The Challenge: Slow Access and Account Provisioning

Many organisations invest heavily in attracting top talent, only to lose momentum in the first week because basic IT access is missing. New hires arrive without laptops, cannot log into core systems, and wait days for tool permissions. HR ends up chasing IT, managers, and vendors via email and spreadsheets, while new employees sit idle and frustrated instead of getting productive.

Traditional onboarding workflows are heavily manual and fragmented across HR, IT, security, and line managers. Requests are buried in inboxes, tasks live in different tools, and there is no single view of who needs what, by when. Even with ticketing systems, configuration is often generic and static, which means edge cases, role changes, and exceptions are handled in ad-hoc ways that constantly leak work back to HR.

The impact is bigger than a few lost days. Slow access and account provisioning drives up onboarding costs, delays time-to-productivity, and undermines your employer brand. Managers lose trust in HR and IT, new hires question their decision to join, and critical projects slip because people simply cannot use the tools they were hired to work with. Over time, these frictions add up to higher early attrition and a competitive disadvantage in attracting and retaining talent.

The good news: this problem is highly solvable. With the right use of AI in HR onboarding, you can orchestrate access requests, automate most provisioning steps, and give every new hire a clear, guided path through their first days. At Reruption, we’ve seen how AI-powered workflows can replace brittle manual coordination with reliable, auditable automation. Below, you’ll find practical guidance on how to use Gemini to transform slow access and account provisioning into a smooth, predictable experience.

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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-powered workflows and assistants inside organisations, we’ve seen that slow onboarding access is rarely a pure IT problem. It’s a coordination and decision problem that is perfect for Gemini as a conversational layer across HR, identity management, and collaboration tools. When designed correctly, Gemini doesn’t just answer questions; it can analyse onboarding bottlenecks, propose automation rules, and orchestrate the flow of access requests between HR, IT, and managers.

Treat Access Provisioning as a Product, Not a Ticket Queue

For Gemini to meaningfully improve onboarding access and account provisioning, HR and IT need to stop thinking in isolated tickets and start thinking in end-to-end journeys. Map the full lifecycle from contract signed to “fully productive” for each key role: what systems, devices, and permissions are needed at each stage? This product mindset gives Gemini a clear target state to orchestrate towards.

With that map in place, Gemini can be configured to interpret HR data (role, department, location, seniority) and recommend a standardised access bundle. Instead of reacting to one-off emails, your teams are curating and improving a product: a predictable, role-based access experience that Gemini helps maintain and explain to stakeholders.

Use Gemini as the Single Front Door for New-Hire Access Questions

Slow onboarding is often amplified by information noise. New hires don’t know who to ask, HR doesn’t know the status of each IT task, and managers are unsure what has already been ordered. Strategically, you want one front door for all access-related questions. Gemini can become that interface, embedded in Google Chat, Gmail, or an intranet.

By connecting Gemini to HRIS data, ticketing systems, and identity platforms, you can let it answer “Do I have VPN access yet?”, “Which tools should I have as a new Sales Manager in Berlin?”, or “Who approves Salesforce access for me?” Gemini doesn’t replace your ITSM or IAM tools; it abstracts their complexity and keeps HR and employees away from low-value status chasing.

Align HR, IT, and Security on Policy Before You Automate

Before pushing Gemini into production, align HR, IT, and security on access policies: what is mandatory, what is optional, and what requires higher-level approval. AI can accelerate bad processes as easily as good ones, so you want consensus on the rules Gemini will help enforce or propose. This includes standard role-based access profiles, exception handling, and approval chains.

In our experience, the most successful teams treat this as a policy-design exercise first, automation second. Gemini then becomes the living documentation and execution layer for those policies, explaining to employees why they have (or don’t yet have) specific permissions and triggering the right workflows without manual interpretation each time.

Start with Observability: Let Gemini Analyse Bottlenecks First

Jumping straight into automation is tempting, but strategically it is smarter to start with bottleneck analysis. Connect Gemini to historical onboarding tickets, email threads, and HR data, and let it identify recurring delays: which roles suffer most, which tools are always late, where approvals stall. This diagnostic phase builds a shared fact base across HR and IT.

Once you know the real friction points, you can prioritise high-impact automations: for example, auto-triggering account creation when a contract is signed, or pre-approving low-risk tools for specific roles. Gemini can then recommend and simulate new rules before you commit to changes in identity or ticketing systems.

Invest in Change Management and Clear Ownership

Even the best AI onboarding assistant will fail if people do not trust or use it. Strategically, define clear ownership: who owns the Gemini access assistant, who maintains the prompts and policies, and how changes are approved. Make sure HR and IT both see the assistant as an asset, not as a competing channel to their existing tools.

Communicate to new hires and managers what Gemini can do (and what it cannot), and bake it into existing onboarding communication. Encourage teams to route repeated questions into Gemini instead of answering them manually. Over time, this creates a virtuous cycle: more usage leads to better training data and a more effective assistant.

Used strategically, Gemini can turn slow, opaque access provisioning into a predictable, data-driven onboarding experience. By treating access as a product, aligning policies, and letting Gemini orchestrate the flow between HR, IT, and identity systems, you reduce delays and give new hires a smooth start. At Reruption, we specialise in turning these ideas into working AI workflows inside real organisations; if you want to explore how Gemini could fit your HR stack, we’re ready to help you test it quickly and safely.

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Real-World Case Studies

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

Airbus

Aerospace

In aircraft design, computational fluid dynamics (CFD) simulations are essential for predicting airflow around wings, fuselages, and novel configurations critical to fuel efficiency and emissions reduction. However, traditional high-fidelity RANS solvers require hours to days per run on supercomputers, limiting engineers to just a few dozen iterations per design cycle and stifling innovation for next-gen hydrogen-powered aircraft like ZEROe. This computational bottleneck was particularly acute amid Airbus' push for decarbonized aviation by 2035, where complex geometries demand exhaustive exploration to optimize lift-drag ratios while minimizing weight. Collaborations with DLR and ONERA highlighted the need for faster tools, as manual tuning couldn't scale to test thousands of variants needed for laminar flow or blended-wing-body concepts.

Lösung

Machine learning surrogate models, including physics-informed neural networks (PINNs), were trained on vast CFD datasets to emulate full simulations in milliseconds. Airbus integrated these into a generative design pipeline, where AI predicts pressure fields, velocities, and forces, enforcing Navier-Stokes physics via hybrid loss functions for accuracy. Development involved curating millions of simulation snapshots from legacy runs, GPU-accelerated training, and iterative fine-tuning with experimental wind-tunnel data. This enabled rapid iteration: AI screens designs, high-fidelity CFD verifies top candidates, slashing overall compute by orders of magnitude while maintaining <5% error on key metrics.

Ergebnisse

  • Simulation time: 1 hour → 30 ms (120,000x speedup)
  • Design iterations: +10,000 per cycle in same timeframe
  • Prediction accuracy: 95%+ for lift/drag coefficients
  • 50% reduction in design phase timeline
  • 30-40% fewer high-fidelity CFD runs required
  • Fuel burn optimization: up to 5% improvement in predictions
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Mayo Clinic

Healthcare

As a leading academic medical center, Mayo Clinic manages millions of patient records annually, but early detection of heart failure remains elusive. Traditional echocardiography detects low left ventricular ejection fraction (LVEF <50%) only when symptomatic, missing asymptomatic cases that account for up to 50% of heart failure risks. Clinicians struggle with vast unstructured data, slowing retrieval of patient-specific insights and delaying decisions in high-stakes cardiology. Additionally, workforce shortages and rising costs exacerbate challenges, with cardiovascular diseases causing 17.9M deaths yearly globally. Manual ECG interpretation misses subtle patterns predictive of low EF, and sifting through electronic health records (EHRs) takes hours, hindering personalized medicine. Mayo needed scalable AI to transform reactive care into proactive prediction.

Lösung

Mayo Clinic deployed a deep learning ECG algorithm trained on over 1 million ECGs, identifying low LVEF from routine 10-second traces with high accuracy. This ML model extracts features invisible to humans, validated internally and externally. In parallel, a generative AI search tool via Google Cloud partnership accelerates EHR queries. Launched in 2023, it uses large language models (LLMs) for natural language searches, surfacing clinical insights instantly. Integrated into Mayo Clinic Platform, it supports 200+ AI initiatives. These solutions overcome data silos through federated learning and secure cloud infrastructure.

Ergebnisse

  • ECG AI AUC: 0.93 (internal), 0.92 (external validation)
  • Low EF detection sensitivity: 82% at 90% specificity
  • Asymptomatic low EF identified: 1.5% prevalence in screened population
  • GenAI search speed: 40% reduction in query time for clinicians
  • Model trained on: 1.1M ECGs from 44K patients
  • Deployment reach: Integrated in Mayo cardiology workflows since 2021
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JPMorgan Chase

Banking

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

Lösung

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

Ergebnisse

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

Healthcare

Sepsis, a life-threatening condition, poses a major threat in emergency departments, with delayed detection contributing to high mortality rates—up to 20-30% in severe cases. At UC San Diego Health, an academic medical center handling over 1 million patient visits annually, nonspecific early symptoms made timely intervention challenging, exacerbating outcomes in busy ERs . A randomized study highlighted the need for proactive tools beyond traditional scoring systems like qSOFA. Hospital capacity management and patient flow were further strained post-COVID, with bed shortages leading to prolonged admission wait times and transfer delays. Balancing elective surgeries, emergencies, and discharges required real-time visibility . Safely integrating generative AI, such as GPT-4 in Epic, risked data privacy breaches and inaccurate clinical advice . These issues demanded scalable AI solutions to predict risks, streamline operations, and responsibly adopt emerging tech without compromising care quality.

Lösung

UC San Diego Health implemented COMPOSER, a deep learning model trained on electronic health records to predict sepsis risk up to 6-12 hours early, triggering Epic Best Practice Advisory (BPA) alerts for nurses . This quasi-experimental approach across two ERs integrated seamlessly with workflows . Mission Control, an AI-powered operations command center funded by $22M, uses predictive analytics for real-time bed assignments, patient transfers, and capacity forecasting, reducing bottlenecks . Led by Chief Health AI Officer Karandeep Singh, it leverages data from Epic for holistic visibility. For generative AI, pilots with Epic's GPT-4 enable NLP queries and automated patient replies, governed by strict safety protocols to mitigate hallucinations and ensure HIPAA compliance . This multi-faceted strategy addressed detection, flow, and innovation challenges.

Ergebnisse

  • Sepsis in-hospital mortality: 17% reduction
  • Lives saved annually: 50 across two ERs
  • Sepsis bundle compliance: Significant improvement
  • 72-hour SOFA score change: Reduced deterioration
  • ICU encounters: Decreased post-implementation
  • Patient throughput: Improved via Mission Control
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Khan Academy

Education

Khan Academy faced the monumental task of providing personalized tutoring at scale to its 100 million+ annual users, many in under-resourced areas. Traditional online courses, while effective, lacked the interactive, one-on-one guidance of human tutors, leading to high dropout rates and uneven mastery. Teachers were overwhelmed with planning, grading, and differentiation for diverse classrooms. In 2023, as AI advanced, educators grappled with hallucinations and over-reliance risks in tools like ChatGPT, which often gave direct answers instead of fostering learning. Khan Academy needed an AI that promoted step-by-step reasoning without cheating, while ensuring equitable access as a nonprofit. Scaling safely across subjects and languages posed technical and ethical hurdles.

Lösung

Khan Academy developed Khanmigo, an AI-powered tutor and teaching assistant built on GPT-4, piloted in March 2023 for teachers and expanded to students. Unlike generic chatbots, Khanmigo uses custom prompts to guide learners Socratically—prompting questions, hints, and feedback without direct answers—across math, science, humanities, and more. The nonprofit approach emphasized safety guardrails, integration with Khan's content library, and iterative improvements via teacher feedback. Partnerships like Microsoft enabled free global access for teachers by 2024, now in 34+ languages. Ongoing updates, such as 2025 math computation enhancements, address accuracy challenges.

Ergebnisse

  • User Growth: 68,000 (2023-24 pilot) to 700,000+ (2024-25 school year)
  • Teacher Adoption: Free for teachers in most countries, millions using Khan Academy tools
  • Languages Supported: 34+ for Khanmigo
  • Engagement: Improved student persistence and mastery in pilots
  • Time Savings: Teachers save hours on lesson planning and prep
  • Scale: Integrated with 429+ free courses in 43 languages
Read case study →

Best Practices

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

Connect Gemini to Your HRIS and Google Workspace as the Foundation

The first tactical step is to connect Gemini to the systems that hold your core onboarding data. In a Google-centric environment, that typically means your HRIS (for role, location, start date) and Google Workspace (for email, groups, and basic access). Use secure connectors or APIs so Gemini can read, but not arbitrarily write, to these systems during the initial phase.

Once connected, configure Gemini to answer basic questions like “When is my start date?”, “Who is my manager?”, and “Which Google Groups am I part of?”. This will free HR from a large chunk of repetitive queries even before you start touching access provisioning workflows.

Use Gemini to Generate Role-Based Access Bundles

Define standard access bundles for common roles (e.g. Sales Manager, Backend Engineer, HR Business Partner). Store these bundles in a structured format (e.g. a Google Sheet or a lightweight configuration database) that Gemini can query. Each bundle should define systems, groups, and permissions required.

Then prompt Gemini to recommend the correct bundle based on HRIS data and to create a human-readable summary that HR and managers can validate:

System prompt example:
You are an HR onboarding and access provisioning assistant.
You receive employee data (role, department, location, seniority) and a catalogue of access bundles.
Your tasks:
1) Select the most appropriate access bundle(s) for the employee.
2) Explain in clear business language what access will be granted and why.
3) Flag any access that requires additional approval.
Respond in JSON with fields: selected_bundles, explanation, approvals_required.

Expected outcome: HR can quickly review and approve Gemini’s suggestion, reducing manual decision-making and inconsistencies across hires.

Automate Ticket Creation and Routing from Gemini Conversations

Once Gemini can suggest access bundles, connect it to your ITSM or ticketing tool (e.g. Jira Service Management, ServiceNow, or a Google Chat-based workflow) to automatically create structured tickets. Use consistent templates, so IT receives all necessary information without back-and-forth emails.

Example Gemini prompt for ticket creation:
You are integrated with the IT ticketing API.
Given the selected access bundle and employee details, generate
separate tickets for:
- Hardware (laptop, accessories)
- Core accounts (email, SSO)
- Business apps (CRM, ERP, HR tools)
Include: due_date (before start date), priority, and approver.
Return a JSON array of ticket objects ready for the API.

Expected outcome: new hires trigger a single HR action (or even automatic action on contract signature), and Gemini fans out well-structured tickets to the right queues, cutting manual coordination time dramatically.

Deploy a New-Hire Gemini Assistant in Google Chat or Intranet

Expose Gemini to new hires directly in the channels they already use, such as Google Chat, Gmail side panel, or your intranet. Give it a clear scope: answer onboarding questions, surface status of access requests, and allow new hires to request missing permissions through a guided flow.

Example Gemini new-hire assistant prompt:
You are a new-hire onboarding and access assistant.
Goals:
- Answer questions about onboarding tasks and IT access.
- Show current status of laptop, accounts, and tool provisioning.
- Collect clear information when the employee requests additional access.
Always:
- Use simple language.
- Link to the relevant internal page or policy when available.
- Escalate to HR or IT if the question is out of scope or policy is unclear.

Expected outcome: fewer direct emails to HR and IT, faster answers for employees, and a consistent onboarding communication experience.

Let Gemini Monitor SLAs and Escalate Delays Proactively

Define realistic SLAs for each onboarding asset (e.g. laptop ready 3 days before start, core accounts ready 1 day before, business apps within 2 days after start). Give Gemini read access to ticket statuses and timestamps so it can calculate whether you are on track or at risk.

Configure Gemini to send proactive alerts when SLAs are threatened. For example, if a laptop ticket is still unassigned 5 days before start, Gemini pings the IT queue owner and HR with a concise summary and suggested next steps.

Example monitoring prompt for Gemini:
You monitor onboarding tickets with SLA targets.
Every hour, you receive updated ticket data.
For each ticket, determine:
- Is it on track, at risk, or breached?
- Who needs to be notified (IT, HR, manager)?
Compose a short status message and recommended action.
Only escalate when there is a clear SLA risk.

Expected outcome: fewer last-minute surprises on day one, higher SLA adherence, and better transparency for HR and managers.

Capture Exceptions and Use Them to Improve Policies

Not every new hire fits a standard bundle. Use Gemini to capture exception requests (e.g. special tools for a senior architect) in a structured way and log the reasoning behind approvals. Over time, analyse these exceptions with Gemini to identify patterns and propose updates to your standard bundles or policies.

For example, you can have Gemini periodically review exception tickets and answer: “Which roles most often request non-standard tools?” or “Which exceptions are always approved and should become standard?” This closes the feedback loop between day-to-day onboarding operations and policy evolution.

When implemented step by step, these Gemini onboarding best practices can realistically reduce manual HR/IT coordination time by 30–50%, cut average access delays from days to hours for many roles, and improve new-hire satisfaction scores in the first 30–60 days. The exact metrics will depend on your starting point, but the pattern is consistent: less chasing, clearer accountability, and faster time-to-productivity.

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Frequently Asked Questions

Gemini speeds up onboarding access provisioning by sitting between HR data, identity systems, and IT ticketing. It can read new-hire information from your HRIS, suggest the right role-based access bundle, and automatically create well-structured tickets for hardware, accounts, and tools.

On top of that, Gemini acts as a conversational interface for new hires and HR: it answers status questions, collects missing information, and nudges IT when SLAs are at risk. This reduces manual email ping-pong and ensures that provisioning work starts earlier and runs more consistently.

You don’t need a large data science team to start. Most implementations require:

  • An HR or People Ops lead who understands your current onboarding process and policies.
  • An IT/identity owner who can provide access to systems like HRIS, Google Workspace, and your ticketing tool.
  • A small engineering capacity (internal or external) to set up secure integrations and basic workflows.

Gemini itself handles the natural language and reasoning layer; the main work is defining clear access rules, mapping your current process, and connecting Gemini via APIs or existing connectors. Reruption typically helps clients compress this into a focused PoC rather than a long IT project.

If the scope is focused, you can see meaningful results in weeks, not months. A realistic timeline looks like:

  • Week 1–2: Map current onboarding flows, define target access bundles, connect Gemini to test data.
  • Week 3–4: Deploy a pilot Gemini assistant for HR only (recommend bundles, generate tickets, analyse bottlenecks).
  • Week 5–8: Extend to a limited group of new hires and managers, add monitoring and SLA alerts.

Improvements often show up immediately as fewer status emails and clearer ticket quality. Time-to-access and new-hire satisfaction usually improve over the first 1–2 onboarding cycles as you refine workflows and bundles.

ROI comes from three main areas: reduced manual effort, faster time-to-productivity, and better retention. Automating access decisions and ticket creation can easily save HR and IT several hours per hire. If you onboard dozens or hundreds of people per year, that becomes a substantial cost reduction.

More importantly, getting laptops and accounts ready on time shortens the unproductive phase of a new hire’s journey. If Gemini helps each employee become productive even one day earlier, the productivity gain across the workforce can outweigh the implementation costs quickly. Finally, smoother onboarding positively affects employer brand and early attrition, which are significant hidden costs for many organisations.

Reruption works as a Co-Preneur inside your organisation: we don’t just advise, we build. With our AI PoC offering (9,900€), we can quickly test whether a Gemini-based onboarding assistant works with your real HR and IT stack. That includes scoping the use case, selecting the right architecture, prototyping the workflows, and measuring performance.

Beyond the PoC, we help you turn the prototype into a robust internal product: integrating with HRIS and Google Workspace, refining prompts and policies, and setting up monitoring and governance. Our focus on AI Strategy, AI Engineering, Security & Compliance, and Enablement ensures that the solution is not just a demo, but a reliable part of your onboarding process.

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