Insights Blog How the AI Operating Model closes the ...

How the AI Operating Model closes the gap between strategy & execution

Written by Lara Schardey & Philip Mader
The most important facts in 20 seconds
  • 85% of AI projects are estimated to fail, and 30% of companies will abandon most of their generative AI initiatives after proof-of-concept this year.* 
  • Common pitfalls across industries why AI initiatives failed include disconnect from business goals, poor governance, low data quality, weak AI adoption, siloed processes, and outdated infrastructure. 
  • An AI Operating Model aligns business and strategy, maximizes value contribution, and ensures organizational readiness for scaling AI. 
Agenda

Start at the source: Why do AI initiatives fail?

The hard truth is: an estimated 85% of AI projects fail. Based on Gartner analysis this year, 30% of companies will abandon most of their generative AI initiatives after proof-of-concept. These statistics represent one of the greatest challenges businesses face today: successful digital business transformation with AI.

Across industries, failed AI initiatives share common pitfalls:

  • Strategy: Disconnect from business goals and high PoC failure rates.
  • Governance: Unaddressed compliance and ethical risks.
  • Data management: Low data quality and accessibility issues.
  • People: Poor user adoption and diminished trust in AI outputs.
  • Processes: Siloed teams with insufficient collaboration.
  • Technology: Outdated infrastructure and a fragmented tool landscape.

These pitfalls show that many organizations take the first step in AI transformation but stumble on the next ones. The true challenge isn’t strategy formulation—it’s translating vision into execution and measurable business impact.

How to overcome these barriers? Implement a comprehensive AI Operating Model that aligns business and strategy, maximizes value contribution, and ensures organizational integration and readiness.

 

What is an AI Operating Model—and how to implement it?

An AI Operating Model covers the strategic, organizational, process-related, and technological aspects required to operate and scale AI successfully. It orchestrates strategy-to-execution, maximizes value, and ensures integration and readiness.

Think of it as building a house:

  • The roof — Expanding horizons (AI strategy & transformation): Define clear objectives that align initiatives with business priorities so technology investments contribute to measurable outcomes.
  • The foundation — Governance & data management: Establish AI compliance and ethics with cross-functional boards. Build robust and accessible data foundations to enable scalable value creation.
  • The core pillars — Operational excellence (people, processes & technology): Empower skilled people, integrate AI into end-to-end processes, and run on a robust technology infrastructure.

When properly constructed, your AI Operating Model turns strategy into operations and delivers lasting business impact that separates AI leaders from the 80% that fail.

 

Governance: The relevant foundation

For your AI Operating Model to perform, you need rock-solid governance—the concrete foundation in the house analogy.

Governance ensures accountability, security, transparency, and efficiency in organization-wide AI handling. Alongside data management, it lays the foundation for effectively managing, scaling, and aligning AI initiatives with the company's strategic goals, while ensuring legal and ethical compliance. 

Here are five critical capabilities to consider when building governance for your AI Operating Model: 

  • Compliance & legal: Ensure AI systems meet regulatory requirements (e.g., GDPR, EU AI Act).
  • Ethics & trust: Embed Responsible AI principles like fairness, transparency, and explainability throughout the lifecycle.
  • Portfolio management: Centrally manage and prioritize AI initiatives and allocate resources transparently.
  • Risk management: Proactively manage AI-related risks to build trust across the value chain.
  • Organizational model: Define how AI is integrated (central, decentralized, hybrid), including roles, ownership, and collaboration.

These five capabilities form an interconnected system where each element reinforces the others. They create the structural and legal integrity needed for building truly resilient AI governance.

 

Data management: Build the right foundation

Your AI is only as good as your data. Many organizations rush into AI implementation, only to realize their data isn’t ready for integrating and scaling AI. Even the most sophisticated algorithms and models are of little value unless you know what data exists and can access it. 

Just as no architect would start building walls without first laying a rock-solid foundation, no organization should scale AI without first investing in robust data management. 

Effective data management ensures that AI-driven processes and systems are built on a reliable, accessible, and trustworthy data foundation, spanning five core areas: data strategy, data governance, data quality, data engineering, and data literacy. 

To put these into practice: 

  • Strategic alignment: Data investments must directly support prioritized AI use cases (use case funnel alignment). 
  • Operational readiness: Mature data pipelines and governance are essential to embedding AI into operations. 
  • Cultural shift: Business users need to become data-literate collaborators – this is as much about enablement as it is about tooling. 

The bottom line: Every AI success story starts with a robust data foundation. Organizations that invest in data management practices create the conditions for lasting AI success, while those that skip this step often find themselves rebuilding from scratch.

 

People, processes & technology: The operational core

With the roof and foundation in place, the following core elements bring your AI Operating Model to life: 

  • People – The "people" pillar brings together everything related to roles, responsibilities, and workforce capabilities as skilled and enabled employees who use AI ethical and effeciency can increase business value. 
  • Processes – The "process" element refers to the organizational workflows and structures required to implement AI initiatives. Effective AI processes require three foundational capabilities: Use case lifecycle management, operational integration, and AI workflows. These capabilities enable organizations to develop processes that systematically embed, operationalize, and scale AI across their entire value chain. 
  • Technology – The “technology” pillar is centers around three core capabilities to create the necessary foundation for AI: A target technical architecture which includes data pipelines, infrastructure, and interfaces/APIs. A tool stack which captures the AI-enabling tools used across the entire AI lifecycle – from model development and experimentation to deployment and monitoring. And in the end  “security & access” which focuses on data protection, identity management, and access control for sensitive AI components (e.g. models, training data & outputs).

 

From concept to competitive advantage

When built on solid foundations, the AI Operating Model turns ambition into execution and execution into impact. It’s a systematic approach which encompasses strategy, governance, data management, people, processes, and technology. In our client work, we often see that organizations with a strong AI Operating Model achieve faster time-to-value and higher pilot-to-production success rates. The better they embed new ways of working across functions, the greater their progress in AI transformation.