Selecting the Right AI Operating Model: A Guide to Finding What Works for Your Organization

As AI adoption accelerates across industries, choosing the right operating model for AI is a critical decision. An effective model enables companies to streamline resources, manage risks, and maximize AI's value. Four main AI operating model archetypes—Centralized, Consulting, Center of Excellence, Functional, and Decentralized—each offer unique advantages and challenges depending on organizational goals, AI maturity, and available resources. Here’s a look at each model to help you find the best fit for your organization’s AI journey.

1. Centralized Model

Description: In a Centralized AI model, all AI initiatives are managed through a single, unified team. This team may sit under a department like IT or an independent AI function, which owns the budget, decision-making, and execution of AI projects across the enterprise.

Strengths:

• Consistency and Standardization: The Centralized model ensures standard approaches, methodologies, and tools across the organization.
• Cost Efficiency: Pooling resources helps in optimizing costs, especially in early AI adoption stages.
• Control and Compliance: It’s easier to enforce governance, risk management, and compliance across all projects.

Challenges:

• Scalability Limits: As demand for AI grows, centralized teams may become bottlenecks.
• Limited Customization: A “one-size-fits-all” approach might not meet specific needs of different departments.

Best Fit: This model works well for organizations at an early stage in their AI journey, where establishing consistent practices and governance is essential.

2. Consulting Model

Description: A Consulting AI model has a specialized, central team providing AI guidance, resources, and oversight. Individual business units or departments execute projects, leveraging consulting support when needed.

Strengths:

• Scalable Expertise: Expert consulting teams offer support for complex projects without carrying the load of execution.
• Encourages Departmental Ownership: Departments maintain control over project execution, boosting relevance to local needs.
• Flexibility in Project Execution: Consulting enables departments to implement solutions tailored to their unique goals.

Challenges:

• Dependence on Internal “Clients”: Business units must proactively engage with the AI consulting team, which can lead to uneven adoption.
• Possible Governance Gaps: Ensuring consistent compliance and standards across various departments can be challenging.

Best Fit: The Consulting model is ideal for mid-sized to large organizations with multiple departments needing AI support but who want to maintain executional independence.

3. Center of Excellence (CoE) Model

Description: In a Center of Excellence model, an AI CoE drives best practices, governance, and cutting-edge research. The CoE may develop initial prototypes, but implementation is left to individual business units.

Strengths:

• Focus on Innovation: The CoE is well-positioned to foster innovation, build reusable assets, and provide high-level AI expertise.
• Shared Best Practices: By establishing standards and frameworks, a CoE helps elevate AI maturity across the organization.
• Strategic Focus: The CoE enables high-quality AI development by pooling talent, research, and resources.

Challenges:

• Risk of Silos: Without robust collaboration, CoE initiatives may not integrate well with individual business units’ needs.
• Resource Intensive: Maintaining a high-quality CoE requires ongoing investment in skilled talent and tools.

Best Fit: CoEs are well-suited to large organizations committed to scaling AI across departments while maintaining control over standards and innovation.

4. Functional Model

Description: A Functional model places AI expertise within specific departments or functions, with each department developing and deploying AI solutions independently. The AI function may align with department-specific goals, such as marketing or finance.

Strengths:

• Deep Domain Knowledge: AI teams embedded within functions benefit from department-specific knowledge, making solutions more relevant.
• Speed to Market: Solutions are tailored and deployed quickly, responding effectively to functional needs.
• Autonomy: Functional teams have complete control over how they leverage AI for their goals.

Challenges:

• Potential Redundancy: Separate AI initiatives in each department can lead to inefficiencies and duplicated efforts.
• Complex Governance: Ensuring consistent standards, ethics, and compliance across departments is more challenging.

Best Fit: The Functional model suits organizations with mature AI capabilities, where departments have well-defined goals and resources to manage independent AI projects.

5. Decentralized Model

Description: In the Decentralized model, all departments own their AI initiatives, including strategy, budgeting, and execution. This model promotes independence, with minimal oversight from a central AI authority.

Strengths:

• Complete Autonomy: Departments have full control, allowing them to align AI with specific priorities.
• Localized Solutions: AI is implemented according to specific needs, leading to high relevance and quick adaptation.
• Flexibility in AI Approaches: Teams can innovate and iterate freely, selecting methods and tools that suit their workflows.

Challenges:

• Resource Duplication: Different departments may build overlapping capabilities, wasting resources.
• Challenges in Data Consistency: A decentralized approach can create data silos, making organization-wide data alignment difficult.
• Lack of Standardization: Lack of central control can hinder organization-wide AI governance and accountability.

Best Fit: A Decentralized model can be beneficial for highly mature organizations with diverse goals across departments, where agility and local relevance are prioritized over standardization.

Final Thoughts

Selecting the right AI operating model requires aligning with the organization’s goals, AI maturity level, and available resources. The right choice provides a foundation for sustainable growth, efficient operations, and innovative solutions. Whichever model you choose, maintaining agility and building a robust governance framework are essential to maximize AI’s impact.