The appliedAI Operating Model: Four Pillars for Scaling
Pilots succeed — but scaling fails due to organizational barriers.
Without an operating model, AI becomes a patchwork of experiments. Shadow IT, unclear risk ownership, and missing standards slow you down exactly when you need to accelerate.
1
Governance, Roles & Mandates: Speed Through Clarity
We don’t define bureaucracy—we define guardrails. Clear RACI models, AI Product Owners, and the right balance of central vs. decentralized responsibilities ensure teams know what they can do—and can simply execute.
2
EU AI Act Operationalization: Compliance as an Enabler
We translate regulatory requirements into concrete technical and organizational measures. Standardized risk classification, documentation templates, and quality gates (compliance by design) remove uncertainty and clear the path for productive deployments.
3
Tech Stack & MLOps: Your AI Factory
We define your production environment: buy-vs-build decisions, platform standards (MLOps, LLMOps, AgentOps), and integration patterns—so AI isn’t hacked together, but built to scale like a product line.
4
Agentic AI Readiness: The Next Level of Scale
AI-Agents change everything. We redesign your processes and control mechanisms—from human-in-the-loop to robust guardrails—so autonomous systems can take action safely and predictably, not just make suggestions.
Why companies trust appliedAI for their AI operating model
Built in the real world:
Our models are grounded in work with Europe’s top AI teams (Shapers).
No compliance theory:
We know how to implement the EU AI Act in practice—technically.
Engineering DNA:
We don’t recommend governance that can’t be executed in real systems.
Future-ready:
We design structures today for tomorrow’s agentic AI era.
Europe’s AI Champions Trust Us
Track record, not promises.
Over 250 companies, including 23 of the 40 DAX corporations, build on our 8+ years of expertise. With 100+ experts and over 70 implemented applications, we deliver scalable results.
Highest AI expertise, professional collaboration, measurable results: With appliedAI, we were able to successfully and sustainably implement our strategic application.
Rolf Buch – CEO (until Jan. 2026), Vonovia SE
Collaborating with appliedAI’s AI Governance Working Group has been invaluable for us. The structured approach, coupled with real-world case studies and open dialogue, ensures we not only stay compliant with the EU AI Act but also build efficient governance models that keep AI innovation front and center.
Dirk Wacker
Giesecke+Devrient
The appliedAI Risk Classification training significantly improved our understanding of the EU AI Act's implications for projects at Roche. We discussed six real life use cases in detail and it was eye-opening.
Dr.-Ing. Anna Osberghaus
Roche Diagnostics GmbH
Collaborating with appliedAI’s AI Governance Working Group has proved very rewarding. Organizers are always prepared, participants bring a wealth of insights. It’s a unique forum to effectively brainstorm and stay ahead of the AI governance curve.
Geoffroy Pavillet
Data Protection Counsel, Linde GmbH
appliedAI has helped Nokia Standards in the ambitious endeavor to upskill each and every team member to be a good AI Citizen. Together with appliedAI we have jointly tailored an AI literate curriculum exactly addressing the needs in our organization.
Prof. Dr.-Ing. Ingo Viering
Department Head RAN Architecture, Senior Research Manager Nokia Standards
Highest AI expertise, professional collaboration, measurable results: With appliedAI, we were able to successfully and sustainably implement our strategic application.
Rolf Buch – CEO (until Jan. 2026), Vonovia SE
Collaborating with appliedAI’s AI Governance Working Group has been invaluable for us. The structured approach, coupled with real-world case studies and open dialogue, ensures we not only stay compliant with the EU AI Act but also build efficient governance models that keep AI innovation front and center.
Dirk Wacker
Giesecke+Devrient
The appliedAI Risk Classification training significantly improved our understanding of the EU AI Act's implications for projects at Roche. We discussed six real life use cases in detail and it was eye-opening.
Dr.-Ing. Anna Osberghaus
Roche Diagnostics GmbH
Collaborating with appliedAI’s AI Governance Working Group has proved very rewarding. Organizers are always prepared, participants bring a wealth of insights. It’s a unique forum to effectively brainstorm and stay ahead of the AI governance curve.
Geoffroy Pavillet
Data Protection Counsel, Linde GmbH
appliedAI has helped Nokia Standards in the ambitious endeavor to upskill each and every team member to be a good AI Citizen. Together with appliedAI we have jointly tailored an AI literate curriculum exactly addressing the needs in our organization.
Prof. Dr.-Ing. Ingo Viering
Department Head RAN Architecture, Senior Research Manager Nokia Standards
FAQs
Because teams hesitate without clear rules. Governance creates a safe space: once standards for security, legal, and data are defined, product teams can build autonomously and fast—without reopening fundamental debates every time.
It makes it mandatory. But instead of fearing penalties, we use it as a quality framework: risk management—grounded in proven practices and the EU AI Act—leads to more robust, safer, better software. We integrate it seamlessly into your development process.
Traditional software executes commands; AI agents pursue goals. That requires new control mechanisms: Who supervises the agent? What budget or authority can it use? We define the new operating rules for digital employees.
Most of the time, yes—but with the right mandate: not as a bottleneck that builds everything, but as an enabler that provides standards, platforms, and governance so business units can scale decentrally.
With minimum viable governance. High-risk systems need strict controls; low-risk internal tools need fast paths. We automate compliance checks in the CI/CD pipeline instead of pushing paperwork.
An AI governance framework defines how AI is steered, monitored, and controlled within a company. It sets clear roles, decision rights, policies, and control mechanisms to ensure AI systems are developed and operated responsibly.
Strong AI governance is essential for scaling because it enables consistent decisions, strengthens AI risk management, and provides the foundation for a scalable AI operating model.
An AI operating model embeds risk management directly into organizational structures, processes, and accountabilities. It ensures risks related to data, models, and AI systems are identified, assessed, and managed across the entire AI lifecycle.
By integrating AI risk management into day-to-day operations, companies can scale AI safely while maintaining control, transparency, and accountability.
To govern AI agents responsibly, companies need a structured agent framework that clearly defines accountabilities, human–AI interactions, escalation paths, and monitoring mechanisms.
Clear AI governance ensures agentic systems operate within defined boundaries, support business objectives, and enable effective AI risk management. This approach supports the safe and scalable use of AI agents within a holistic AI operating model.
AI risk management is a core requirement of the EU AI Act and a critical component of compliant AI applications. It covers the systematic identification, documentation, and mitigation of risks associated with AI systems.
By embedding AI risk management into the AI governance framework and the AI operating model, companies can meet regulatory requirements while ensuring transparency, traceability, and long-term operational resilience—as part of a structured AI compliance approach.
Governance and operating models reduce AI-related risks through clear accountabilities, standardized processes, and continuous monitoring. A well-designed AI governance framework and an effective AI operating model enable the targeted use of AI for risk management—so potential risks can be identified early and addressed proactively.
This structured approach embeds AI risk management into day-to-day operations and strengthens it as a core element of scalable, compliant AI systems across the enterprise.