The Challenge: Fragmented Preboarding Communication

For many HR teams, preboarding has become a chaotic mix of emails, PDFs, spreadsheets and ad-hoc messages from different stakeholders. IT sends account details, managers send role information, HR shares policies and forms, and facilities add their own instructions. New hires are left stitching this together themselves, usually across multiple threads and channels.

Traditional approaches rely on manual coordination, generic email templates and static checklists stored in shared drives. They do not adapt to the role, location, seniority or start date of each new hire. There is no single source of truth for what has been sent, what has been completed and what is still outstanding. As hiring volumes and remote work grow, this model simply does not scale.

The business impact is substantial. Important tasks get buried in inboxes, contract signatures and compliance trainings are delayed, and equipment is not ready on day one. HR spends hours chasing confirmations and answering the same questions instead of focusing on strategic onboarding and culture-building. Time-to-productivity increases, and the first impression of your company is one of confusion rather than clarity — which directly affects retention in the critical first 90 days.

This challenge is real, but it is also highly solvable. With the right use of AI, preboarding can become a structured, personalized and largely self-driving experience that works across Gmail, Docs and Chat. At Reruption, we have hands-on experience turning similar information-heavy, manual processes into AI-powered workflows. The rest of this page walks through how HR leaders can use Gemini to transform fragmented preboarding communication into a coherent, engaging journey for every new hire.

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Our Assessment

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

From Reruption's perspective, Gemini in Google Workspace is a strong fit for solving fragmented preboarding communication because it lives where HR work already happens: Gmail, Docs, Sheets, Calendar and Chat. Instead of introducing yet another portal, you can orchestrate preboarding with AI directly inside your existing tools. Based on our experience building real AI products and automations, the opportunity is not just to "speed up emails" but to redesign the whole preboarding flow as a personalized, AI-guided experience for every new hire.

Define Preboarding as a Repeatable Process, Not a Series of Emails

Before you deploy Gemini for HR preboarding, you need a clear definition of what “good” preboarding looks like in your organization. Map the end-to-end process: triggers (offer accepted), required tasks (contracts, IT access, compliance trainings), stakeholders (HR, manager, IT, facilities) and milestones (all prerequisites done before day one). Treat this as a process design exercise, not a copy-paste of your current email habits.

Once you have this blueprint, you can use Gemini to orchestrate the flow: generating role-specific timelines, surfacing the right documents at the right time, and nudging stakeholders when they are the bottleneck. Without this foundation, AI will only accelerate existing fragmentation rather than remove it.

Start with a Narrow Pilot Around One or Two Roles

Strategically, it is risky to “AI-ify” every onboarding scenario at once. Instead, select one or two high-volume roles (for example, sales representatives or customer service agents) and design a focused Gemini preboarding pilot just for them. This keeps scope manageable and gives you quick feedback from a meaningful user group.

In the pilot, track metrics like completion rates of preboarding tasks, number of HR follow-up emails, and self-reported clarity from new hires. Use these insights to refine prompts, templates and workflows before scaling to other roles or regions. This iterative approach is core to Reruption’s Co-Preneur mindset: ship something real fast, then improve based on data.

Position Gemini as a Co-Pilot for HR, Not a Replacement for Human Touch

People-related processes require trust. Internally, HR and hiring managers may worry that using AI in onboarding will make interactions feel impersonal or scripted. Strategically, you should position Gemini as a co-pilot that handles coordination, information retrieval and routine questions, while humans provide context, empathy and culture.

Communicate clearly: Gemini helps consolidate information, personalize checklists and answer standard questions 24/7, but managers are still responsible for welcome calls, team introductions and feedback conversations. This framing reduces resistance and encourages HR teams to embrace AI as leverage, not a threat.

Design for Cross-Functional Alignment from Day One

Preboarding sits at the intersection of HR, IT, security, facilities and line managers. Implementing Gemini for onboarding workflows without those teams at the table will create new friction points. Strategically, include representatives from each function when defining templates and triggers: what should Gemini send, who approves it, what data can it access, and what are the escalation paths?

This alignment ensures that AI-generated timelines and reminders actually reflect reality — for example, realistic lead times for laptop provisioning or access approvals. It also helps you address security and compliance concerns early, especially around what employee data Gemini is allowed to process inside Google Workspace.

Establish Clear Guardrails, Governance and Success Metrics

To use Gemini in HR responsibly, define governance upfront: who can modify prompts and templates, what content is off-limits for AI generation, and how you will monitor quality. For regulated environments, document which parts of preboarding communication can be AI-generated and which must remain human-authored or legally reviewed.

At the same time, define a small set of success metrics that tie directly to business value: reduced HR time spent on preboarding, improved time-to-productivity, fewer missed tasks before day one, and higher new-hire satisfaction scores. These KPIs make it easier to communicate impact internally and secure further investment in AI-driven HR initiatives.

Used thoughtfully, Gemini in Google Workspace can turn fragmented preboarding into a structured, AI-orchestrated experience that reduces manual HR work while giving new hires a clear, personalized path to day one. The real value comes from combining the tool with solid process design, cross-functional alignment and pragmatic guardrails. Reruption’s team is used to building exactly these kinds of AI-first workflows inside existing systems; if you want to explore a focused preboarding pilot or validate feasibility with a technical PoC, we’re ready to work alongside your HR and IT teams to make it real.

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Real-World Case Studies

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

DBS Bank

Banking

DBS Bank, Southeast Asia's leading financial institution, grappled with scaling AI from experiments to production amid surging fraud threats, demands for hyper-personalized customer experiences, and operational inefficiencies in service support. Traditional fraud detection systems struggled to process up to 15,000 data points per customer in real-time, leading to missed threats and suboptimal risk scoring. Personalization efforts were hampered by siloed data and lack of scalable algorithms for millions of users across diverse markets. Additionally, customer service teams faced overwhelming query volumes, with manual processes slowing response times and increasing costs. Regulatory pressures in banking demanded responsible AI governance, while talent shortages and integration challenges hindered enterprise-wide adoption. DBS needed a robust framework to overcome data quality issues, model drift, and ethical concerns in generative AI deployment, ensuring trust and compliance in a competitive Southeast Asian landscape.

Lösung

DBS launched an enterprise-wide AI program with over 20 use cases, leveraging machine learning for advanced fraud risk models and personalization, complemented by generative AI for an internal support assistant. Fraud models integrated vast datasets for real-time anomaly detection, while personalization algorithms delivered hyper-targeted nudges and investment ideas via the digibank app. A human-AI synergy approach empowered service teams with a GenAI assistant handling routine queries, drawing from internal knowledge bases. DBS emphasized responsible AI through governance frameworks, upskilling 40,000+ employees, and phased rollout starting with pilots in 2021, scaling production by 2024. Partnerships with tech leaders and Harvard-backed strategy ensured ethical scaling across fraud, personalization, and operations.

Ergebnisse

  • 17% increase in savings from prevented fraud attempts
  • Over 100 customized algorithms for customer analyses
  • 250,000 monthly queries processed efficiently by GenAI assistant
  • 20+ enterprise-wide AI use cases deployed
  • Analyzes up to 15,000 data points per customer for fraud
  • Boosted productivity by 20% via AI adoption (CEO statement)
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Cleveland Clinic

Healthcare

At Cleveland Clinic, one of the largest academic medical centers, physicians grappled with a heavy documentation burden, spending up to 2 hours per day on electronic health record (EHR) notes, which detracted from patient care time. This issue was compounded by the challenge of timely sepsis identification, a condition responsible for nearly 350,000 U.S. deaths annually, where subtle early symptoms often evade traditional monitoring, leading to delayed antibiotics and 20-30% mortality rates in severe cases. Sepsis detection relied on manual vital sign checks and clinician judgment, frequently missing signals 6-12 hours before onset. Integrating unstructured data like clinical notes was manual and inconsistent, exacerbating risks in high-volume ICUs.

Lösung

Cleveland Clinic piloted Bayesian Health’s AI platform, a predictive analytics tool that processes structured and unstructured data (vitals, labs, notes) via machine learning to forecast sepsis risk up to 12 hours early, generating real-time EHR alerts for clinicians. The system uses advanced NLP to mine clinical documentation for subtle indicators. Complementing this, the Clinic explored ambient AI solutions like speech-to-text systems (e.g., similar to Nuance DAX or Abridge), which passively listen to doctor-patient conversations, apply NLP for transcription and summarization, auto-populating EHR notes to cut documentation time by 50% or more. These were integrated into workflows to address both prediction and admin burdens.

Ergebnisse

  • 12 hours earlier sepsis prediction
  • 32% increase in early detection rate
  • 87% sensitivity and specificity in AI models
  • 50% reduction in physician documentation time
  • 17% fewer false positives vs. physician alone
  • Expanded to full rollout post-pilot (Sep 2025)
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Mastercard

Payments

In the high-stakes world of digital payments, card-testing attacks emerged as a critical threat to Mastercard's ecosystem. Fraudsters deploy automated bots to probe stolen card details through micro-transactions across thousands of merchants, validating credentials for larger fraud schemes. Traditional rule-based and machine learning systems often detected these only after initial tests succeeded, allowing billions in annual losses and disrupting legitimate commerce. The subtlety of these attacks—low-value, high-volume probes mimicking normal behavior—overwhelmed legacy models, exacerbated by fraudsters' use of AI to evade patterns. As transaction volumes exploded post-pandemic, Mastercard faced mounting pressure to shift from reactive to proactive fraud prevention. False positives from overzealous alerts led to declined legitimate transactions, eroding customer trust, while sophisticated attacks like card-testing evaded detection in real-time. The company needed a solution to identify compromised cards preemptively, analyzing vast networks of interconnected transactions without compromising speed or accuracy.

Lösung

Mastercard's Decision Intelligence (DI) platform integrated generative AI with graph-based machine learning to revolutionize fraud detection. Generative AI simulates fraud scenarios and generates synthetic transaction data, accelerating model training and anomaly detection by mimicking rare attack patterns that real data lacks. Graph technology maps entities like cards, merchants, IPs, and devices as interconnected nodes, revealing hidden fraud rings and propagation paths in transaction graphs. This hybrid approach processes signals at unprecedented scale, using gen AI to prioritize high-risk patterns and graphs to contextualize relationships. Implemented via Mastercard's AI Garage, it enables real-time scoring of card compromise risk, alerting issuers before fraud escalates. The system combats card-testing by flagging anomalous testing clusters early. Deployment involved iterative testing with financial institutions, leveraging Mastercard's global network for robust validation while ensuring explainability to build issuer confidence.

Ergebnisse

  • 2x faster detection of potentially compromised cards
  • Up to 300% boost in fraud detection effectiveness
  • Doubled rate of proactive compromised card notifications
  • Significant reduction in fraudulent transactions post-detection
  • Minimized false declines on legitimate transactions
  • Real-time processing of billions of transactions
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Klarna

Fintech

Klarna, a leading fintech BNPL provider, faced enormous pressure from millions of customer service inquiries across multiple languages for its 150 million users worldwide. Queries spanned complex fintech issues like refunds, returns, order tracking, and payments, requiring high accuracy, regulatory compliance, and 24/7 availability. Traditional human agents couldn't scale efficiently, leading to long wait times averaging 11 minutes per resolution and rising costs. Additionally, providing personalized shopping advice at scale was challenging, as customers expected conversational, context-aware guidance across retail partners. Multilingual support was critical in markets like US, Europe, and beyond, but hiring multilingual agents was costly and slow. This bottleneck hindered growth and customer satisfaction in a competitive BNPL sector.

Lösung

Klarna partnered with OpenAI to deploy a generative AI chatbot powered by GPT-4, customized as a multilingual customer service assistant. The bot handles refunds, returns, order issues, and acts as a conversational shopping advisor, integrated seamlessly into Klarna's app and website. Key innovations included fine-tuning on Klarna's data, retrieval-augmented generation (RAG) for real-time policy access, and safeguards for fintech compliance. It supports dozens of languages, escalating complex cases to humans while learning from interactions. This AI-native approach enabled rapid scaling without proportional headcount growth.

Ergebnisse

  • 2/3 of all customer service chats handled by AI
  • 2.3 million conversations in first month alone
  • Resolution time: 11 minutes → 2 minutes (82% reduction)
  • CSAT: 4.4/5 (AI) vs. 4.2/5 (humans)
  • $40 million annual cost savings
  • Equivalent to 700 full-time human agents
  • 80%+ queries resolved without human intervention
Read case study →

AstraZeneca

Healthcare

In the highly regulated pharmaceutical industry, AstraZeneca faced immense pressure to accelerate drug discovery and clinical trials, which traditionally take 10-15 years and cost billions, with low success rates of under 10%. Data silos, stringent compliance requirements (e.g., FDA regulations), and manual knowledge work hindered efficiency across R&D and business units. Researchers struggled with analyzing vast datasets from 3D imaging, literature reviews, and protocol drafting, leading to delays in bringing therapies to patients. Scaling AI was complicated by data privacy concerns, integration into legacy systems, and ensuring AI outputs were reliable in a high-stakes environment. Without rapid adoption, AstraZeneca risked falling behind competitors leveraging AI for faster innovation toward 2030 ambitions of novel medicines.

Lösung

AstraZeneca launched an enterprise-wide generative AI strategy, deploying ChatGPT Enterprise customized for pharma workflows. This included AI assistants for 3D molecular imaging analysis, automated clinical trial protocol drafting, and knowledge synthesis from scientific literature. They partnered with OpenAI for secure, scalable LLMs and invested in training: ~12,000 employees across R&D and functions completed GenAI programs by mid-2025. Infrastructure upgrades, like AMD Instinct MI300X GPUs, optimized model training. Governance frameworks ensured compliance, with human-in-loop validation for critical tasks. Rollout phased from pilots in 2023-2024 to full scaling in 2025, focusing on R&D acceleration via GenAI for molecule design and real-world evidence analysis.

Ergebnisse

  • ~12,000 employees trained on generative AI by mid-2025
  • 85-93% of staff reported productivity gains
  • 80% of medical writers found AI protocol drafts useful
  • Significant reduction in life sciences model training time via MI300X GPUs
  • High AI maturity ranking per IMD Index (top global)
  • GenAI enabling faster trial design and dose selection
Read case study →

Best Practices

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

Centralize Preboarding Data in a Single Source of Truth

Before you can automate anything with Gemini, consolidate your preboarding data in one place. Use a Google Sheet or an HRIS export as the master table with key fields: name, role, manager, location, start date, required trainings, equipment needs and special notes. This becomes the structured input that Gemini can reference when generating communications and timelines.

Connect this sheet to your workflows: when a new row is added (offer accepted), it should trigger a sequence of Gemini-assisted tasks in Gmail, Docs and Calendar. Even without custom code, you can combine Google Apps Script with Gemini prompts to read from the sheet and generate tailored content.

Use Gemini to Generate Role-Specific Preboarding Timelines

Instead of sending a long generic email, let Gemini for HR preboarding create a concise, role-specific plan for each new hire. In Google Docs, you can maintain a base template and use Gemini to fill in dates, links and tasks based on the person’s start date, country and role seniority.

For example, in a Doc connected to your data sheet, you can prompt Gemini like this:

Act as an HR onboarding assistant.
Create a preboarding plan for the following new hire:
- Name: {{Name}}
- Role: {{Role}}
- Location: {{Location}}
- Start date: {{Start_Date}}
- Manager: {{Manager_Name}}

The plan should:
- Be structured by week before start date
- Highlight mandatory actions (contracts, ID upload, compliance training)
- Include links (placeholders) to relevant policies and tools
- Use a friendly, professional tone
- Keep it to max 600 words

The generated plan can be shared as a Doc link or summarized by Gemini again into a shorter email for Gmail.

Automate Clear, Consolidated Preboarding Emails in Gmail

Gemini’s integration with Gmail enables HR to send personalized, consolidated preboarding emails that combine all key tasks in one place instead of ten different messages. Start with a standard draft and ask Gemini to adapt it using data from your master sheet.

Example prompt inside Gmail:

You are assisting HR with preboarding communication.
Using the details below, rewrite this draft email so that it:
- Lists all preboarding tasks in a clear checklist
- Orders tasks by priority and due date
- Highlights mandatory items in bold
- Uses a warm, welcoming tone suitable for a new colleague

New hire details:
{{Paste row from preboarding sheet}}

Existing draft:
{{Draft email content}}

Expected outcome: new hires receive a single, well-structured email that replaces multiple scattered messages, reducing confusion and follow-up questions.

Deploy Gemini in Google Chat as a Preboarding Q&A Assistant

Many first-week questions are repetitive: “Where do I upload my ID?”, “How do I access the LMS?”, “Who approves my hardware request?”. You can configure a Gemini-powered Chat space (or use Gemini in Chat) as a preboarding Q&A assistant that points to the right Docs, policies and contacts.

Seed Gemini with key resources: a curated document of FAQs, links to internal knowledge bases, and your HR policy documents. Then use a system-style prompt like:

You are an internal HR preboarding assistant for ACME.
Answer questions from new hires using ONLY these sources:
- HR preboarding FAQ: <link>
- IT onboarding wiki: <link>
- Company policies overview: <link>

Guidelines:
- If you are not sure, ask the user to contact HR at hr@company.com
- Provide short, actionable answers with links to the right documents
- Be friendly and encouraging

This doesn’t replace HR, but it dramatically reduces simple, repetitive queries and gives HR more time for higher-value conversations.

Let Gemini Summarize Policies into Human-Friendly Overviews

New hires rarely read 30-page policy documents. Use Gemini for document summarization to create short, role-relevant overviews that explain “what this means for you” in plain language. Work within Google Docs: paste or link the policy, then ask Gemini to generate a new-hire summary.

Sample prompt in Docs:

Summarize the following policy for a new employee in {{Location}} working as {{Role}}.

Output requirements:
- 3–5 short sections with headings
- Focus on what the employee must do or avoid
- Highlight any deadlines or mandatory trainings
- Neutral, clear tone
- Max 700 words

Policy content:
{{Paste policy text or key sections}}

You can then link these summaries directly in preboarding emails or timelines, ensuring compliance information is both accessible and understandable.

Track Completion and Risks with Gemini-Generated Status Summaries

To move beyond manual chasing, use Gemini to create preboarding status summaries for HR and hiring managers based on your master sheet or checklists. For example, track completion of contract signing, identity verification, equipment ordering and mandatory trainings in a Google Sheet. Then have Gemini generate weekly summaries by manager or department.

Example status prompt in Sheets or Docs:

Analyze the following preboarding status table.
For each manager, summarize:
- New hires starting in the next 3 weeks
- Any missing critical tasks (contracts, equipment, compliance)
- Clear actions the manager or HR must take this week

Be concise and action-oriented.

Data:
{{Copy-paste filtered table from Sheets}}

Expected outcome: HR can proactively flag risks (e.g., laptop not ordered, mandatory training incomplete) and nudge stakeholders, reducing day-one surprises.

When implemented step by step, these practices typically lead to tangible improvements: 20–40% less HR time spent on preboarding coordination, a sharp drop in missing documents or access on day one, and higher new-hire satisfaction scores in onboarding surveys. The exact metrics depend on your baseline, but with a focused Gemini pilot and clear KPIs, you should see meaningful impact within one or two hiring cycles.

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Frequently Asked Questions

Gemini reduces fragmentation by centralizing information and generating personalized outputs across the tools you already use in Google Workspace. Instead of multiple stakeholders sending separate emails, Gemini can compile data from a master sheet or HR system and create a single, role-specific preboarding plan and email for each new hire.

It also helps summarize policies, generate checklists, and power a Q&A assistant in Google Chat. This means fewer ad-hoc messages, clearer timelines for new hires, and less manual coordination for HR.

You do not need a large data science team to start. For a first Gemini preboarding pilot, you typically need:

  • An HR process owner who knows the current preboarding steps and desired future state.
  • Access to Google Workspace admin/configuration (to manage Docs, Sheets, Gmail and Chat setup).
  • Basic scripting or IT support if you want to trigger workflows automatically (e.g., via Apps Script or simple integrations).

Reruption usually works with a small cross-functional squad (HR, IT, sometimes Legal) to design prompts, templates and guardrails. Over time, HR team members can maintain and adapt these assets themselves.

With a focused scope, you can see visible improvements within one to two hiring cycles. A typical timeline looks like this:

  • Week 1–2: Map the preboarding process for a target role, set up master data (Sheet), and design first Gemini prompts/templates.
  • Week 3–4: Run a live pilot with a small group of new hires; collect feedback from them, HR and managers.
  • Week 5–6: Refine prompts, templates and governance; extend to more roles or regions if the results are positive.

Clients often report reduced follow-up emails and clearer new-hire communication as early as the first cohort using the new Gemini-enabled workflow.

ROI comes from saved HR time, fewer errors and faster time-to-productivity. For example, if your HR team spends several hours per hire on manual preboarding emails, chasing documents and answering repetitive questions, automating communication with Gemini can reduce that by 20–40% for high-volume roles.

On top of time savings, improved preboarding reduces day-one issues (missing access, incomplete compliance steps), which in turn shortens the ramp-up period for new hires and improves early retention. While exact numbers depend on your baseline, it is usually straightforward to calculate a business case by combining HR time savings with reduced delays and fewer avoidable tickets to IT or HR support.

Reruption supports companies end-to-end, from idea to working solution. With our AI PoC offering (9,900€), we can quickly test whether your specific preboarding use case works in practice: we define the scope, prototype Gemini prompts and workflows inside Google Workspace, measure performance and outline a production-ready architecture.

Beyond the PoC, our Co-Preneur approach means we embed with your HR and IT teams to actually build and roll out the solution: structuring preboarding data, designing templates, setting up governance and iterating based on real user feedback. We don’t stop at slides; we ship a functioning Gemini-enabled preboarding flow that fits your organization and can be scaled across roles and locations.

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