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 Telecommunications to Healthcare: Learn how companies successfully use Gemini.

Ooredoo (Qatar)

Telecommunications

Ooredoo Qatar, Qatar's leading telecom operator, grappled with the inefficiencies of manual Radio Access Network (RAN) optimization and troubleshooting. As 5G rollout accelerated, traditional methods proved time-consuming and unscalable , struggling to handle surging data demands, ensure seamless connectivity, and maintain high-quality user experiences amid complex network dynamics . Performance issues like dropped calls, variable data speeds, and suboptimal resource allocation required constant human intervention, driving up operating expenses (OpEx) and delaying resolutions. With Qatar's National Digital Transformation agenda pushing for advanced 5G capabilities, Ooredoo needed a proactive, intelligent approach to RAN management without compromising network reliability .

Lösung

Ooredoo partnered with Ericsson to deploy cloud-native Ericsson Cognitive Software on Microsoft Azure, featuring a digital twin of the RAN combined with deep reinforcement learning (DRL) for AI-driven optimization . This solution creates a virtual network replica to simulate scenarios, analyze vast RAN data in real-time, and generate proactive tuning recommendations . The Ericsson Performance Optimizers suite was trialed in 2022, evolving into full deployment by 2023, enabling automated issue resolution and performance enhancements while integrating seamlessly with Ooredoo's 5G infrastructure . Recent expansions include energy-saving PoCs, further leveraging AI for sustainable operations .

Ergebnisse

  • 15% reduction in radio power consumption (Energy Saver PoC)
  • Proactive RAN optimization reducing troubleshooting time
  • Maintained high user experience during power savings
  • Reduced operating expenses via automated resolutions
  • Enhanced 5G subscriber experience with seamless connectivity
  • 10% spectral efficiency gains (Ericsson AI RAN benchmarks)
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Walmart (Marketplace)

Retail

In the cutthroat arena of Walmart Marketplace, third-party sellers fiercely compete for the Buy Box, which accounts for the majority of sales conversions . These sellers manage vast inventories but struggle with manual pricing adjustments, which are too slow to keep pace with rapidly shifting competitor prices, demand fluctuations, and market trends. This leads to frequent loss of the Buy Box, missed sales opportunities, and eroded profit margins in a platform where price is the primary battleground . Additionally, sellers face data overload from monitoring thousands of SKUs, predicting optimal price points, and balancing competitiveness against profitability. Traditional static pricing strategies fail in this dynamic e-commerce environment, resulting in suboptimal performance and requiring excessive manual effort—often hours daily per seller . Walmart recognized the need for an automated solution to empower sellers and drive platform growth.

Lösung

Walmart launched the Repricer, a free AI-driven automated pricing tool integrated into Seller Center, leveraging generative AI for decision support alongside machine learning models like sequential decision intelligence to dynamically adjust prices in real-time . The tool analyzes competitor pricing, historical sales data, demand signals, and market conditions to recommend and implement optimal prices that maximize Buy Box eligibility and sales velocity . Complementing this, the Pricing Insights dashboard provides account-level metrics and AI-generated recommendations, including suggested prices for promotions, helping sellers identify opportunities without manual analysis . For advanced users, third-party tools like Biviar's AI repricer—commissioned by Walmart—enhance this with reinforcement learning for profit-maximizing daily pricing decisions . This ecosystem shifts sellers from reactive to proactive pricing strategies.

Ergebnisse

  • 25% increase in conversion rates from dynamic AI pricing
  • Higher Buy Box win rates through real-time competitor analysis
  • Maximized sales velocity for 3rd-party sellers on Marketplace
  • 850 million catalog data improvements via GenAI (broader impact)
  • 40%+ conversion boost potential from AI-driven offers
  • Reduced manual pricing time by hours daily per seller
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FedEx

Logistics

FedEx faced suboptimal truck routing challenges in its vast logistics network, where static planning led to excess mileage, inflated fuel costs, and higher labor expenses . Handling millions of packages daily across complex routes, traditional methods struggled with real-time variables like traffic, weather disruptions, and fluctuating demand, resulting in inefficient vehicle utilization and delayed deliveries . These inefficiencies not only drove up operational costs but also increased carbon emissions and undermined customer satisfaction in a highly competitive shipping industry. Scaling solutions for dynamic optimization across thousands of trucks required advanced computational approaches beyond conventional heuristics .

Lösung

Machine learning models integrated with heuristic optimization algorithms formed the core of FedEx's AI-driven route planning system, enabling dynamic route adjustments based on real-time data feeds including traffic, weather, and package volumes . The system employs deep learning for predictive analytics alongside heuristics like genetic algorithms to solve the vehicle routing problem (VRP) efficiently, balancing loads and minimizing empty miles . Implemented as part of FedEx's broader AI supply chain transformation, the solution dynamically reoptimizes routes throughout the day, incorporating sense-and-respond capabilities to adapt to disruptions and enhance overall network efficiency .

Ergebnisse

  • 700,000 excess miles eliminated daily from truck routes
  • Multi-million dollar annual savings in fuel and labor costs
  • Improved delivery time estimate accuracy via ML models
  • Enhanced operational efficiency reducing costs industry-wide
  • Boosted on-time performance through real-time optimizations
  • Significant reduction in carbon footprint from mileage savings
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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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Commonwealth Bank of Australia (CBA)

Banking

As Australia's largest bank, CBA faced escalating scam and fraud threats, with customers suffering significant financial losses. Scammers exploited rapid digital payments like PayID, where mismatched payee names led to irreversible transfers. Traditional detection lagged behind sophisticated attacks, resulting in high customer harm and regulatory pressure. Simultaneously, contact centers were overwhelmed, handling millions of inquiries on fraud alerts and transactions. This led to long wait times, increased operational costs, and strained resources. CBA needed proactive, scalable AI to intervene in real-time while reducing reliance on human agents.

Lösung

CBA deployed a hybrid AI stack blending machine learning for anomaly detection and generative AI for personalized warnings. NameCheck verifies payee names against PayID in real-time, alerting users to mismatches. CallerCheck authenticates inbound calls, blocking impersonation scams. Partnering with H2O.ai, CBA implemented GenAI-driven predictive models for scam intelligence. An AI virtual assistant in the CommBank app handles routine queries, generates natural responses, and escalates complex issues. Integration with Apate.ai provides near real-time scam intel, enhancing proactive blocking across channels.

Ergebnisse

  • 70% reduction in scam losses
  • 50% cut in customer fraud losses by 2024
  • 30% drop in fraud cases via proactive warnings
  • 40% reduction in contact center wait times
  • 95%+ accuracy in NameCheck payee matching
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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