The Challenge: Unprepared Customer Meetings

In many sales organisations, customer meetings still start with generic introductions and broad product pitches because reps don’t have the time or tools to prepare properly. To get ready, they would need to review emails, CRM notes, previous proposals, LinkedIn profiles, company news, and industry trends—often across six or more tabs. Under constant quota pressure and back-to-back calls, this research simply doesn’t happen, so meetings begin with guesswork instead of insight.

Traditional approaches to preparation no longer work at enterprise scale. Asking reps to "do more research" or giving them static battlecards and slide decks doesn’t solve the core issue: information is fragmented, changes rapidly, and has to be tailored to each account, each buying committee, and even each meeting. Manual prep doesn’t survive in a world of 8–10 stakeholders per deal, complex solution portfolios, and prospects who expect you to know their context better than they do.

The business impact of unprepared customer meetings is significant. Reps default to one-size-fits-all demos, miss critical discovery questions, and overlook buying signals hidden in email threads or previous conversations. This leads to lower conversion rates, longer sales cycles, and lost deals to competitors who arrive with sharper perspectives. Leaders then compensate with more headcount or more meetings instead of better execution per meeting—driving up acquisition costs and burning out teams.

The good news: this is a solvable problem. With the right use of AI copilots like Gemini, you can turn scattered information into concise, deal-specific briefs in minutes, not hours. At Reruption, we’ve built AI solutions that transform how teams prepare, decide, and act. In the rest of this guide, you’ll see how to apply Gemini strategically and tactically so your reps walk into every customer conversation informed, confident, and ready to move the deal forward.

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

From Reruption’s hands-on work building AI copilots for commercial teams, we’ve seen that the biggest gains don’t come from yet another dashboard—they come from embedding intelligence directly into workflows like meeting preparation. Google Gemini is particularly strong here: it can combine public web research, company information, and internal content (emails, docs, CRM exports) into focused, sales-ready briefs. Used correctly, it becomes an always-on analyst that prepares reps for each customer interaction in minutes, not hours.

Anchor Gemini in a Clear Sales Workflow, Not a Generic "Research Tool"

Many teams introduce AI for sales as a general-purpose assistant and then wonder why adoption stalls. For meeting preparation, you need to define a concrete workflow: what inputs Gemini receives, what outputs reps get, and when in the sales process it is used. For example, define that every discovery call triggers a standard Gemini prep package: account summary, stakeholder map, tailored value hypotheses, and 10 discovery questions.

This mindset turns Gemini from a "nice-to-have" tool into a non-negotiable step in your playbook. It also gives sales managers something to coach against: they can review the AI-generated prep, compare it with call outcomes, and refine prompts and templates over time. Without this structure, Gemini risks becoming another tab open in the browser—used sporadically and without measurable impact.

Design for Variability Across Segments, Industries, and Deal Stages

Unprepared meetings look different in SMB vs. enterprise, and in early discovery vs. late-stage negotiations. A strategic deployment of Gemini for sales productivity acknowledges these differences. You should configure different prep templates for new logo discovery, expansion into existing accounts, and executive briefings, each with a distinct focus on expected outcomes and stakeholder types.

Think in terms of "prep playbooks" instead of a single universal prompt. For example, enterprise meetings might prioritise organisational charts, strategic initiatives, and risk narratives, while mid-market calls might emphasise competitive differentiation and fast ROI. This segmentation increases relevance, prevents information overload, and helps reps trust that the AI output genuinely supports their next conversation.

Invest in Data Foundations and Access Boundaries Early

Gemini is only as good as the data and documents it can see. To turn it into a reliable sales meeting preparation copilot, you need to consider which internal sources it should access (CRM exports, proposal libraries, case studies, previous call notes) and under what security and compliance constraints. Fragmented or outdated data leads to low-quality prep; overly open access can create compliance or confidentiality issues.

Strategically, this means aligning with IT, security, and legal early on. Define what can be used for AI, how PII and sensitive deal data are handled, and where logs are stored. At Reruption, our AI Engineering and Security & Compliance workstreams typically run in parallel for this reason: strong guardrails increase trust and adoption, which is critical when AI starts to touch executive-level customer interactions.

Prepare Your Team for a Copilot, Not a Replacement

One recurring failure mode in AI for sales projects is the belief that the tool will magically "know" the customer. In reality, Gemini excels at aggregating and structuring information, but your reps still need to apply judgment, challenge assumptions, and adapt the conversation in real time. Position Gemini as a copilot that takes over the heavy lifting of research and drafting—but makes the human responsible for decisions and nuance.

Organisationally, this means training reps to critique and improve AI outputs: check names and titles, adjust value hypotheses, and add account-specific nuance. Sales leaders should model this behavior, for example by reviewing AI-prepared briefs in pipeline reviews. When reps see that "AI prep" is the starting point, not the final answer, they are more likely to adopt it and less likely to blindly copy-paste content into crucial meetings.

Start with a Focused Pilot and Clear Productivity Metrics

Instead of rolling Gemini out to the entire sales organisation at once, start with a targeted pilot around the specific problem of unprepared customer meetings. Choose a representative group of reps, a defined set of meeting types (e.g. first discovery calls in one region), and a clear baseline of current prep time, call outcomes, and subjective meeting quality.

Then, measure the impact: reduction in manual research time, increased number of meetings with documented prep, changes in conversion from first meeting to opportunity, and qualitative feedback from prospects. This approach aligns well with Reruption’s AI PoC philosophy: you validate that the use case works in practice, including the human aspects, before you invest in full integration with CRM and communication systems.

Using Gemini for sales meeting preparation is not about adding another shiny tool—it’s about building a consistent, AI-powered workflow that turns scattered information into sharp, tailored customer conversations. When you frame Gemini as a structured copilot, grounded in your data and sales playbooks, reps arrive better prepared and deals move faster. Reruption has repeatedly helped organisations move from abstract AI ideas to working copilots embedded in daily workflows; if you want to explore a focused PoC around unprepared meetings, our team can help you scope, prototype, and roll out a solution that actually gets used.

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Real-World Case Studies

From Banking to Logistics: Learn how companies successfully use Gemini.

Lunar

Banking

Lunar, a leading Danish neobank, faced surging customer service demand outside business hours, with many users preferring voice interactions over apps due to accessibility issues. Long wait times frustrated customers, especially elderly or less tech-savvy ones struggling with digital interfaces, leading to inefficiencies and higher operational costs. This was compounded by the need for round-the-clock support in a competitive fintech landscape where 24/7 availability is key. Traditional call centers couldn't scale without ballooning expenses, and voice preference was evident but underserved, resulting in lost satisfaction and potential churn.

Lösung

Lunar deployed Europe's first GenAI-native voice assistant powered by GPT-4, enabling natural, telephony-based conversations for handling inquiries anytime without queues. The agent processes complex banking queries like balance checks, transfers, and support in Danish and English. Integrated with advanced speech-to-text and text-to-speech, it mimics human agents, escalating only edge cases to humans. This conversational AI approach overcame scalability limits, leveraging OpenAI's tech for accuracy in regulated fintech.

Ergebnisse

  • ~75% of all customer calls expected to be handled autonomously
  • 24/7 availability eliminating wait times for voice queries
  • Positive early feedback from app-challenged users
  • First European bank with GenAI-native voice tech
  • Significant operational cost reductions projected
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Revolut

Fintech

Revolut faced escalating Authorized Push Payment (APP) fraud, where scammers psychologically manipulate customers into authorizing transfers to fraudulent accounts, often under guises like investment opportunities. Traditional rule-based systems struggled against sophisticated social engineering tactics, leading to substantial financial losses despite Revolut's rapid growth to over 35 million customers worldwide. The rise in digital payments amplified vulnerabilities, with fraudsters exploiting real-time transfers that bypassed conventional checks. APP scams evaded detection by mimicking legitimate behaviors, resulting in billions in global losses annually and eroding customer trust in fintech platforms like Revolut. Urgent need for intelligent, adaptive anomaly detection to intervene before funds were pushed.

Lösung

Revolut deployed an AI-powered scam detection feature using machine learning anomaly detection to monitor transactions and user behaviors in real-time. The system analyzes patterns indicative of scams, such as unusual payment prompts tied to investment lures, and intervenes by alerting users or blocking suspicious actions. Leveraging supervised and unsupervised ML algorithms, it detects deviations from normal behavior during high-risk moments, 'breaking the scammer's spell' before authorization. Integrated into the app, it processes vast transaction data for proactive fraud prevention without disrupting legitimate flows.

Ergebnisse

  • 30% reduction in fraud losses from APP-related card scams
  • Targets investment opportunity scams specifically
  • Real-time intervention during testing phase
  • Protects 35 million global customers
  • Deployed since February 2024
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Stanford Health Care

Healthcare

Stanford Health Care, a leading academic medical center, faced escalating clinician burnout from overwhelming administrative tasks, including drafting patient correspondence and managing inboxes overloaded with messages. With vast EHR data volumes, extracting insights for precision medicine and real-time patient monitoring was manual and time-intensive, delaying care and increasing error risks. Traditional workflows struggled with predictive analytics for events like sepsis or falls, and computer vision for imaging analysis, amid growing patient volumes. Clinicians spent excessive time on routine communications, such as lab result notifications, hindering focus on complex diagnostics. The need for scalable, unbiased AI algorithms was critical to leverage extensive datasets for better outcomes.

Lösung

Partnering with Microsoft, Stanford became one of the first healthcare systems to pilot Azure OpenAI Service within Epic EHR, enabling generative AI for drafting patient messages and natural language queries on clinical data. This integration used GPT-4 to automate correspondence, reducing manual effort. Complementing this, the Healthcare AI Applied Research Team deployed machine learning for predictive analytics (e.g., sepsis, falls prediction) and explored computer vision in imaging projects. Tools like ChatEHR allow conversational access to patient records, accelerating chart reviews. Phased pilots addressed data privacy and bias, ensuring explainable AI for clinicians.

Ergebnisse

  • 50% reduction in time for drafting patient correspondence
  • 30% decrease in clinician inbox burden from AI message routing
  • 91% accuracy in predictive models for inpatient adverse events
  • 20% faster lab result communication to patients
  • Improved autoimmune detection by 1 year prior to diagnosis
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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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NYU Langone Health

Healthcare

NYU Langone Health, a leading academic medical center, faced significant hurdles in leveraging the vast amounts of unstructured clinical notes generated daily across its network. Traditional clinical predictive models relied heavily on structured data like lab results and vitals, but these required complex ETL processes that were time-consuming and limited in scope. Unstructured notes, rich with nuanced physician insights, were underutilized due to challenges in natural language processing, hindering accurate predictions of critical outcomes such as in-hospital mortality, length of stay (LOS), readmissions, and operational events like insurance denials. Clinicians needed real-time, scalable tools to identify at-risk patients early, but existing models struggled with the volume and variability of EHR data—over 4 million notes spanning a decade. This gap led to reactive care, increased costs, and suboptimal patient outcomes, prompting the need for an innovative approach to transform raw text into actionable foresight.

Lösung

To address these challenges, NYU Langone's Division of Applied AI Technologies at the Center for Healthcare Innovation and Delivery Science developed NYUTron, a proprietary large language model (LLM) specifically trained on internal clinical notes. Unlike off-the-shelf models, NYUTron was fine-tuned on unstructured EHR text from millions of encounters, enabling it to serve as an all-purpose prediction engine for diverse tasks. The solution involved pre-training a 13-billion-parameter LLM on over 10 years of de-identified notes (approximately 4.8 million inpatient notes), followed by task-specific fine-tuning. This allowed seamless integration into clinical workflows, automating risk flagging directly from physician documentation without manual data structuring. Collaborative efforts, including AI 'Prompt-a-Thons,' accelerated adoption by engaging clinicians in model refinement.

Ergebnisse

  • AUROC: 0.961 for 48-hour mortality prediction (vs. 0.938 benchmark)
  • 92% accuracy in identifying high-risk patients from notes
  • LOS prediction AUROC: 0.891 (5.6% improvement over prior models)
  • Readmission prediction: AUROC 0.812, outperforming clinicians in some tasks
  • Operational predictions (e.g., insurance denial): AUROC up to 0.85
  • 24 clinical tasks with superior performance across mortality, LOS, and comorbidities
Read case study →

Best Practices

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

Create a Standard Gemini "Meeting Prep" Prompt Template

The fastest way to operationalise AI-powered sales meeting preparation is to define a reusable prompt that every rep can use before a call. This template should combine public web research with internal information about the account, opportunity, and product lines. Encourage reps to paste relevant snippets from CRM, recent emails, and any briefing notes directly into the prompt.

Example prompt for Gemini:

You are a sales meeting preparation copilot.

Goal: Prepare me for a 45-minute discovery call with the following prospect.

Prospect details:
- Company: <company name>
- Website: <URL>
- Prospect roles and LinkedIn profiles: <names/URLs if available>
- Meeting context: <short description, e.g. inbound demo request, RFP, expansion>

Internal information:
- CRM notes: <paste relevant notes>
- Recent emails: <paste last 3–5 emails>
- Relevant products/solutions: <list>

Tasks:
1) Summarise the company in 5 bullet points (business model, size, key markets).
2) Identify likely business priorities and challenges based on industry and recent news.
3) Suggest 3–5 tailored value hypotheses connecting our solutions to their context.
4) Propose 10 discovery questions, grouped by theme.
5) Highlight potential risks or red flags I should be aware of.
6) Provide a concise one-page meeting brief I can review in 3 minutes.

Train reps to save this prompt (e.g. in a snippet manager or internal wiki) and adapt it lightly per segment. Over time, you can refine the prompt based on which parts of the AI-generated prep correlate with successful meetings.

Use Gemini to Turn Long Email Threads and Docs into a Deal Brief

Before many meetings, the most valuable signals are buried inside long email chains, technical attachments, or internal Slack threads. Instead of asking reps to scroll through everything, use Gemini to transform this noise into a short deal brief they can digest quickly. This is especially powerful for handovers between SDRs and AEs or between sales and customer success.

Example prompt for Gemini:

You are assisting with sales deal preparation.

Here are:
- Email threads between our team and the prospect
- Internal notes and Slack messages about this account
- Any attached requirements or documents

Tasks:
1) Summarise the history of the deal in a narrative of max 10 bullet points.
2) List all known stakeholders, their roles, and inferred interests.
3) Extract key requirements, constraints, and decision criteria.
4) Identify any unanswered questions we should clarify in the next meeting.
5) Suggest a short meeting agenda to advance the opportunity.

This workflow can cut handover time dramatically and ensure that even a rep joining mid-cycle has enough context to run a confident, focused meeting.

Generate Custom Discovery Question Sets by Industry and Persona

Generic discovery questions are a hallmark of unprepared meetings. With Gemini, you can generate and refine persona-specific discovery questions that are tied to industry context and your solution portfolio. Start by creating master question sets for key personas (CFO, CIO, Head of Operations, Sales Leader) and industries, then use Gemini to adapt them to each account.

Example prompt for Gemini:

You are a sales discovery expert.

Context:
- Industry: <industry>
- Persona: <e.g. CFO, VP Operations>
- Our solution focus: <brief description>
- Prospect situation (if known): <short summary>

Task:
Create 15 discovery questions that:
- Avoid generic "tell me about your business" prompts
- Tie directly to likely KPIs and initiatives for this persona
- Surface current pain, existing solutions, and decision process
- Are phrased in a consultative, non-leading way

Group the questions into 3 sections: Current State, Impact & Priorities, Decision & Next Steps.

Store your best outputs in a shared repository and have reps paste 5–8 questions from this library into their specific meeting prep. Over time, you build a living asset that keeps getting sharper with feedback from real calls.

Connect Gemini Prep with Your CRM and Note-Taking Habits

To avoid creating yet another disconnected artefact, integrate Gemini meeting prep with your CRM and documentation habits. After Gemini generates the brief, reps should paste the key parts (summary, stakeholders, value hypotheses, planned questions) into the relevant CRM fields or a standardised meeting prep section. This makes preparation visible and reportable.

After the meeting, reps can paste their raw notes or call transcript snippets back into Gemini with a follow-up prompt such as: "Summarise the meeting in 8 bullet points, update the opportunity description, and list next best actions with owners and dates." This closes the loop: the same copilot that prepared the meeting also structures its outcomes, reducing admin work and improving data quality.

Use Gemini to Draft Tailored Opening and Recap Emails

Prepared meetings are reinforced by clear communication before and after. Use Gemini to draft personalised pre-meeting and post-meeting emails based on the brief and notes. This saves time and ensures that every customer touchpoint feels tailored and professional.

Example prompt for pre-meeting email:

You are helping a sales rep write a concise, professional pre-meeting email.

Inputs:
- Prospect details and meeting context: <paste from brief>
- Planned agenda: <list>

Task:
Draft an email that:
- Confirms the time and participants
- Shares a 2–3 bullet agenda
- Demonstrates that we understand their context
- Invites them to add topics or questions

Keep it under 180 words and match a neutral, business-friendly tone.

For recap emails, ask Gemini to highlight agreed pain points, next steps, owners, and timelines. This reduces follow-up friction and helps drive the opportunity forward without extra manual effort.

Continuously Refine Prompts Based on Call Outcomes and Manager Feedback

Your first version of Gemini prompts for meeting preparation will not be perfect. Treat them as living assets: after key calls, ask reps and managers where the prep was helpful and where it missed the mark. Then refine the prompts to emphasise or de-emphasise certain sections, add new questions, or adjust the level of detail.

For example, if reps report that the AI spends too much time on generic company descriptions and not enough on potential blockers, tweak the prompt to explicitly ask for "3 potential internal blockers and how to address them." Document these iterations centrally so improvements spread across the team. Over several cycles, you will see prep quality stabilise and become a consistent strength rather than a hit-or-miss effort.

When implemented this way, organisations typically see tangible outcomes: 30–50% less manual research time per meeting, near-100% rate of documented prep for key calls, more focused discovery conversations, and a measurable uplift in conversion from first meeting to qualified opportunity. The exact metrics will vary, but the pattern is consistent: better prepared reps close more with the same or smaller headcount.

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Frequently Asked Questions

Gemini acts as a sales meeting preparation copilot. Reps provide basic inputs—company name, meeting context, recent emails, and relevant CRM notes—and Gemini generates a concise brief: company overview, likely priorities, stakeholder map, tailored value hypotheses, and targeted discovery questions. It can also scan long email threads and internal notes to summarise deal history and open issues.

Instead of spending 30–60 minutes jumping between tabs, reps get a structured, 3–5 minute briefing they can review before the call. This drastically reduces the chance of "blank slate" meetings and helps even new team members operate with senior-level context.

You don’t need a large data science team to start. At minimum, you need: (1) a project owner in sales or revenue operations, (2) access to Gemini (via Google Workspace or API), and (3) collaboration with IT/security to define data access rules. The core work is designing effective prompts, standardising workflows, and integrating outputs into your existing tools (CRM, email, note-taking).

Reruption typically brings in an AI engineer and a product-minded lead to work with your sales leadership. Together, we define workflows, build and test prompt templates, and set up light integrations or automations so the experience is smooth for reps.

For a focused pilot on unprepared customer meetings, you can usually see first results within 2–4 weeks. In the first week, you define workflows and initial prompts; in weeks two and three, a subset of reps uses Gemini on real meetings while you collect feedback and adjust the setup. By week four, you should have clear indicators on preparation time reduction, adoption, and early impact on meeting quality.

Deeper integration with CRM and company-wide rollout may take several more weeks, depending on your tech landscape and change management needs. But you don’t need to wait for full integration to benefit; even a copy-paste workflow can create visible gains quickly.

The ROI comes from both time savings and better deal outcomes. On the time side, teams often cut manual research and note structuring by 30–50%, freeing up several hours per rep per week for actual selling. On the effectiveness side, more prepared meetings typically drive higher conversion from first call to qualified opportunity and reduce cycle times, especially in complex B2B deals.

Because Gemini is a flexible platform, you can start small—e.g. a limited number of seats or a narrow use case—then expand once you’ve validated impact. During a PoC, Reruption helps you define and track concrete metrics (prep time, adoption, conversion rates) so you can quantify ROI rather than relying on anecdotes.

Reruption supports you end-to-end with a Co-Preneur approach: we embed with your team, challenge existing sales workflows, and build something that actually ships. Our AI PoC offering (9.900€) is a structured way to validate this use case quickly. We help you define the meeting prep workflow, design and test Gemini prompts, assess feasibility and security, and deliver a working prototype your reps can use in real opportunities.

Beyond the PoC, our AI Engineering, Strategy, and Enablement capabilities ensure the solution scales: we integrate with your CRM and productivity tools, set up governance, and train your sales organisation so Gemini becomes a natural part of every important customer meeting—not another unused tool in the stack.

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