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# Blueprints for Success: Steering a Global Data & AI Architecture

Merck stopped unauthorized AI use by redirecting employees to a secure, internal GPT clone. Dominic Schneider reveals the architecture governing and scaling over 1,400 enterprise data solutions.

- **Speakers:** [Dominik Schneider](https://www.wearedevelopers.com/@dominik-schneider)
- **Event:** World Congress 2025
- **Published:** August 20, 2025
- **Duration:** 19:44
- **URL:** https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture

## Summary

Dominic Schneider outlines how Merck, a global leader in life sciences and healthcare, manages its massive enterprise data and AI ecosystem, 'Optimize.' Operating under a hub-and-spoke model, the architecture centralizes powerful infrastructure—leveraging AWS, Snowflake, and Palantir Foundry—while empowering specific business sectors. The ecosystem includes everything from data visualization and business process mining to custom LLM endpoints, vector databases, and an internal GPT clone currently used by half the workforce. A crucial element of this stack is ensuring specific analytics components are GXP-enabled to maintain strict compliance within the pharmaceutical domain.

To prevent the unchecked mushrooming of isolated technical deployments, the strategy relies heavily on robust governance and user journey guardrails. To combat the rise of unauthorized applications, Merck employs network firewall detection and a clever soft block strategy: intercepting external AI tool usage and redirecting employees to internal, secure alternatives with explicit reminders not to upload confidential data. Moving projects from ideation to production requires mandatory architectural reviews, with strict policies enforcing that deployments happen exclusively via infrastructure as code and CI/CD pipelines.

Supporting over 1,400 active production solutions requires constant evolution, managed through Scaled Agile Framework product increment planning. Looking ahead, the architecture teams intend to streamline developer self-service by applying generative AI directly to the governance process. Upcoming initiatives include augmenting technical reviews with AI models trained on internal infrastructure documentation, building a developer-facing chatbot to guide engineering tool selection, and fully automating the categorization of shadow IT software to keep automated firewall blocklists continuously updated.

**Keywords:** enterprise data architecture, AI ecosystem governance, hub-and-spoke operating model, shadow IT detection, infrastructure as code deployment, CI/CD pipeline automation, corporate AI tool provisioning, pharmaceutical data compliance, GXP enabled architecture, internal LLM development, AWS Snowflake integration, technology capability mapping, SAFe framework management, generative AI architecture chatbot, network firewall AI blocking

## Chapters

1. **Merck business overview and market diversity** (00:05) — Merck operates diverse business areas with a legacy spanning life science, healthcare, and electronics.
1. **Three pillars of the data and AI strategy** (01:56) — The data strategy relies on foundational pillars focusing on culture, operating models, and business teams.
1. **Core components of the internal Optimize ecosystem** (03:11) — The centralized data services environment covers cloud storage, AI workflows, and regulatory compliance.
1. **Ecosystem adoption progress and governance alignment challenges** (06:25) — Scaling data tools across the organization requires balancing rapid adoption with security and governance.
1. **Managing external tool requests and mitigating shadow IT** (07:59) — Interceptor processes redirect unauthorized software usage and guide users toward approved corporate tools.
1. **Navigating architecture guidance for internal tool selection** (10:32) — Capability maps and architecture blueprints help consolidate technology and guide users to appropriate tools.
1. **Provisioning cloud infrastructure through centralized architecture reviews** (12:09) — A mandatory review process establishes governed environment accounts for centralized AI use cases.
1. **Mandating infrastructure as code for secure production deployment** (14:10) — Mandating infrastructure as code and automated deployment pipelines ensures robust and secure production releases.
1. **Driving sector collaboration with scaled agile framework ceremonies** (15:25) — Embedded lead architects and agile ceremonies align decentralized teams with the central data ecosystem.
1. **Augmenting future architecture governance processes with generative AI** (16:56) — Automated architecture reviews, specialized chatbots, and dynamic tool detection aim to enhance operational efficiency.

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