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 Aerospace to Banking: Learn how companies successfully use Claude.

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
Read case study →

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)
Read case study →

Nubank (Pix Payments)

Payments

Nubank, Latin America's largest digital bank serving over 114 million customers across Brazil, Mexico, and Colombia, faced the challenge of scaling its Pix instant payment system amid explosive growth. Traditional Pix transactions required users to navigate the app manually, leading to friction, especially for quick, on-the-go payments. This app navigation bottleneck increased processing time and limited accessibility for users preferring conversational interfaces like WhatsApp, where 80% of Brazilians communicate daily. Additionally, enabling secure, accurate interpretation of diverse inputs—voice commands, natural language text, and images (e.g., handwritten notes or receipts)—posed significant hurdles. Nubank needed to overcome accuracy issues in multimodal understanding, ensure compliance with Brazil's Central Bank regulations, and maintain trust in a high-stakes financial environment while handling millions of daily transactions.

Lösung

Nubank deployed a multimodal generative AI solution powered by OpenAI models, allowing customers to initiate Pix payments through voice messages, text instructions, or image uploads directly in the app or WhatsApp. The AI processes speech-to-text, natural language processing for intent extraction, and optical character recognition (OCR) for images, converting them into executable Pix transfers. Integrated seamlessly with Nubank's backend, the system verifies user identity, extracts key details like amount and recipient, and executes transactions in seconds, bypassing traditional app screens. This AI-first approach enhances convenience, speed, and safety, scaling operations without proportional human intervention.

Ergebnisse

  • 60% reduction in transaction processing time
  • Tested with 2 million users by end of 2024
  • Serves 114 million customers across 3 countries
  • Testing initiated August 2024
  • Processes voice, text, and image inputs for Pix
  • Enabled instant payments via WhatsApp integration
Read case study →

Insilico Medicine

Biotech

The drug discovery process traditionally spans 10-15 years and costs upwards of $2-3 billion per approved drug, with over 90% failure rate in clinical trials due to poor efficacy, toxicity, or ADMET issues. In idiopathic pulmonary fibrosis (IPF), a fatal lung disease with limited treatments like pirfenidone and nintedanib, the need for novel therapies is urgent, but identifying viable targets and designing effective small molecules remains arduous, relying on slow high-throughput screening of existing libraries. Key challenges include target identification amid vast biological data, de novo molecule generation beyond screened compounds, and predictive modeling of properties to reduce wet-lab failures. Insilico faced skepticism on AI's ability to deliver clinically viable candidates, regulatory hurdles for AI-discovered drugs, and integration of AI with experimental validation.

Lösung

Insilico deployed its end-to-end Pharma.AI platform, integrating generative AI and deep learning for accelerated discovery. PandaOmics used multimodal deep learning on omics data to nominate novel targets like TNIK kinase for IPF, prioritizing based on disease relevance and druggability. Chemistry42 employed generative models (GANs, reinforcement learning) to design de novo molecules, generating and optimizing millions of novel structures with desired properties, while InClinico predicted preclinical outcomes. This AI-driven pipeline overcame traditional limitations by virtual screening vast chemical spaces and iterating designs rapidly. Validation through hybrid AI-wet lab approaches ensured robust candidates like ISM001-055 (Rentosertib).

Ergebnisse

  • Time from project start to Phase I: 30 months (vs. 5+ years traditional)
  • Time to IND filing: 21 months
  • First generative AI drug to enter Phase II human trials (2023)
  • Generated/optimized millions of novel molecules de novo
  • Preclinical success: Potent TNIK inhibition, efficacy in IPF models
  • USAN naming for Rentosertib: March 2025, Phase II ongoing
Read case study →

Associated Press (AP)

News Media

In the mid-2010s, the Associated Press (AP) faced significant constraints in its business newsroom due to limited manual resources. With only a handful of journalists dedicated to earnings coverage, AP could produce just around 300 quarterly earnings reports per quarter, primarily focusing on major S&P 500 companies. This manual process was labor-intensive: reporters had to extract data from financial filings, analyze key metrics like revenue, profits, and growth rates, and craft concise narratives under tight deadlines. As the number of publicly traded companies grew, AP struggled to cover smaller firms, leaving vast amounts of market-relevant information unreported. This limitation not only reduced AP's comprehensive market coverage but also tied up journalists on rote tasks, preventing them from pursuing investigative stories or deeper analysis. The pressure of quarterly earnings seasons amplified these issues, with deadlines coinciding across thousands of companies, making scalable reporting impossible without innovation.

Lösung

To address this, AP partnered with Automated Insights in 2014, implementing their Wordsmith NLG platform. Wordsmith uses templated algorithms to transform structured financial data—such as earnings per share, revenue figures, and year-over-year changes—into readable, journalistic prose. Reporters input verified data from sources like Zacks Investment Research, and the AI generates draft stories in seconds, which humans then lightly edit for accuracy and style. The solution involved creating custom NLG templates tailored to AP's style, ensuring stories sounded human-written while adhering to journalistic standards. This hybrid approach—AI for volume, humans for oversight—overcame quality concerns. By 2015, AP announced it would automate the majority of U.S. corporate earnings stories, scaling coverage dramatically without proportional staff increases.

Ergebnisse

  • 14x increase in quarterly earnings stories: 300 to 4,200
  • Coverage expanded to 4,000+ U.S. public companies per quarter
  • Equivalent to freeing time of 20 full-time reporters
  • Stories published in seconds vs. hours manually
  • Zero reported errors in automated stories post-implementation
  • Sustained use expanded to sports, weather, and lottery reports
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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