Institutionalizing AI: Building Systems of Enablement

For AI to succeed, it takes more than just a robust operating model and a well-defined strategy.

According to a recent study by McKinsey & Company (2023), only 15% of organizations have successfully scaled AI across multiple business units. This raises a critical question: While many companies are investing heavily in AI, why are only a few realizing its full, enterprise-wide impact?

Based on my doctoral research on transformational leadership in the age of AI, the true differentiator is often not the technology itself, but the "software of leadership"—the cultural, structural, and behavioral systems that enable scale, sustainability, and lasting impact.

As discussed in my article, 'Building a Future-Ready AI Operating Model', foundational elements such as data enablement, tooling, and customer-centric use cases are essential to bridge the strategy-execution gap. However, these elements alone are not sufficient. To truly scale AI across the enterprise, leaders must move beyond operational readiness and build systems that embed AI into the fabric of how the business operates.

Leaders must implement what I call systems of enablement (SOEs)—organizational mechanisms that institutionalize AI capabilities across the enterprise. These SOEs catalyze enterprise-wide success and encompass process design, talent development, cultural readiness, and a supportive work environment.

The following best practices illustrate how leading organizations are successfully accelerating their AI momentum by strategically leveraging SOEs.

1. AI-first Process Design

True transformation does not begin by merely digitizing existing processes, but by reimagining them. There is little value in optimizing processes that should not exist in the first place. The following are recommended practices to catalyze AI-led execution:

• Conduct design thinking workshops to uncover pain points experienced by business teams and identify opportunities where AI can add value. (Note: Not all identified opportunities will lead to AI applications; AI use cases will form a subset of these.)
• Engage external consultants to benchmark processes against global best practices and identify areas for reengineering.
• Create cross-functional design pods including users, technologists, data scientists, and domain experts to define requirements and develop execution roadmaps.

These practices foster collaboration across business units and build a bottom-up culture of experimentation, enabling teams to scope, prototype, and test AI use cases more effectively.

2. Build Mentoring Teams and Communities of Practice

Appoint AI mentors in each functional area (e.g., HR, finance, legal, payroll) to guide project scoping and experimentation. These mentors should assess not only the technological feasibility of AI solutions but also their commercial viability. In parallel, establish internal communities of practice to:

• Document domain knowledge and common pain points
• Share best practices and lessons learned from AI pilots
• Promote continuous learning and idea cross-pollination

Open knowledge sharing and the development of centers of competency can help create a culture of experimentation. Teams can learn from one another and build the momentum to move from prototypes to production-grade AI solutions.

3. Clarify Roles and Strengthen Accountability

To attract and retain top AI talent, organizations must define clear roles and career paths. Human resources plays a vital role in institutionalizing this structure by establishing and supporting key roles, such as:

• AI product owners who steer use case development based on end-user needs and measurable business impact
• Risk officers who ensure compliance and enforce governance guardrails
• Data stewards who manage data quality, readiness, and fairness
• AI ethics committees that monitor transparency, explainability, and unintended consequences

HR must also create incentives and job rotation policies that nurture the right environment and culture to help SOEs succeed.

4. Drive Cultural Nudges and Build AI Literacy

Execution is not about top-down mandates and slogans; it is about consistent reinforcement and behavioral nudges.

• Reward experimentation—even failed initiatives—when the lessons are clearly documented and shared. Encourage teams to fail fast and learn continuously.
• Promote cross-functional collaboration by rotating employees into central AI teams and transitioning them back into business roles, enhancing integration.
• Institutionalize AI literacy through structured certification programs. Consider gamified learning platforms to foster healthy competition and engagement. Leaders must model this behavior by actively participating in learning initiatives.
• Enable hands-on experimentation through AI labs and sandbox environments accessible to employees at all levels.

Building AI execution capability is not a one-time effort. It requires a comprehensive set of actions that span process redesign, cultural transformation, role clarity, and capability building. Only when these systems work together can AI transition from pilot initiatives to enterprise-wide transformation.

As Porter (1996) observed in his seminal work ‘What is Strategy’, strategy and execution depend on a system of reinforcing activities. Similarly, an AI operation, along with the model and systems of enablement, compels each other to succeed.

Organizational enablers such as process reimagination, structured mentoring, role clarity, and a culture that supports experimentation and continuous learning are essential for leaders to establish. Together, these elements form a system of enablement (SOE) that serves as a catalyst for the AI operating model, driving sustainable impact and enterprise-wide transformation.

As AI continues to evolve, so too must the systems that support it. Organizations that invest in building these enabling structures today will be best positioned to lead tomorrow. The question isn’t whether AI will scale—but whether your organization will scale with it.