Building a Future-Ready AI Operating Model: Synergising Top-Down Vision with Bottom-Up Execution

A recurring challenge for CXOs is bridging the gap between AI strategy and demonstrating measurable business outcomes. This is not due to a weak vision or lack of ambition, but is due to the complexity of translating AI aspirations into scalable execution. The missing link is often a clearly defined, bottom-up AI operating model—a framework of practices and enablers that advance AI from pilots to enterprise-wide adoption.

An issue that consistently surfaces in my conversations with CXOs is this:

We have a strategy, but we’re struggling to turn it into tangible results. How do we execute in a way that truly maximises business impact?

In my role, leading large deals and strategic transformation programs for some of the most complex digital engagements globally, I often share this perspective with CXOs: AI cannot be approached as a one-off experiment. It must be grounded in strong, foundational building blocks—across data, tools, and early quick-wins that build momentum. Only then can leaders truly clear the path to unlocking AI’s full potential.

From Strategy to Operating Model: What’s the Difference?

To understand an operating model construct better, let us first get some of the fundamental definitions right. Vision, strategy, operating model, etc, are all loosely used in business parlance. Hence, it is important first to define them clearly and understand the differences before we dive into the roadblocks and enablers.

• AI Vision is the aspirational future a company seeks to achieve with AI (e.g., hyper-personalized CX, 10x operational productivity). This is the pull and priority that CEOs and boards set for the organisation.  
• AI Strategy outlines the roadmap —what to pursue, where to invest, and why. It is also about the trade-offs regarding what to do and what not to do, and how it all comes together.  
• AI Operating Model is the practical execution system: roles, processes, infrastructure, data flows, governance, and routines that make the strategy come alive. Think of it like where the rubber (Strategy) meets the road. This is the bottom-up approach that must align with strategy to deliver outcomes that CXOs expect.  
• AI Framework is a conceptual tool used to describe the design of models, governance layers, or capability maturity. Think of it like a guidepost that will make it easier to communicate and get things done.  

One can’t always fault the organizational strategy. More often than not, that’s not where the execution gap lies. Without a strong operating muscle, even the best strategies will stagnate or stumble. Let us find out why.

Ground Reality: Why Execution Stumbles

Let’s explore three key areas where execution often stumbles—drawn from my experience interacting with numerous leaders, board members, and CXOs. These insights have also been empirically validated in my doctoral thesis on ‘Transformational Leadership in the Age of AI,’ resulting in a statistically validated blueprint for leaders to scale AI execution in their organization.

1. Data Enablement & Governance

The biggest barriers to AI execution often lie in the data itself: quality issues, inconsistent availability across systems, and the absence of anonymised datasets.

In large organisations, stringent data security controls can further restrict access to clean, usable data. This is particularly true for business users, analysts, and developers, which in turn stifles AI experimentation, slows model training, and limits the ability to simulate real-world scenarios.

To address these challenges, organisations should focus on a few key enablers:

• Controlled-access data sandboxes that allow safe, governed experimentation.
• Anonymisation of internal data combined with publicly available external datasets to enrich insights.
• A shared data catalogue to improve data discoverability and usability across teams.

As we enter the age of AI agents, it is also wise to deploy a “Checker Agent” that verifies compliance with organisational policies and regulatory standards, working alongside an “Execution Agent”. This ensures that data policies, regulations, and privacy guardrails are consistently respected and never bypassed.

2. Tools, Environment & Infrastructure for Experimentation

Another significant impediment to AI execution is the absence of robust tools, scalable infrastructure, and secure environments for experimentation. In many instances, business leaders understand the challenges they face and even the potential solutions, but lack the technical capabilities to implement those solutions effectively.

Without these enablers, skilled teams can become underutilized and disempowered. Mid-level managers may have limited access to scalable testing environments, while developers face obstacles in moving prototypes into production due to inadequate deployment mechanisms.

To overcome these challenges, organizations should:

• Implement a robust MLOps framework to support continuous model learning and deployment
• Enable workflow automation and provide access to open-source models and model catalogues (e.g., Google’s Model Garden), prioritizing re-use over building from scratch
• Establish secure, well-governed AI sandboxes for experimentation
• Create a centralized data lake to fuel AI workbenches with high-quality data

Without secure, structured experimentation environments, it is unrealistic to expect even top talent to present bold proposals and execute on the comprehensive efforts needed to unlock the Return on Investment (ROI).

3. Customer-Centric Use Cases to Build Momentum

The final key dimension to accelerating AI adoption is starting the journey in areas where the benefits are most visible and measurable. Customer experience transformation is one such area, offering clear, tangible outcomes that build confidence among both operating teams and senior leadership. It also provides a more straightforward path to demonstrating ROI. Initiatives with high visibility and measurable results are more likely to secure continued funding, creating a ripple effect that extends into other functions. AI presents a wealth of use cases to personalize communication, enhance service delivery, and strengthen customer loyalty.

Quick wins in this space include:

• Personalized customer engagement through AI-driven recommendation engines
• 24/7 intelligent virtual assistants for real-time support
• Predictive service and issue resolution

By prioritizing customer experience as an early focus area, leaders can build momentum and credibility around their AI initiatives. The insights and operational learnings from these use cases can be invaluable. They can then be leveraged to fine-tune the AI operating model and scale adoption across other parts of the organization more effectively.

A Practical Operating System for AI

AI is not just a technology transformation—it represents a systemic shift in how the organization operates and delivers value. It is clear from my interactions with CXOs that AI initiatives rarely fail due to a lack of vision or strategy. The real challenge lies in the execution—and without a robust operating model, strategy remains theoretical, stifling the very innovation AI is meant to unlock.

The roadblocks, as we discussed, often form a vicious triad: 

Security compliance requirements restrict data, which in turn limits experimentation and meaningful outcomes, ultimately hindering talent development.  Unless all three are addressed in unison, AI strategies will struggle to break out of the pilot trap.

By focusing on three foundational pillars—data enablement, tooling and infrastructure, and customer-centric use cases that deliver early, measurable wins—organizations can lay the groundwork for scalable AI adoption.

Success will come to those organizations and senior leaders who listen and enable from the bottom up, align strategy top-down with the operating model, and relentlessly execute their way to meaningful impact!