Scaling Enterprise AI: Unleashing the Power of the AI Operating Model Beyond Pilot Phase

18 hours ago
19 min read

Most enterprises have crossed the AI experimentation threshold. The proof-of-concept is complete. The pilot delivered impressive results. Leadership is enthusiastic. And yet, eighteen months later, that successful pilot remains exactly what it was: a pilot. The path from AI experiment to enterprise-wide transformation remains elusive for most organizations, not because the technology failed, but because they lack the organizational blueprint to make AI work at scale.

This challenge keeps CIOs, Chief Data Officers, and AI leaders awake at night. Organizations that successfully scale AI treat it as an operating model challenge, not merely a technology deployment. The difference between AI that delivers sustained business value and AI that stalls after the pilot phase comes down to one critical element: the AI operating model.

This article provides a comprehensive framework for building an AI operating model that works. Drawing on implementation patterns across multiple industries and geographies, it examines what an AI operating model actually entails, explores the patterns that have proven effective, addresses what happens after deployment, and shows how these frameworks must evolve for the emerging agentic AI era.

What Is an AI Operating Model and Why Does It Matter?

An AI operating model is the organizational blueprint that integrates technology platforms with business processes and defines the systematic approach for how AI is designed, implemented, monitored, and operated across the enterprise. Drawing from the foundational work of MIT researchers like Jeanne Ross on enterprise operating models, an AI operating model extends these concepts to address the unique requirements of artificial intelligence at scale.

Traditional enterprise operating models focus on the level of business process integration and standardization needed to deliver products and services. For AI, this foundation must evolve to encompass the continuous learning nature of AI systems, the need for ongoing monitoring and intervention, and the complex interplay between automated decision-making and human oversight. The AI operating model functions as the assembly line for AI: not for building AI systems once, but for designing, deploying, governing, and continuously improving them across every corner of an organization.

Without an AI operating model, AI work remains fragmented. Ownership becomes unclear. Controls are applied inconsistently. Successful solutions cannot be replicated across business units. The result is an organization with pockets of AI innovation but no pathway to enterprise-wide transformation. Perhaps most damaging, without a clear operating model, the question of accountability remains unanswered: when an AI system produces incorrect outputs in production, who owns the escalation and the outcome?

The Two Pillars of an Effective AI Operating Model

Every successful AI operating model rests on two fundamental pillars: standardization and integration. These are not abstract concepts. They represent concrete organizational capabilities that determine whether AI scales or stalls.

Standardization: Establishing Ways of Working

Standardization encompasses the shared standards, processes, and governance structures that enable consistent, repeatable AI delivery. This includes clearly defined roles and decision rights (often captured through RACI frameworks), standardized intake processes for evaluating and prioritizing AI opportunities, consistent delivery methodologies, common data protocols, shared evaluation and testing approaches, unified monitoring practices, and comprehensive control frameworks. A critical point: governance is part of standardization, not the entirety of it. Organizations that equate AI governance with their AI operating model are addressing only a fraction of what is required.

Effective standardization answers essential questions: How does the organization evaluate whether an AI opportunity is worth pursuing? What quality gates must every AI solution pass before deployment? How is success measured? Who has authority to approve production deployment? These standards must be documented, communicated, and consistently applied, not written once and forgotten in a shared drive.

Integration: Connecting AI Across the Enterprise

Integration refers to the mechanisms that link AI efforts across business units, geographies, and functions. This pillar ensures that successful AI solutions can be identified, adapted, and reused rather than rebuilt from scratch in every corner of the organization. Integration enables the network effects that separate organizations achieving enterprise-wide AI value from those stuck in perpetual pilot mode. When a customer service AI solution succeeds in one region, integration mechanisms allow that success to propagate across other regions with appropriate localization, without each region starting from zero.

Integration requires both technical and organizational components. Technical integration includes shared platforms, common APIs, and interoperable data structures. Organizational integration includes communication channels between teams, knowledge-sharing mechanisms, and incentive structures that reward collaboration over siloed optimization.

The Shared Services Foundation: What Every AI Operating Model Needs

Before examining operating model patterns, it is essential to understand that virtually every enterprise AI operating model includes a shared services layer, typically enabled by IT. This foundation provides the common infrastructure upon which all AI initiatives build.

The shared services layer encompasses core platforms including cloud infrastructure, data platforms, identity and access management, integration capabilities, and observability tools. It also includes essential guardrails: security controls, risk management protocols, auditability requirements, and data governance standards. Finally, it provides reusable assets such as templates, evaluation frameworks, and monitoring patterns that accelerate delivery while ensuring consistency.

This shared services layer is not optional. Even organizations that adopt highly decentralized AI operating models require this common foundation to ensure security, compliance, and basic operational coherence. The strategic question is not whether to have shared services, but what additional organizational structures to build upon this foundation.

Three Operating Model Patterns: Choosing the Right Organizational Structure

Above the shared services foundation, organizations must choose how to structure ownership and delivery of AI solutions. Three primary patterns have emerged, each with distinct advantages and appropriate use cases.

The Centralized Model: AI Center of Excellence

In the centralized model, a dedicated AI Center of Excellence (CoE) builds and ships most AI solutions while establishing enterprise-wide standards. This model works best in the early stages of AI maturity when the organization is still building fundamental capabilities and consistency matters more than speed. The centralized approach ensures quality and prevents fragmentation but can become a bottleneck as demand for AI solutions grows. Organizations often begin here and evolve toward more distributed models as capabilities mature.

The centralized model also serves organizations in highly regulated industries where consistency of controls matters more than speed of delivery. Financial services, healthcare, and government organizations often find value in maintaining stronger central control even as they mature.

The Hybrid Model: Hub-and-Spoke

The hub-and-spoke model represents the most common pattern for scaling AI across complex enterprises. A central hub owns platforms, standards, and risk controls while business-aligned teams (or country teams in global organizations) own delivery and adoption within those guardrails. This model balances the need for consistency and governance with the speed and contextual knowledge that comes from embedding AI capabilities within business units.

DBS Bank provides an instructive example of this pattern in action. The Singapore-based bank reorganized to function as what leadership describes as a "28,000-person startup." Their centralized AI hub standardizes roles and decision rights, delivery practices, data protocols, and control layers including governance.

Meanwhile, spokes embedded into business units ensure AI becomes part of daily workflows rather than isolated projects. This structure has enabled DBS to achieve consistent AI delivery while maintaining the agility to respond to business-specific needs.

The Decentralized Model: Business Unit Ownership

In the decentralized model, business units build and run their own AI solutions with only light coordination from a central function. This pattern suits organizations where business units operate in truly independent domains with minimal overlap. However, this model carries significant risks. Duplication of effort becomes common as multiple units solve similar problems independently. Inconsistent controls create governance gaps and compliance risks. Successful solutions remain trapped within individual units, unable to benefit the broader enterprise. For most organizations, pure decentralization represents the failure mode to avoid rather than the target state to pursue.

The Human Side of AI Transformation: Managing Workforce and Organizational Change

Operating model discussions often focus on structures and processes while overlooking the humans who must work within these systems. This represents a critical oversight. When AI automates a significant portion of a team's workload, what happens to that team? The answer determines whether an AI initiative succeeds or faces internal resistance that no governance framework can overcome.

The most successful AI transformations treat workforce evolution as a first-order concern, not an afterthought. This means proactive communication about how roles will change, investment in reskilling programs before automation arrives, and clear pathways for employees to move into higher-value work. Organizations that announce AI initiatives without addressing workforce implications create fear that manifests as passive resistance, data hoarding, and quiet sabotage of adoption metrics.

Navigating the Politics of Central Teams Versus Business Units

The tension between AI Centers of Excellence and business units is real and often underestimated. Business unit leaders view central teams as bureaucratic obstacles that slow them down. Central teams view business units as cowboys creating compliance risks and technical debt. Neither perspective is entirely wrong.

Successful navigation requires explicit agreements on decision rights. Organizations should document precisely which decisions the center owns (platform selection, security standards, model risk thresholds) and which decisions belong to business units (use case prioritization, workflow design, user experience).

Where boundaries are ambiguous, escalation paths should be created that do not require executive intervention for routine disagreements. Most importantly, central teams should be measured on business outcomes, not activity metrics. A CoE that ships dozens of models nobody uses has failed, regardless of how impressive its technical capabilities appear.

Driving Adoption Across Skeptical Regional Teams

Global AI rollouts face a particular challenge: country or regional leaders who view centrally-driven initiatives as headquarters imposing solutions that do not fit local realities. This skepticism often has legitimate roots in past failed deployments that ignored local requirements.

The approach that works is pull over push. Organizations should identify early adopter regions with enthusiastic local sponsors, deliver measurable wins in those markets, then use those success stories, told by local leaders to their peers, to create demand from other regions. Mandates from headquarters create compliance without commitment. Peer success creates genuine adoption. Organizations that have scaled AI across multiple countries consistently report that later-stage adopters move faster than initial pilots because they have seen colleagues succeed and want the same results.

Common Pitfalls: Why AI Operating Models Fail

Understanding failure patterns is as important as understanding success patterns. Several common pitfalls consistently derail AI operating models across industries.

The Governance Paralysis Trap

Organizations frequently build comprehensive AI governance frameworks with multiple review committees, detailed risk assessments, and thorough documentation requirements. While well-intentioned, these frameworks sometimes mean that low-risk AI applications require weeks of review before deployment. Teams begin building shadow AI solutions outside the governance framework because they cannot wait for approval on straightforward use cases. The lesson: governance must be tiered by risk. Applying the same rigor to a meeting summarization tool as to a credit decisioning model creates bottlenecks that drive workarounds and ultimately undermine governance objectives.

The Platform Without Adoption Problem

Many enterprises invest heavily in AI platforms with impressive capabilities, only to find utilization remains low months after launch. The pattern is consistent: the platform is technically excellent but requires specialized skills that business teams lack. No self-service tools exist for common use cases. Training is available but optional. The platform team measures success by features shipped rather than business problems solved. The lesson: platforms succeed only when accompanied by enablement, training, and self-service capabilities that meet users where they are. Technical capability without organizational enablement produces expensive shelfware.

Underestimating Data Complexity in Global Deployments

In multi-country or multi-business-unit AI deployments, the biggest challenge is rarely the operating model structure or the technology platform. It is discovering that data definitions that seemed standard vary significantly across contexts. What counts as a "customer" differs between units. Date formats create subtle bugs. Local regulatory requirements mean certain data fields exist in some regions but not others. Organizations consistently budget adequate time for user interface localization but inadequate time for data harmonization. The lesson: in global deployments, data variation is typically the hardest problem, and it reveals itself only after implementation begins. Operating models must account for this reality.

Confusing Governance with Operating Model

The most frequent conceptual mistake is equating AI governance with the AI operating model. Organizations implement governance frameworks and believe they have addressed the operating model challenge. Governance addresses risk and compliance; an operating model addresses how work gets done. An organization can have excellent governance and still fail to scale AI because it lacks the delivery mechanisms, integration capabilities, and scale structures that operating models provide. Governance is necessary but not sufficient.

Build Versus Buy: Making Vendor and Platform Decisions

Every AI operating model must address the fundamental question of what to build internally and what to purchase. This decision has significant implications for cost, speed, capability, and long-term flexibility.

A Framework for Build Versus Buy Decisions

Organizations should build internally when the AI capability represents a core competitive differentiator, when requirements diverge significantly from available solutions, or when data sensitivity prevents use of external platforms. Buying makes sense when the capability is mature, commoditized, and well-served by vendors, when speed to deployment matters more than customization, or when internal expertise to build and maintain the solution is lacking. The mistake organizations make most frequently is building when they should buy, typically because engineering teams want interesting projects rather than because the business case supports custom development.

Evaluating AI Vendors Effectively

Vendor evaluation for AI solutions requires different criteria than traditional software procurement. Beyond standard factors like functionality, security, and cost, organizations should evaluate vendors on model transparency (can decisions be understood?), data handling practices (where does data go, and how is it used for model training?), integration capabilities with existing technology stacks, and the vendor's approach to model updates and versioning. Organizations should request evidence of performance on data similar to theirs, not just benchmark results on public datasets, and insist on pilot periods with actual data before committing to enterprise agreements.

Contract Negotiation Strategies for AI Vendors

Organizations that approach AI vendor negotiations strategically can achieve significant cost reductions. Effective tactics include consolidating purchasing power across business units or countries into a single enterprise agreement rather than allowing fragmented procurement. Volume tier commitments in exchange for unit price reductions, with contractual protections if volumes fall short, create mutual benefit. Most-favored-customer clauses ensure the organization receives any better terms offered to comparable customers. Price caps on annual increases prove valuable as AI demand surges. Contractual flexibility to reduce consumption without penalty protects against changing business needs. The leverage in AI vendor negotiations comes from demonstrating sophistication as a buyer who understands the market, has evaluated alternatives, and will walk away from unfavorable terms.

The Day Two Problem: What Happens After Deployment

Most AI content focuses on building and deploying models. But deployment is the beginning, not the end. Organizations that extract sustained value from AI invest as heavily in post-deployment operations as in initial development.

Model Drift and Performance Degradation

AI models degrade over time. The world changes, user behavior evolves, and the patterns the model learned become less relevant. This phenomenon, called model drift, is not a bug but an inherent characteristic of machine learning systems. Operating models must include monitoring for drift detection, with clear thresholds that trigger investigation. Leading indicators such as input data distribution shifts and prediction confidence changes provide early warning before business metrics decline. Clear ownership must be established: who is responsible for monitoring model health, and who has authority to take models offline when performance falls below acceptable levels?

Retraining Triggers and Model Lifecycle Management

Every AI solution needs a defined retraining strategy. Some models require scheduled periodic retraining. Others need event-triggered retraining when drift exceeds thresholds. Still others require continuous learning with appropriate safeguards. Retraining triggers, approval requirements, and testing protocols should be documented before deployment. Model updates should be treated with the same rigor as software releases, including staging environments, regression testing, and rollback capabilities. Organizations that skip this discipline inevitably face the crisis of a model behaving unexpectedly in production with no clear path to remediation.

Ownership Handoffs: From Development to Operations

Who owns an AI model after the development team moves to the next project? This question exposes operating model gaps more than any other. Clear handoff protocols must define operational ownership, including who monitors daily performance, who handles user issues, and who coordinates with business stakeholders. Support tiers should specify which issues operations can resolve independently and which require data science involvement. Documentation requirements must establish what artifacts the development team must provide before handoff is complete. Escalation paths should clarify how operations summons development resources when unexpected issues arise. Without these protocols, AI solutions become orphans that slowly degrade while development and operations teams point fingers at each other.

Agentic AI: New Operating Model Requirements for Autonomous Systems

The emergence of agentic AI, systems that autonomously plan workflows, invoke tools, and execute actions, fundamentally changes the requirements for AI operating models. What worked for copilot-style AI that augments human work becomes insufficient when AI agents can take independent action with real-world consequences.

Agent Identity and Authorization Architecture

In traditional applications, identity and authorization happen at user login. In agentic systems, agents need their own identity layer that is evaluated at runtime with every action. Practically, this means each agent type receives a distinct identity with defined permissions. Permissions specify not just which systems the agent can access but what actions it can perform (read versus write, query versus modify). Authorization checks occur at action time, not session start. Permissions can be dynamically scoped based on context; for example, an agent requesting a financial transaction may have different limits based on transaction type or amount. Audit logs must capture every agent action with sufficient detail to reconstruct what happened and why. Organizations deploying agents for financial operations or customer-facing actions must implement these controls or accept unacceptable risk exposure.

Multi-Agent Orchestration: Preventing Conflicts and Infinite Loops

When multiple agents operate in the same environment, coordination becomes critical. Without proper orchestration, agents can take conflicting actions, enter infinite loops where one agent's output triggers another's input indefinitely, or compete for resources in ways that degrade performance. Orchestration standards must address resource locking to prevent conflicting actions on the same data or systems, loop detection to identify and halt circular invocation patterns, priority hierarchies that govern which agent proceeds when conflicts arise, and communication protocols that define how agents share context and coordinate work. Early agentic implementations that skip orchestration consistently discover these problems in production when an agent loop consumes unexpected compute resources or conflicting actions corrupt data.

The Control Tower: Human Oversight at Scale

The control tower concept extends traditional monitoring into active human oversight of agent operations. Unlike dashboards that display metrics for human review, a control tower provides real-time visibility into agent actions and decisions, defined intervention points where humans must approve high-risk actions, escalation mechanisms for agents to flag uncertainty and request human guidance, kill switches that allow rapid agent shutdown when necessary, and audit capabilities that support after-the-fact review and investigation. The control tower is not a single tool but an integrated capability combining technology, processes, and defined human roles. Building it requires investment, but operating high-autonomy agents without it invites consequences that dwarf the implementation cost.

Federated Hub-and-Spoke: The Emerging Pattern for Agentic AI

For complex enterprises, a federated hub-and-spoke model is emerging as the necessary target state for the agentic era. The central hub provides enterprise-wide technical governance and safety rails. Business-specific hubs define use cases, bring domain expertise, and manage the human teams that oversee domain-specific agents. This structure delivers centralized safety with distributed innovation, governed by enterprise standards but executed with business context.

Even organizations that prefer decentralized execution must recognize a critical truth: in the agentic era, decentralized governance creates unacceptable risk. An autonomous agent built independently by one business function can create enterprise-wide consequences if it hallucinates during a critical system update. Teams must align on security baselines and audit requirements regardless of how independently they operate. Decentralized execution remains viable. Decentralized governance invites chaos.

Tiered Governance: Right-Sizing Control to Risk

Not every AI agent requires the same level of oversight. Effective AI operating models in the agentic era implement tiered governance based on agent autonomy and business impact. Personal productivity agents handling research and summarization carry lower risk and can operate under lighter governance. Business workflow agents touching financial operations, customer actions, or compliance requirements demand rigorous controls and clearly defined human ownership. The optimal approach segments agents by risk profile and applies governance proportionally, avoiding both the paralysis of over-governance and the danger of under-governance.

Scale Mechanisms: From Single Success to Enterprise-Wide Value

Choosing an ownership pattern is necessary but insufficient. Organizations must also establish mechanisms for scaling successful AI solutions across the enterprise. Two complementary approaches have proven effective.

The AI Factory: A Replication Engine for High-Volume Patterns

The AI factory applies assembly-line principles to AI delivery, enabling high-volume, repeatable patterns with predictable timelines and outcomes. This approach standardizes pipelines and creates reusable components while allowing domains or countries to plug in specific use cases. The factory model works particularly well for common AI patterns like document processing, customer service automation, or predictive analytics where the underlying approach can be templated even as specific applications vary. Organizations with multiple business units or geographic presence benefit enormously from this approach, as it enables rapid replication of proven solutions.

Building an effective AI factory requires investment in documentation, tooling, and training. Solutions must be designed for replication from the outset, with clear separation between the core capability and the context-specific configuration. Organizations that treat replication as an afterthought consistently struggle to achieve factory-level efficiency.

The Center for Acceleration: Enabling Speed Without Sacrificing Safety

The center for acceleration is often confused with shared services, but serves a distinct purpose. While shared services provide foundational platforms and minimum controls, a center for acceleration actively reduces friction so teams can ship AI solutions safely without reinventing governance, compliance, and quality processes for each initiative.

This enablement layer provides approved templates and patterns that teams can adopt, reusable components that accelerate development, pre-defined guardrails that simplify compliance, and streamlined review paths that reduce time-to-deployment. The center for acceleration transforms governance from a gate that blocks progress into a guide that speeds delivery. Teams no longer need to figure out how to satisfy security, privacy, and compliance requirements from scratch; the center provides the playbook.

How to Choose an AI Operating Model: A Decision Framework

Selecting the right AI operating model requires honest assessment of an organization's current state and strategic objectives. Three questions guide this decision.

First, does the organization need the center to build or to enable? If AI capabilities do not exist in business units and the central team must deliver solutions, a more centralized model makes sense initially. If business units have emerging capabilities and need support to move faster, an enablement-focused model becomes appropriate.

Second, do priority workflows require cross-unit or cross-country integration? If the highest-value AI opportunities span multiple business units or geographies, the operating model must facilitate that integration. Pure decentralization will fail to capture this value.

Third, how strict must controls, evaluation, monitoring, and escalation paths be? Highly regulated industries or organizations with significant AI risk exposure require more robust centralized governance. Organizations with lower risk profiles have more flexibility in how they structure oversight.

Critical Questions Every AI Leader Must Answer

When an AI system is wrong in production, who owns escalation and the outcome: IT, product, or risk? If an organization cannot answer this question clearly, its AI operating model has a fundamental gap. This single question exposes whether AI has been truly operationalized or merely deployed as technology.

Similarly, leaders should ask: is the bigger bottleneck shared platforms or shared decision rights? Many organizations invest heavily in AI platforms while leaving decision rights ambiguous. The result is powerful technology that cannot be deployed because no one has authority to approve its use.

Finally, consider: does governance enable speed or create friction? Governance that requires weeks of review for low-risk AI applications will drive teams to work around the system. Effective operating models implement tiered governance that applies rigor proportional to risk.

From Blueprint to Reality: Building an Effective AI Operating Model

An AI operating model is what turns a successful pilot into something an organization can run, govern, and scale. It is not a one-time design exercise but an evolving capability that must adapt as AI technology advances and the organization matures.

Organizations should start by establishing the shared services foundation that every model requires. Then, honest assessment reveals which ownership pattern fits the current state and trajectory. Scale mechanisms prevent successful solutions from remaining isolated. The human side of transformation deserves the same rigor applied to technology. Day two operations require planning before deployment. And preparation for the agentic era means building governance structures that can accommodate AI systems that act autonomously.

The organizations that will lead in the AI era are not those with the most sophisticated models or the largest data sets. They are the organizations that build the operational capability to deploy AI consistently, safely, and at scale. That capability starts with the AI operating model.

The question is not whether an organization will adopt AI. The question is whether it will build the operating model that allows AI to transform the enterprise, or whether AI investments will remain a collection of disconnected experiments that never achieve their potential.

AI Operating Model Selection Checklist

Use this diagnostic to determine which operating model pattern best fits an organization. Answer each question based on current state, not aspirational state.

Section A: Organizational Maturity

1. Where does AI capability currently reside in the organization?

a) Primarily in a central team (IT, data science, or innovation group)

b) Split between a central team and some business units

c) Distributed across business units with minimal central coordination

2. How would you characterize the organization's AI track record?

a) Early stage: mostly experiments and pilots, few production deployments

b) Developing: several production AI systems, learning how to scale

c) Mature: multiple AI systems in production, established practices

3. Do business units have dedicated data science or ML engineering resources?

a) No, all technical AI work requires central team involvement

b) Some business units have limited capability; most rely on central

c) Yes, most business units can execute AI projects independently

Section B: Integration Requirements

4. How much do the highest-value AI opportunities span multiple business units?

a) Significantly: the best opportunities require cross-unit data or processes

b) Moderately: some opportunities are cross-unit, most are unit-specific

c) Minimally: business units operate independently with distinct opportunities

5. Could a successful AI solution in one unit or country be replicated elsewhere?

a) Yes, with minor modifications (high replication potential)

b) Partially: core approach reusable but significant adaptation needed

c) Rarely: each unit or country has unique requirements

6. How standardized are data definitions and processes across units?

a) Highly standardized with common data models and process definitions

b) Moderately standardized in some areas, significant variation in others

c) Limited standardization; each unit has evolved independently

Section C: Risk and Regulatory Environment

7. How heavily regulated is the industry with respect to AI and data use?

a) Heavily regulated (financial services, healthcare, government)

b) Moderately regulated with specific requirements for certain use cases

c) Lightly regulated with general data protection requirements

8. What is the potential business impact if an AI system fails or produces errors?

a) Severe: regulatory penalties, significant financial loss, safety risks

b) Moderate: customer impact, operational disruption, reputational risk

c) Limited: internal efficiency loss, manageable with manual workarounds

9. How well established are AI governance and control frameworks?

a) Comprehensive with defined policies, review processes, and controls

b) Developing with basic policies but inconsistent application

c) Minimal or nonexistent formal AI governance

Section D: Speed and Scale Requirements

10. What is more important for the AI strategy in the next 12 to 18 months?

a) Building foundational capabilities and establishing consistent practices

b) Accelerating delivery while maintaining quality and controls

c) Maximizing speed and enabling business unit autonomy

11. How many AI solutions are expected to be deployed in the next two years?

a) Fewer than ten focused solutions

b) Ten to fifty solutions across multiple domains

c) More than fifty solutions at scale

12. Is the organization planning to deploy agentic AI (autonomous agents)?

a) Yes, in high-stakes business processes within 12 months

b) Exploring agentic AI for lower-risk applications

c) Not currently planning agentic AI deployments

Interpreting the Results

Tally responses in each section and use the following interpretation guide.

Predominantly (a) responses: Centralized Model Recommended

The organization would benefit from a centralized AI Center of Excellence model. Focus on building foundational capabilities, establishing consistent standards, and developing governance frameworks before distributing AI development to business units. This model provides the control and consistency needed for the current maturity level and risk profile.

Mixed responses with majority (b): Hub-and-Spoke Model Recommended

The organization is well-suited for a hub-and-spoke model. Build a central hub that owns platforms, standards, and risk controls while enabling business-aligned spokes to drive delivery and adoption. This model balances governance with speed and supports scaling while maintaining appropriate oversight. Consider a federated variant if operating across multiple business units or geographies.

Predominantly (c) responses: Federated or Decentralized Model Possible

The organization may support a more decentralized model, but caution is warranted. Ensure strong shared services and governance frameworks are in place before fully distributing AI ownership. If deploying agentic AI, maintain centralized governance even with decentralized execution. Watch for duplication of effort and inconsistent controls as common failure modes.

Special Consideration for Agentic AI (Question 12a):

Organizations planning to deploy agentic AI in high-stakes processes should shift one level toward more centralized governance regardless of other scores. Agentic AI requires robust control towers, agent identity management, and defined human oversight that only centralized or strong hub models can provide.

Next Steps After Assessment

Document the current state across each dimension. Identify the largest gaps between the current operating model and the recommended target. Prioritize closing gaps in governance and shared services before addressing ownership structure. Build a roadmap that sequences capability development over 12 to 24 months. Review and reassess quarterly as AI maturity evolves.