

Implement system-level instructions and approval flows for sensitive segments or geographies. For example, require manager review for first-touch emails to strategic accounts, or restrict certain phrasing in industries with strict compliance rules. These controls let you benefit from Gemini’s personalization power while maintaining brand consistency and legal safety.

Used deliberately, Gemini can turn scattered sales data into tailored, role-specific value propositions that match each buyer’s priorities and move deals forward faster. The real leverage comes from combining clean CRM signals, clear value frameworks, and smart workflow integration—areas where Reruption’s AI engineering and Co-Preneur approach are built to help. If you want to explore how Gemini could personalize your outreach without overwhelming your reps, we can work with you to prototype a focused use case and scale it once it proves its value.

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Real-World Case Studies

From Banking to Transportation: 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
Read case study →

Shell

Energy

Unplanned equipment failures in refineries and offshore oil rigs plagued Shell, causing significant downtime, safety incidents, and costly repairs that eroded profitability in a capital-intensive industry. According to a Deloitte 2024 report, 35% of refinery downtime is unplanned, with 70% preventable via advanced analytics—highlighting the gap in traditional scheduled maintenance approaches that missed subtle failure precursors in assets like pumps, valves, and compressors. Shell's vast global operations amplified these issues, generating terabytes of sensor data from thousands of assets that went underutilized due to data silos, legacy systems, and manual analysis limitations. Failures could cost millions per hour, risking environmental spills and personnel safety while pressuring margins amid volatile energy markets.

Lösung

Shell partnered with C3 AI to implement an AI-powered predictive maintenance platform, leveraging machine learning models trained on real-time IoT sensor data, maintenance histories, and operational metrics to forecast failures and optimize interventions. Integrated with Microsoft Azure Machine Learning, the solution detects anomalies, predicts remaining useful life (RUL), and prioritizes high-risk assets across upstream oil rigs and downstream refineries. The scalable C3 AI platform enabled rapid deployment, starting with pilots on critical equipment and expanding globally. It automates predictive analytics, shifting from reactive to proactive maintenance, and provides actionable insights via intuitive dashboards for engineers.

Ergebnisse

  • 20% reduction in unplanned downtime
  • 15% slash in maintenance costs
  • £1M+ annual savings per site
  • 10,000 pieces of equipment monitored globally
  • 35% industry unplanned downtime addressed (Deloitte benchmark)
  • 70% preventable failures mitigated
Read case study →

H&M

Apparel Retail

In the fast-paced world of apparel retail, H&M faced intense pressure from rapidly shifting consumer trends and volatile demand. Traditional forecasting methods struggled to keep up, leading to frequent stockouts during peak seasons and massive overstock of unsold items, which contributed to high waste levels and tied up capital. Reports indicate H&M's inventory inefficiencies cost millions annually, with overproduction exacerbating environmental concerns in an industry notorious for excess. Compounding this, global supply chain disruptions and competition from agile rivals like Zara amplified the need for precise trend forecasting. H&M's legacy systems relied on historical sales data alone, missing real-time signals from social media and search trends, resulting in misallocated inventory across 5,000+ stores worldwide and suboptimal sell-through rates.

Lösung

H&M deployed AI-driven predictive analytics to transform its approach, integrating machine learning models that analyze vast datasets from social media, fashion blogs, search engines, and internal sales. These models predict emerging trends weeks in advance and optimize inventory allocation dynamically. The solution involved partnering with data platforms to scrape and process unstructured data, feeding it into custom ML algorithms for demand forecasting. This enabled automated restocking decisions, reducing human bias and accelerating response times from months to days.

Ergebnisse

  • 30% increase in profits from optimized inventory
  • 25% reduction in waste and overstock
  • 20% improvement in forecasting accuracy
  • 15-20% higher sell-through rates
  • 14% reduction in stockouts
Read case study →

American Eagle Outfitters

Apparel Retail

In the competitive apparel retail landscape, American Eagle Outfitters faced significant hurdles in fitting rooms, where customers crave styling advice, accurate sizing, and complementary item suggestions without waiting for overtaxed associates . Peak-hour staff shortages often resulted in frustrated shoppers abandoning carts, low try-on rates, and missed conversion opportunities, as traditional in-store experiences lagged behind personalized e-commerce . Early efforts like beacon technology in 2014 doubled fitting room entry odds but lacked depth in real-time personalization . Compounding this, data silos between online and offline hindered unified customer insights, making it tough to match items to individual style preferences, body types, or even skin tones dynamically. American Eagle needed a scalable solution to boost engagement and loyalty in flagship stores while experimenting with AI for broader impact .

Lösung

American Eagle partnered with Aila Technologies to deploy interactive fitting room kiosks powered by computer vision and machine learning, rolled out in 2019 at flagship locations in Boston, Las Vegas, and San Francisco . Customers scan garments via iOS devices, triggering CV algorithms to identify items and ML models—trained on purchase history and Google Cloud data—to suggest optimal sizes, colors, and outfit complements tailored to inferred style and preferences . Integrated with Google Cloud's ML capabilities, the system enables real-time recommendations, associate alerts for assistance, and seamless inventory checks, evolving from beacon lures to a full smart assistant . This experimental approach, championed by CMO Craig Brommers, fosters an AI culture for personalization at scale .

Ergebnisse

  • Double-digit conversion gains from AI personalization
  • 11% comparable sales growth for Aerie brand Q3 2025
  • 4% overall comparable sales increase Q3 2025
  • 29% EPS growth to $0.53 Q3 2025
  • Doubled fitting room try-on odds via early tech
  • Record Q3 revenue of $1.36B
Read case study →

Royal Bank of Canada (RBC)

Financial Services

In the competitive retail banking sector, RBC customers faced significant hurdles in managing personal finances. Many struggled to identify excess cash for savings or investments, adhere to budgets, and anticipate cash flow fluctuations. Traditional banking apps offered limited visibility into spending patterns, leading to suboptimal financial decisions and low engagement with digital tools. This lack of personalization resulted in customers feeling overwhelmed, with surveys indicating low confidence in saving and budgeting habits. RBC recognized that generic advice failed to address individual needs, exacerbating issues like overspending and missed savings opportunities. As digital banking adoption grew, the bank needed an innovative solution to transform raw transaction data into actionable, personalized insights to drive customer loyalty and retention.

Lösung

RBC introduced NOMI, an AI-driven digital assistant integrated into its mobile app, powered by machine learning algorithms from Personetics' Engage platform. NOMI analyzes transaction histories, spending categories, and account balances in real-time to generate personalized recommendations, such as automatic transfers to savings accounts, dynamic budgeting adjustments, and predictive cash flow forecasts. The solution employs predictive analytics to detect surplus funds and suggest investments, while proactive alerts remind users of upcoming bills or spending trends. This seamless integration fosters a conversational banking experience, enhancing user trust and engagement without requiring manual input.

Ergebnisse

  • Doubled mobile app engagement rates
  • Increased savings transfers by over 30%
  • Boosted daily active users by 50%
  • Improved customer satisfaction scores by 25%
  • $700M+ projected enterprise value from AI by 2027
  • Higher budgeting adherence leading to 20% better financial habits
Read case study →

Best Practices

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

Connect Gemini to CRM and Workspace for a 360° Prospect Snapshot

The foundation of relevant value propositions is context. Start by configuring Gemini so it can access key CRM objects (accounts, contacts, opportunities, activities) and relevant Google Workspace data such as Gmail threads, meeting notes in Docs, and account plans in Sheets. Work with IT and sales ops to define which fields and documents are in scope and how access is controlled.

Once connected, create a simple internal pattern for reps: open an email thread or account workspace, invoke Gemini, and ask it to summarize the prospect’s situation plus likely priorities. This should become a standard step before writing any major email, call script, or proposal section.

Example prompt inside Gmail/Docs:
You are a sales co-pilot. Based on the CRM data and email history
available to you, answer in English:

1. Who is this prospect (company, role, industry)?
2. What problems and goals have they expressed so far?
3. Which 2-3 value proposition themes (cost savings, growth,
   efficiency, compliance, security, UX, speed) seem most relevant?
4. What evidence from past interactions supports this?
5. What should we explicitly avoid emphasizing based on what you see?

This gives the rep a focused brief that anchors the next touch in real data instead of assumptions.

Generate Role-Specific Email Intros and Call Openers

Most irrelevance happens in the first 3–4 sentences of an email or call. Use Gemini to generate targeted openings that connect directly to the prospect’s role, industry, and recent actions (e.g., whitepaper downloads, webinar attendance, questions asked in email).

Build a small arsenal of prompt templates your team can reuse. Encourage reps to keep the AI-generated structure but adapt tone and specifics to their style.

Example prompt for email intros:
You are helping a sales rep write a short, relevant email intro.
Use the data you have from CRM and Gmail.

Task:
1) Identify the recipient's role and likely top 2 priorities.
2) Write 2 alternative opening paragraphs (2-3 sentences each)
   that:
   - Reference a recent interaction or signal (meeting, question,
     content download, website behavior if available)
   - Tie directly to those priorities
   - Avoid generic product pitching

Output format:
- Brief explanation of priorities
- Version A intro
- Version B intro

Reps can pick the best version, tweak a few words, and send a message that feels tailored rather than templated.

Turn Discovery Notes into Tailored Value Proposition Summaries

After discovery calls, reps often have unstructured notes that never translate into sharp, written value propositions. With Gemini integrated into Docs or your note-taking tool, you can standardize a post-call workflow where reps paste or dictate notes, then let Gemini propose a concise summary plus 3–4 value angles that match what they heard.

Example prompt for discovery synthesis:
You are a sales strategist. Here are my raw notes from a discovery
call (may be messy and incomplete):

[PASTE NOTES]

1. Clean up and structure the notes by topic.
2. Summarize the customer's situation, key pains, and goals.
3. Propose 3-4 tailored value propositions from our list below,
   each in 2-3 sentences, and explain why it fits.

Our value themes:
- Reduce operating costs
- Increase revenue or conversion
- Improve reliability and uptime
- Strengthen compliance and security
- Accelerate time-to-market
- Improve end-user experience

4. Suggest 2 questions for the next call to validate these angles.

Reps can paste the selected value propositions directly into follow-up emails and proposals, ensuring continuity between what was said and what is sent.

Use Gemini to Align Messaging Across Multiple Stakeholders

In complex deals, a big source of irrelevance is sending the same message to every stakeholder. Configure a workflow where Gemini analyzes existing communication with different contacts on the same account and helps the rep adjust the message per role: economic buyer, technical evaluator, day-to-day user.

From within your account workspace, ask Gemini to generate separate talking points or email drafts for each stakeholder, using the same core narrative but emphasizing what each cares about.

Example prompt for multi-stakeholder alignment:
You are preparing tailored messages for a buying committee.
Use CRM and Workspace data for this account.

1. Identify the key stakeholders we are in contact with and their roles.
2. For each person, list:
   - Likely main objective
   - Likely main risk or concern
3. Create 3 bullet points per person explaining our value in their
   language (no product features, only outcomes).
4. Draft a short email or call opener for each stakeholder that
   reinforces these points.

This keeps your story consistent while avoiding one-size-fits-all pitches that don’t land.

Embed Guardrails and Snippets for Consistent yet Flexible Messaging

To balance personalization with control, build a library of approved snippets (proof points, customer outcomes, security statements) and instruct Gemini to pull from these when crafting value propositions. Store these in a shared Doc or Sheet and reference it in your prompts.

You can also specify forbidden claims, tone guidelines, and formatting rules inside the prompt so Gemini stays within your brand and compliance boundaries.

Example configuration prompt for guardrails:
You are a sales co-pilot for [Company].

Rules:
- Only use benefits, numbers, and claims from this library:
  [LINK OR PASTED CONTENT]
- Do NOT invent results, customer names, or percentages.
- Always focus on business outcomes before mentioning features.
- Keep language clear and concrete, avoid buzzwords.

Task:
Given the prospect context and notes above, write 3 tailored
value propositions using ONLY allowed content and rules.

This ensures that even as Gemini personalizes for each prospect, the underlying claims remain accurate and vetted.

Track Impact with Simple, Visible Metrics

To prove that Gemini-based personalization actually fixes irrelevant value propositions, define a small, concrete KPI set before rollout. Typical metrics include: reply rate to first-touch emails, meeting booked rate, progression from discovery to proposal stage, and average number of clarification emails per opportunity.

Set up basic A/B tests: Gemini-supported vs. business-as-usual outreach for comparable segments. Use your CRM to tag AI-assisted activities and review results monthly. Share success examples internally so reps see where tailored value propositions shortened cycles or helped win competitive deals.

Expected outcomes, when implemented well, are realistic and measurable: 10–25% uplift in reply rates on targeted segments, 5–15% higher conversion from discovery to proposal, and a noticeable reduction in back-and-forth emails needed to “clarify what you actually do” within the first 2–3 months of use.

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Frequently Asked Questions

Gemini analyzes CRM data, Gmail threads, Docs, and Sheets to understand who the prospect is, what has been discussed, and which signals they’ve given about their priorities. Instead of generating generic product pitches, Gemini is instructed to map this context to a set of predefined value themes—such as cost savings, growth, security, or time-to-market.

In practice, that means a rep can ask Gemini for a prospect summary and tailored talking points before writing an email or proposal. Gemini then suggests value propositions that connect directly to the buyer’s goals and past interactions, dramatically reducing the risk of sending tone-deaf or irrelevant messaging.

You typically need three ingredients: access to Gemini within Google Workspace, someone who understands your CRM data model, and a small cross-functional team (sales, sales ops, IT) to define value frameworks and guardrails. No deep AI research skills are required, but you do need basic prompt engineering capabilities and the ability to integrate APIs if you want tighter CRM connections.

Reruption usually works with an internal product owner or sales leader, one technical contact (IT or engineering), and 3–5 pilot reps. Together, we shape the prompts, workflows, and access control so Gemini supports real deals without disrupting your existing processes.

For most organisations, an initial Gemini sales personalization pilot can be up and running in 3–6 weeks. In the first 1–2 weeks, you connect data sources, define value themes, and build the first prompt templates. The next 2–4 weeks are focused on testing with a small rep group, collecting examples, and refining prompts and guardrails.

Meaningful early signals—such as higher reply rates or better quality of prospect responses—often appear within the first month of active use. More robust metrics like stage conversion and win rate usually need 2–3 sales cycles to become statistically meaningful, depending on your typical deal length.

The direct software cost is driven by your Gemini licensing within Google Workspace and any additional infrastructure or API usage. For many sales teams, this is modest compared to the cost of headcount and lost opportunities from misaligned messaging. The larger investment is the one-off setup: data connections, value framework definition, and workflow design.

ROI comes from multiple levers: higher reply and meeting-booked rates, faster movement from discovery to proposal, higher win rates from better stakeholder alignment, and reduced rep time spent drafting from scratch. In our experience, even a few incremental wins in your core segments quickly justify the investment if you track uplift against a control group.

Reruption supports companies end-to-end, from defining the concrete use case to shipping a working solution. Our AI PoC offering (9,900€) is designed to prove quickly whether Gemini can meaningfully improve your sales outreach in your specific environment: we scope the use case (inputs, outputs, metrics), prototype the Gemini workflows, and evaluate performance and cost.

With our Co-Preneur approach, we don’t just deliver slides—we embed with your team, work inside your P&L, and build real integrations into CRM and Google Workspace. After the PoC, we can help you harden the prototype, roll it out to more reps, define governance and training, and continuously refine prompts and data structures so your value propositions stay relevant as your market evolves.

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