AI Has Become an Enterprise Operating Mandate

Across our client conversations, the shift around artificial intelligence is increasingly clear. CEOs are no longer debating whether their organizations should adopt AI. They are communicating that adoption is critical to improving efficiency, modernizing the business, strengthening products and services, and remaining competitive.

The challenge is no longer proving that AI matters. It is determining where AI can create meaningful value and building an operating model capable of delivering it.

That model will look different across organizations. In some companies, AI is becoming a direct CEO-led transformation priority. In others, it is being embedded into products, platforms, assets, and customer experiences.

What these approaches share is a need for clear ownership, focused priorities, and leaders with enough authority to move the organization from experimentation to execution.

AI is Moving Closer to the CEO

At one large industrial organization, AI has become a direct CEO priority.

The company’s Chief Operating Officer shared that hardly a day passes without the CEO reinforcing the importance of AI adoption. The organization has also appointed a dedicated AI strategist who reports directly to the chief executive.

The focus is practical. At the corporate level, AI can improve operational efficiency and automate routine work. Across the broader enterprise, it can help accelerate the modernization of legacy technology and support routine plant and production decisions.

This reflects the opportunity facing many traditional businesses. These organizations are often managing aging systems, fragmented data, operational complexity, and pressure to improve productivity. AI may accelerate modernization, but only when it is connected to the realities of the business rather than managed as a separate innovation program.

The reporting structure is also significant. Placing AI accountability close to the CEO provides the visibility and authority needed to coordinate decisions involving operations, technology, investment, risk, talent, and multiple business units.

Without that level of ownership, AI efforts can remain fragmented across functions or stall when they encounter established systems and competing priorities.

AI is Becoming Part of the Customer Experience

At a global commercial real estate organization, AI is being applied across both internal operations and the services provided to clients.

Because the company manages billions of square feet of real estate, the opportunities extend across asset management, leasing, investment decisions, predictive maintenance, and energy consumption.

Within real estate portfolios, AI can identify potential maintenance issues before they become more disruptive or expensive. It can analyze building performance to improve energy use and operating efficiency.

Within leasing, AI can reduce administrative complexity and shorten transaction timelines by accelerating documentation, analysis, and workflow management.

In this example, AI is not only an internal efficiency tool. It is becoming part of the company’s product and client value proposition.

The organization is also hiring a senior AI product strategy leader to guide end-to-end strategy and execution. The role is expected to sit within digital and technology leadership, reflecting the need to integrate AI into products, platforms, and the broader technology roadmap.

This is a different model from the industrial example. One organization is centralizing AI close to the CEO to support enterprise modernization. The other is positioning AI within digital and product leadership so it can be embedded into client solutions.

The right structure depends on the business problem the organization is trying to solve.

The Mandate Matters More Than the Title

There is no universal title or reporting line for enterprise AI leadership.

In technology-native companies, accountability may sit within product, engineering, data, or digital leadership. In more traditional businesses, AI leaders and task forces are increasingly being held accountable to the CEO because the work touches legacy systems, capital investment, workforce changes, and multiple parts of the enterprise.

The title matters less than the authority attached to the role.

An AI leader will struggle to deliver enterprise outcomes if the position is limited to advising leadership, promoting tools, or coordinating isolated pilots. The individual needs access to senior decision-makers and the ability to influence investment, data, technology, talent, governance, and business priorities.

The role also requires more than technical expertise. Enterprise AI leaders need commercial judgment, organizational influence, and the ability to translate technology into business outcomes.

Organizations must also balance centralized direction with execution close to the work. A central team can establish priorities, governance, infrastructure, and investment standards. Business and functional leaders are often better positioned to identify where AI can solve meaningful problems.

The strongest model combines both: enterprise ownership with distributed execution.

Five Questions to Move from Mandate to Execution

Declaring AI a priority does not create value on its own.

Organizations can launch pilots, purchase tools, and establish task forces without materially changing performance. The more important work is deciding where AI should change the business and what must be true for those changes to scale.

Leadership teams should begin with five questions:

1. Where can AI materially improve efficiency, growth, customer value, or decision-making?

2. Which opportunities are significant enough to justify investment and organizational change?

3. What data, technology, governance, and talent will be required?

4. Who owns the outcome and has the authority to work across functions?

5. How will the organization decide which initiatives to scale, which processes to redesign, and which efforts to stop?

These questions shift the conversation from broad ambition to practical choices. They help leaders distinguish between activity and progress, prioritize the opportunities with the greatest potential value, and identify the organizational changes required to support them.

Companies that have the resources but lack a clear starting point may benefit from an experienced strategy or transformation partner with AI expertise. That support should go beyond technical guidance. It should help leaders connect AI to the business model, assess organizational readiness, and create a practical roadmap for execution.

External expertise can support the process, but leadership accountability cannot be outsourced.

The Operating Model Will Determine the Outcome

AI has become an enterprise operating mandate, but the mandate will not look the same in every organization.

For one industrial company, it is a CEO-led path toward modernization and operational efficiency. For a global real estate organization, it is strengthening internal performance while becoming part of the product and customer experience.

The common thread is not a particular title, reporting line, or technology. It is the need to connect AI directly to the business.

The organizations that succeed will not necessarily be those that launch the most pilots or adopt the most tools. They will be the ones who determine where AI can create meaningful value, establish clear ownership, and build the operating discipline required to move from ambition to execution.

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