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OpenAI Commercial and Professional Services Europe & UK

HSP GRUPPE

Document Processing · Enterprise Knowledge Search

HSP wanted AI to create more capacity across tax advisory, legal research, client communication, and financial analysis without weakening confidentiality, governance, or professional accountability.

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40,000+ estimated annual hours of additional capacity
500,000+ ChatGPT conversations in six months
84% weekly active usage
98.6% of employees report higher productivity

Reported impact

Business outcome

HSP increased employee productivity and work quality, created substantial additional annual capacity without reducing headcount, improved client service, and built a firm-wide AI operating model that can scale across 81 organizational groups.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

HSP GRUPPE treated ChatGPT Enterprise as an organizational capability instead of a standalone productivity tool. Leadership combined monthly AI forums, role-based learning, and internal governance requirements so employees could use AI across tax advisory, legal research, client communication, financial analysis, and knowledge sharing while professionals retained final responsibility for outputs. The network standardized workflows into shared agents such as AI Client Communication for first drafts and tone support, and Booking Assistant SKR03 & SKR04 for preparing and classifying booking questions. Individual professionals then used the platform as a sparring partner for complex tax work and meeting preparation, while the organization began piloting ChatGPT Work to redesign workflows like year-end accounting into more proactive, agentic processes. This created a scalable operating model where repeatable best practices are embedded once and reused across legally independent firms.

Data flow

Professionals submit client communications, booking questions, legal and tax research, and financial-analysis tasks into ChatGPT Enterprise and shared agents. Outputs are reviewed by the relevant specialist, while successful patterns are standardized into reusable agents and later extended into broader workflow pilots with ChatGPT Work.

Solution architecture

4 components · 2 layers
  1. Serving
    • ChatGPT Enterprise Provides the secure shared workspace for tax, legal, finance, and knowledge workflows across the network.
    • AI Client Communication Supports first drafts, structure, clarity, and client-oriented tone in communications.
    • Booking Assistant SKR03 & SKR04 Helps prepare and classify recurring booking questions for finance workflows.
  2. Orchestration
    • ChatGPT Work Is being piloted for more proactive, workflow-level automation across accounting processes.

Architecture clues

  • ChatGPT Work pilots focus on end-to-end workflow redesign rather than point tasks
  • Monthly AI forums spread successful patterns across the firm network
  • Professional review remains mandatory for client-facing outputs
  • Shared agents are standardized and reused across 81 organizational groups

Evidence from the source

"84% weekly active usage"
"98.6% of surveyed employees reported higher productivity"
"A deliberately conservative internal scenario estimates approximately 40,000 hours of additional annual capacity."
"The organization also began standardizing successful use cases into custom Agents."
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. OpenAI

    HSP GRUPPE customer story

    First-party
    https://openai.com/index/hsp-gruppe/
    Source type
    Vendor-published customer story
    Published
    Aug 7, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Aug 7, 2026 · OpenAI · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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