Crafting an AI Operating Model

July 22, 2025

By Sam Kharazmi

Introduction

The contemporary business landscape is undergoing a profound transformation, driven by the rapid evolution of artificial intelligence. This shift extends far beyond mere technological adoption. It fundamentally reimagines how businesses operate, innovate, and create value. This requires a strategic rather than piecemeal approach to AI integration. The success of AI initiatives increasingly depends on an organization’s capacity to integrate, govern, and leverage these technologies effectively, rather than relying solely on the sophistication of the algorithms.

An AI operating model serves as a comprehensive framework that defines how an organization structures, governs, and deploys artificial intelligence throughout its business processes. This framework encompasses critical elements—the people involved, the processes they follow, the technology they use, and the data management practices that underpin everything—designed to enable AI initiatives to thrive. Moving beyond a technical roadmap, an effective AI operating model aligns AI capabilities with overarching business objectives and establishes clear accountability for AI‑driven outcomes.

The interdependence of processes, people, technology, and data management means that a deficiency in any one area can compromise the entire AI strategy. This highlights the critical role of organizational readiness and cultural integration alongside technical capabilities. An effective AI operating model ensures that AI investments are aligned with core business goals, efficiently allocated, and focused on transformative changes rather than incremental improvements. This structured approach significantly increases the likelihood of realizing substantial financial benefit from AI. The distinction between “transformation” and “incremental improvements” represents a fundamental strategic choice for organizations. The decision to optimize existing operations versus redefining the value proposition through AI will significantly influence the design and investment in the AI operating model.

AI-Enabled vs AI-Native

There are two pathways organizations typically take for AI integration: enhancing existing foundations or building from the ground up. Understanding these distinctions is crucial for designing an appropriate AI model.

AI-Enabled

AI-enabled companies are characterised by their approach of retrofitting AI into existing processes and workflows. Here, AI is integrated as an additive layer to existing platforms, enhancing specific functions without fundamentally altering the core product's ability to function. Examples include Airbnb's AI search capabilities or Shopify's AI copywriting tools. The core product continues to operate independently of AI, but the AI features significantly boost its value, speed, or personalisation.

The benefits of this approach include the ability to modernize operations without the extensive effort and cost of rebuilding from scratch. It often proves more budget-friendly and allows for a faster time to market for specific AI features, as it leverages existing platforms and data infrastructure. This strategy also capitalizes on established user bases and existing workflows. Operationally, AI teams within these companies tend to focus on specific enhancements rather than foundational changes, often relying on existing talent, readily available AI APIs, or external services.

AI-Native

In contrast, AI‑native companies are built around AI from their inception, designing products, infrastructure, and business models that inherently leverage AI capabilities. For these organizations, AI is central to operations; if removed, the product loses its core value and functionality, as seen with Notion AI or GitHub Copilot. Every interaction, workflow, and decision within an AI‑native company is deeply intertwined with AI.

AI‑native organizations typically achieve superior agility, efficiency, and innovation velocity. They are better equipped to respond to market changes faster and continuously optimize operations. These companies often gain a competitive edge by leveraging unique datasets or proprietary models that facilitate continuous learning and adaptation. Their operational design demands systems‑level thinking, with AI capabilities serving as foundational components of the entire technology stack. This approach prioritizes continuous integration and deployment (CI/CD) for AI models, robust monitoring, and feedback loops for model retraining. Such a model typically requires significant investment in scalable cloud infrastructure, specialized AI talent, and ongoing research and development.

Key Distinction in Operating Model

The fundamental difference between the "architecture‑first AI integration" of AI‑native companies and the "retrofitting" approach of AI‑enabled companies indicates a divergence in organizational DNA and long‑term competitive potential. AI‑native firms are designed for inherent leverage and seamless integration, while AI‑enabled firms may encounter technical debt and limitations in AI potential due to rigid existing structures.

However, becoming AI‑native is not universally superior; the optimal approach depends on the specific organizational context. Many successful organizations adopt hybrid strategies, maintaining core stability while building AI‑native capabilities in specific areas. The most effective AI operating model is not a one‑size‑fits‑all solution but a dynamic strategy tailored to the organization’s maturity, industry, and goals.

Building Your AI Operating Model: Key Considerations for Success

Developing and implementing an effective AI operating model is a complex undertaking that requires careful consideration of several critical factors.

Tailoring the Model to Your Organisational Context and Maturity

The optimal operating model is not universal; organizations must weigh immediate stability against future competitiveness and conservative risk management against innovation velocity. Many find value in hybrid strategies: maintaining core stability while building AI‑native capabilities in specific, high‑impact areas. This allows for gradual transformation and minimizes disruption to essential operations. Because this is inherently iterative, embed adaptability and continuous evaluation so the model evolves with organizational capabilities and the dynamic AI environment.

Addressing Common Challenges

Organizations embarking on AI transformation frequently encounter several common challenges that must be proactively addressed within their operating model:

• Talent gaps and shortages: The market for AI skills is evolving rapidly, creating significant gaps. This necessitates continuous upskilling, a reevaluation of sourcing decisions (in‑house vs outsourcing), and strong retention strategies for AI professionals.
• Cultural resistance: Overcoming skepticism and embracing AI’s speed and adaptability requires strong leadership support, a culture of experimentation, and robust cross‑functional collaboration. Effective change management is crucial.
• Data quality and accessibility: High‑quality, accurate, complete, and consistent data forms the bedrock for successful AI models. Establish robust data management, accessibility frameworks, and security protocols to ensure integrity and utility.
• Ethical AI governance: Address algorithmic bias, ensure transparency and accountability, protect data privacy, and manage IP issues to achieve long‑term success and regulatory compliance (e.g., EU AI Act). Proactive governance and a commitment to fairness and transparency serve as strategic advantages, not mere compliance.
• Integration complexity: Seamlessly embedding diverse AI technologies across existing systems to ensure interoperability and synchronized intelligent functionality is a significant technical and operational challenge.

The Imperative of Continuous Learning and Adaptation

AI‑native organizations, by design, foster a continuous learning culture that drives rapid skill evolution and an innovation mindset across the enterprise. Establish adaptive feedback mechanisms and self‑improving systems that evolve with performance data and technological advances. Given AI’s dynamism, an AI operating model must be intrinsically agile and self‑optimizing—beyond conventional change management—cultivating a "learning organization" where AI systems and human proficiencies develop in concert.

Conclusion

An effective AI operating model holistically integrates people, processes, technology, and data. Its purpose is to align AI initiatives with core business objectives, driving transformative outcomes rather than incremental gains. The journey to an AI‑powered future demands strategic intent, a clear decision on whether to enhance existing capabilities or build natively, and the judicious adoption of robust frameworks. To truly harness AI, organizations must address talent, culture, data quality, and governance while navigating integration complexity. Leaders should embrace experimentation and view AI not merely as a tool, but as a fundamental shift in how the organization creates value and sustains competitive advantage.