The Challenge: Slow Access and Account Provisioning

For many organisations, the most frustrating part of onboarding is also the most basic: getting new hires the tools they need to work. Laptops arrive late, accounts are created manually, and access to core systems depends on long email chains between HR, IT and line managers. New employees spend their first days waiting for logins instead of contributing value.

Traditional approaches rely on static checklists, ticket portals and spreadsheets owned by HR or IT. Every exception – a special role, a new SaaS tool, a change in policy – adds more manual decision-making and follow-up. HR ends up chasing status updates, managers are unclear what they must approve, and IT teams process poorly specified tickets. None of this scales when you hire across locations, roles and contract types.

The business impact is larger than a few lost days. Slow access and account provisioning increases time-to-productivity, frustrates new hires, and weakens your employer brand. Line managers lose trust in HR and IT. Security risk rises as shortcuts appear: shared logins, ad-hoc access, and undocumented exceptions. Over time, these delays become a structural drag on growth and a silent competitive disadvantage in talent markets.

Yet this is a highly solvable problem. With the right AI-powered onboarding assistant, you can orchestrate access, standardise decisions, and keep everyone informed in real time. At Reruption, we’ve seen how intelligent workflows and conversational interfaces can replace manual coordination in similarly complex processes. In the rest of this guide, you’ll find practical steps to use Claude to turn access provisioning from a bottleneck into a predictable, fast, and positive part of the onboarding experience.

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Our Assessment

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

From Reruption’s perspective, Claude as an HR onboarding assistant is particularly well suited to the access and account provisioning problem. We’ve implemented AI solutions that sit between business teams and technical systems, turning messy processes into guided workflows. The same pattern works here: Claude can translate HR and manager intent into structured requests, interact with HRIS and IAM APIs, and keep new hires informed without adding manual work for HR or IT.

Treat Access Provisioning as an Experience, Not Just an IT Task

Strategically, slow access provisioning is often viewed as an IT backlog issue, when it’s actually a core part of the employee onboarding experience. HR should own the end-to-end journey, with Claude acting as a single conversational layer that new hires, managers and IT can all interact with – instead of scattered emails and portals. This reframing clarifies that the goal is not only faster ticket closure, but a smoother first week for every new colleague.

When you design your Claude use case, start from the new hire’s perspective: what do they need to accomplish in day 1, 3, 7 and 30, and which systems and permissions are required at each step? Map this journey first, then align IT workflows underneath. This mindset prevents you from simply bolting AI on top of broken processes.

Start with Clear Rules and Guardrails for Access Decisions

Claude is powerful at interpreting unstructured requests, but role-based access must remain governed by clear policies. Strategically, invest time in defining which systems and permission sets correspond to which roles, seniority levels and locations. Claude can then use these rules to automatically assemble access packages when a new hire joins or changes role.

Instead of letting Claude “decide” access, let it apply predefined policies, flag exceptions, and route them to the right approver. This limits risk, reduces the need for AI expertise in HR or IT, and makes audit and compliance discussions far easier. You want Claude to be an intelligent coordinator, not an unbounded decision-maker.

Make HR, IT and Security Co-Owners of the AI Assistant

Many onboarding initiatives fail because they live solely in HR or solely in IT. For Claude to streamline access and account provisioning, you need a joint ownership model. HR defines the onboarding journey and communication tone. IT owns the integration to ticketing, HRIS and IAM systems. Security sets the rules around data access, logging and approval flows.

From a readiness perspective, ensure you have stakeholders from each function at the table early. Align them on success metrics (e.g. time-to-access, fewer escalations, policy compliance) and on what Claude is allowed to do autonomously versus where human approval is mandatory. This upfront alignment will reduce friction when you scale the solution across business units.

Use a Pilot to De-Risk Integrations and Change Management

AI onboarding assistants touch sensitive systems and cross-team workflows. A strategic way to de-risk is to launch Claude in a focused pilot: for example, a specific country, business unit or role family with relatively standard access needs. This lets you validate Claude’s orchestration capabilities and fine-tune prompts, policies and API calls before going enterprise-wide.

During the pilot, track not only performance metrics but also qualitative feedback: Are new hires clearer on what will happen when? Do managers feel better supported? Are IT teams receiving better-structured requests? This approach allows you to adjust both the technical configuration and the communication approach before formal rollout and change management.

Plan for Governance, Monitoring and Continuous Improvement

Once Claude is embedded in onboarding, it becomes part of your critical infrastructure. Strategically, that means treating it like a product: define ownership, SLAs and a process for updating rules and prompts as your tool landscape evolves. HR and IT should regularly review access provisioning analytics: time to create accounts, error rates, manual overrides, and policy exceptions.

Claude can also assist in its own governance by summarising edge cases, suggesting policy improvements, and highlighting recurring exceptions. Building this feedback loop into your operating model ensures the assistant remains aligned with security requirements, new applications and organisational changes, rather than drifting out of sync over time.

Used thoughtfully, Claude can turn slow, opaque access provisioning into a predictable, guided onboarding flow where new hires always know what happens next and IT receives clean, policy-compliant requests. The key is to pair Claude’s conversational and orchestration strengths with clear rules, cross-functional ownership and ongoing monitoring. If you want support in designing and implementing this kind of solution, Reruption brings hands-on AI engineering and HR process expertise to build a working proof-of-concept quickly and then scale it with confidence.

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Real-World Case Studies

From Energy to Logistics: Learn how companies successfully use Claude.

Shell

Energy

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

Lösung

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

Ergebnisse

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

Maersk

Shipping

In the demanding world of maritime logistics, Maersk, the world's largest container shipping company, faced significant challenges from unexpected ship engine failures. These failures, often due to wear on critical components like two-stroke diesel engines under constant high-load operations, led to costly delays, emergency repairs, and multimillion-dollar losses in downtime. With a fleet of over 700 vessels traversing global routes, even a single failure could disrupt supply chains, increase fuel inefficiency, and elevate emissions . Suboptimal ship operations compounded the issue. Traditional fixed-speed routing ignored real-time factors like weather, currents, and engine health, resulting in excessive fuel consumption—which accounts for up to 50% of operating costs—and higher CO2 emissions. Delays from breakdowns averaged days per incident, amplifying logistical bottlenecks in an industry where reliability is paramount .

Lösung

Maersk tackled these issues with machine learning (ML) for predictive maintenance and optimization. By analyzing vast datasets from engine sensors, AIS (Automatic Identification System), and meteorological data, ML models predict failures days or weeks in advance, enabling proactive interventions. This integrates with route and speed optimization algorithms that dynamically adjust voyages for fuel efficiency . Implementation involved partnering with tech leaders like Wärtsilä for fleet solutions and internal digital transformation, using MLOps for scalable deployment across the fleet. AI dashboards provide real-time insights to crews and shore teams, shifting from reactive to predictive operations .

Ergebnisse

  • Fuel consumption reduced by 5-10% through AI route optimization
  • Unplanned engine downtime cut by 20-30%
  • Maintenance costs lowered by 15-25%
  • Operational efficiency improved by 10-15%
  • CO2 emissions decreased by up to 8%
  • Predictive accuracy for failures: 85-95%
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Amazon

Retail

In the vast e-commerce landscape, online shoppers face significant hurdles in product discovery and decision-making. With millions of products available, customers often struggle to find items matching their specific needs, compare options, or get quick answers to nuanced questions about features, compatibility, and usage. Traditional search bars and static listings fall short, leading to shopping cart abandonment rates as high as 70% industry-wide and prolonged decision times that frustrate users. Amazon, serving over 300 million active customers, encountered amplified challenges during peak events like Prime Day, where query volumes spiked dramatically. Shoppers demanded personalized, conversational assistance akin to in-store help, but scaling human support was impossible. Issues included handling complex, multi-turn queries, integrating real-time inventory and pricing data, and ensuring recommendations complied with safety and accuracy standards amid a $500B+ catalog.

Lösung

Amazon developed Rufus, a generative AI-powered conversational shopping assistant embedded in the Amazon Shopping app and desktop. Rufus leverages a custom-built large language model (LLM) fine-tuned on Amazon's product catalog, customer reviews, and web data, enabling natural, multi-turn conversations to answer questions, compare products, and provide tailored recommendations. Powered by Amazon Bedrock for scalability and AWS Trainium/Inferentia chips for efficient inference, Rufus scales to millions of sessions without latency issues. It incorporates agentic capabilities for tasks like cart addition, price tracking, and deal hunting, overcoming prior limitations in personalization by accessing user history and preferences securely. Implementation involved iterative testing, starting with beta in February 2024, expanding to all US users by September, and global rollouts, addressing hallucination risks through grounding techniques and human-in-loop safeguards.

Ergebnisse

  • 60% higher purchase completion rate for Rufus users
  • $10B projected additional sales from Rufus
  • 250M+ customers used Rufus in 2025
  • Monthly active users up 140% YoY
  • Interactions surged 210% YoY
  • Black Friday sales sessions +100% with Rufus
  • 149% jump in Rufus users recently
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Waymo (Alphabet)

Transportation

Developing fully autonomous ride-hailing demanded overcoming extreme challenges in AI reliability for real-world roads. Waymo needed to master perception—detecting objects in fog, rain, night, or occlusions using sensors alone—while predicting erratic human behaviors like jaywalking or sudden lane changes. Planning complex trajectories in dense, unpredictable urban traffic, and precise control to execute maneuvers without collisions, required near-perfect accuracy, as a single failure could be catastrophic . Scaling from tests to commercial fleets introduced hurdles like handling edge cases (e.g., school buses with stop signs, emergency vehicles), regulatory approvals across cities, and public trust amid scrutiny. Incidents like failing to stop for school buses highlighted software gaps, prompting recalls. Massive data needs for training, compute-intensive models, and geographic adaptation (e.g., right-hand vs. left-hand driving) compounded issues, with competitors struggling on scalability .

Lösung

Waymo's Waymo Driver stack integrates deep learning end-to-end: perception fuses lidar, radar, and cameras via convolutional neural networks (CNNs) and transformers for 3D object detection, tracking, and semantic mapping with high fidelity. Prediction models forecast multi-agent behaviors using graph neural networks and video transformers trained on billions of simulated and real miles . For planning, Waymo applied scaling laws—larger models with more data/compute yield power-law gains in forecasting accuracy and trajectory quality—shifting from rule-based to ML-driven motion planning for human-like decisions. Control employs reinforcement learning and model-predictive control hybridized with neural policies for smooth, safe execution. Vast datasets from 96M+ autonomous miles, plus simulations, enable continuous improvement; recent AI strategy emphasizes modular, scalable stacks .

Ergebnisse

  • 450,000+ weekly paid robotaxi rides (Dec 2025)
  • 96 million autonomous miles driven (through June 2025)
  • 3.5x better avoiding injury-causing crashes vs. humans
  • 2x better avoiding police-reported crashes vs. humans
  • Over 71M miles with detailed safety crash analysis
  • 250,000 weekly rides (April 2025 baseline, since doubled)
Read case study →

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
Read case study →

Best Practices

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

Use Claude to Generate Role-Based Access Packages from HR Data

A practical first step is to have Claude translate HRIS data into structured access packages. When a new hire is created in your HR system, send Claude a payload with key fields such as role, department, location, seniority and contract type. Claude can then apply your access rules to recommend which systems, groups and permission levels are required.

You can expose this via an internal chat interface for HR or managers. They paste or confirm the new hire details, and Claude responds with a precise access plan that can be pushed into your ITSM or IAM system via API.

System prompt example for Claude:
You are an HR onboarding and access provisioning assistant.

Goal:
- Based on the new hire data I send you, propose a structured list of
  systems and permission groups they should receive.
- Use the company's access policy below.

Access policy (excerpt):
- Sales Managers (EU): CRM (SalesManager role), Email, Slack Sales channel,
  Finance reporting (read-only), SSO to SalesAcademy.
- Software Engineers (DE): GitHub (Engineer role), Jira (Developer),
  Confluence (Standard), VPN, SSO to CI/CD.

When I send you new hire data, respond in valid JSON with fields:
- systems: [list of systems]
- groups: [list of permission groups]
- approvals_required: [list of managers/roles]

Expected outcome: HR and managers get consistent, policy-aligned access plans within seconds, reducing back-and-forth with IT and cutting errors in initial provisioning.

Automate Ticket Creation and Status Updates via ITSM and IAM APIs

Once Claude generates an access package, connect it to your ITSM (e.g. ServiceNow, Jira Service Management) and IAM (e.g. Azure AD, Okta) via APIs. Claude should not directly create accounts, but it can generate fully specified tickets and API calls that your backend services execute.

For example, when an offer is accepted, HR triggers a “provision access” event. Claude composes a set of API requests: create a user in the directory, assign them to the correct groups, and open any necessary hardware or manual approval tickets. As status changes in these systems, feed updates back to Claude so it can keep new hires and managers informed.

Example workflow configuration (pseudo-code):
On HRIS event: NewHireCreated
  - Call Claude with new hire JSON and access policy
  - Receive structured access_plan JSON
  - For each system in access_plan.systems:
      - Create ITSM ticket with all required fields
      - Or call IAM API to assign standard groups
  - Store mapping of tickets to new hire ID

On ITSM event: TicketStatusChanged
  - Update status store
  - Notify Claude so it can answer status queries

Expected outcome: IT receives complete, standardised requests; new hires and managers can ask Claude for real-time status instead of emailing HR or IT.

Provide New Hires with a Claude-Powered Onboarding Chat

Deploy Claude as a chat assistant (in Teams, Slack, or your intranet) dedicated to onboarding and access questions. Connect it to your HR knowledge base, policies, and the status data from your ITSM/IAM integrations. New hires can ask, “Do I already have access to the CRM?” or “When will my laptop arrive?” and receive accurate, contextual answers.

Configure Claude with guardrails: it should only show status data for the authenticated user and respond with human escalation options when something is stuck (e.g. “It looks like your VPN access is still pending manager approval. I can remind your manager or you can contact IT here.”).

Example user-facing prompt:
You are the onboarding assistant for new employees.

Capabilities:
- You can see the user's own onboarding tasks and access status
  (provided as structured JSON in the system messages).
- You can answer HR onboarding FAQs based on the knowledge base.

When answering:
- Be concise and action-oriented.
- If a task is blocked, explain why and what will happen next.
- Never reveal data about other employees.

Expected outcome: Reduction in repetitive HR and IT questions, increased clarity for new hires, and a more professional, guided first-week experience.

Embed Manager Approval Flows Directly into Claude Conversations

Often, access provisioning stalls because managers don’t respond to approval emails or are unsure which permissions to grant. Use Claude to streamline this by pushing approval requests directly to managers via chat, email or your HR portal, with one-click options.

Claude can explain the default access package based on role and highlight any higher-risk permissions. Managers can approve the standard set or request changes through the same interface. Claude then updates the access plan and triggers the necessary backend actions.

Example approval message Claude sends to managers:
"A new team member, Jane Doe (Sales Manager, EU), starts on 15 March.
Based on our policy, we plan to grant:
- CRM: SalesManager role
- Email & calendar
- Slack: #sales-europe, #company-announcements
- Finance reporting: read-only

Reply with one of the following:
- APPROVE STANDARD
- APPROVE WITH CHANGES: <describe changes>
- ESCALATE TO IT SECURITY

If no response in 48 hours, I will send a reminder."

Expected outcome: Faster manager decisions, fewer over-privileged accounts, and clear documentation of who approved what, when.

Use Claude to Detect Risks and Anomalies in Access Requests

Beyond automation, Claude can help strengthen security and compliance. Feed it logs of provisioned access and requests over time and ask it to surface anomalies: roles frequently requesting non-standard permissions, inconsistent group assignments within the same job family, or tools that are being granted without an owner.

You can run this as a scheduled batch analysis or trigger it when certain thresholds are met (e.g. a new hire receives more than X privileged roles). Claude can summarise potential issues for IT security or HR to review, along with suggested policy updates.

Example analysis prompt for periodic review:
You are assisting with an access governance review.

Input:
- A list of new hire access packages for the last 90 days
- The official access policy for each role

Tasks:
1) Identify patterns where actual access deviates from policy.
2) Highlight potential over-privileged accounts.
3) Suggest specific policy updates or training topics for managers.

Respond with:
- Summary (plain language for HR leadership)
- Detailed findings (table-like markdown)
- Recommended next steps.

Expected outcome: Reduced risk of access sprawl, better alignment between policy and practice, and more informed conversations between HR, IT and security.

Track KPIs and Feed Them Back into Claude’s Workflow Logic

To make the most of Claude in onboarding, define and track clear onboarding KPIs related to access: time from contract signature to all mandatory accounts active, percentage of new hires fully provisioned before day one, number of access-related support tickets per hire, and average time to resolve access issues.

Store these metrics centrally and periodically summarise them with Claude to identify bottlenecks by role, location or system. Over time, you can adjust triggers (e.g. starting provisioning earlier for specific roles), refine prompts, or update rules to meet target SLAs.

Expected outcomes: Typically, organisations implementing these practices with Claude can aim for 30–60% faster access provisioning for standard roles, a substantial drop in onboarding-related IT tickets, and a clear, measurable improvement in time-to-productivity for new hires.

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Frequently Asked Questions

Claude accelerates access and account provisioning by acting as an intelligent coordinator between HR, managers, IT and your HRIS/IAM systems. It uses new hire data (role, department, location, start date) to assemble standardised access packages based on your policies, generates fully specified tickets or API calls for IT, and keeps new hires informed on progress via chat.

Instead of HR manually emailing IT and tracking spreadsheets, Claude turns these steps into automated workflows. The result is faster, more consistent provisioning and fewer status inquiries landing on HR and IT desks.

To use Claude effectively for this problem, you typically need access to three types of systems via API: your HRIS (for new hire and role data), your ITSM or ticketing system (for hardware and manual tasks), and your IAM/directory (for accounts, groups and permissions). In many organisations, these are already in place; the missing piece is a smart orchestration layer.

Claude connects to these systems through a backend service that Reruption or your internal IT team sets up. Claude itself focuses on interpreting policies, assembling access packages and generating structured requests, while the integrations handle execution and authentication.

A focused pilot for onboarding and access provisioning can often be delivered in a few weeks, assuming APIs are available. A typical timeline is: 1–2 weeks for discovery and policy mapping, 1–2 weeks for building prompts, workflows and integrations for a limited scope (e.g. one country or role family), and another 1–2 weeks for testing and refinement with real hires.

You’ll need input from HR (on the onboarding journey and communication), IT (for systems and integrations), and security/compliance (for policies and guardrails). Reruption usually works with a small cross-functional squad to keep decisions fast and the implementation practical.

Most organisations can expect measurable improvements in both efficiency and experience. Typical outcomes include 30–60% faster provisioning for standard roles, a significant reduction in access-related support tickets, and fewer delays in new hires reaching basic productivity milestones. For HR and IT, this translates into reclaimed capacity previously spent on coordination and manual follow-up.

ROI comes from multiple sources: reduced time-to-productivity for new hires, lower manual workload in HR/IT, fewer access errors and escalations, and a stronger employer brand due to a smoother first week. These benefits are easy to quantify once you track baseline and post-implementation KPIs such as time-to-access and ticket volume per hire.

Reruption supports you end-to-end, from identifying the right employee onboarding and access use cases to shipping a working solution. With our 9.900€ AI PoC, we quickly validate that Claude can orchestrate your HRIS, ITSM and IAM stack for a defined scope, including a functioning prototype, performance metrics and a concrete rollout plan.

Beyond the PoC, our Co-Preneur approach means we embed with your HR and IT teams like co-founders: we help refine policies, design workflows, build and secure the integrations, and support change management until the assistant is truly adopted. We operate in your P&L, not just in slide decks, ensuring that Claude delivers real impact on time-to-productivity and the new-hire experience.

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