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Enterprises that embraced product thinking years ago are now discovering they built the perfect foundation for an AI-first future.
The shift to an AI-first enterprise is not simply about adopting new tools or building bigger data pipelines. It is about rethinking how the organization operates, giving AI the right of first refusal to solve problems, and extending its capabilities beyond a central team into the business units and functions that own outcomes.
It is a bold ambition, and for many, the hardest part is knowing where to start. But for organizations that have already embraced a product-oriented operating model, the groundwork for an AI-first future may already be in place.
In many ways, the product model was AI’s organizational dress rehearsal. Companies that have embraced it have already restructured around enduring business capabilities and customer journeys such as “market and sell products” or “quote to cash.” They have aligned teams by domain, empowered product owners to drive measurable outcomes, and brought together cross-functional expertise spanning business, design, and technology.
That combination of empowerment, context, and accountability creates fertile ground for AI. When context is king, and it always is with AI, domain-aligned teams already possess the deep understanding of data, customers, and processes needed to identify meaningful use cases and implement them responsibly. Moreover, product teams are accustomed to operating within enterprise standards for architecture and security, the same scaffolding required to scale AI safely and sustainably.
For organizations still structured around a project-based, plan-build-run model, the road to AI is much steeper. Teams form and disband based on project timelines rather than enduring business capabilities, making it difficult to know where to embed or federate AI expertise. Without standing teams, there is no clear home for ownership, and no natural connection between business outcomes and the application of AI.
This model also reinforces dependence on a central data and AI function that is already spread thin. Demand outpaces supply, and with finite centralized resources, scaling becomes a bottleneck rather than a catalyst. In contrast, a domain-based product model allows business units and functions that already own their product outcomes to begin funding, prioritizing, and managing their own AI initiatives. This shift toward democratization, teaching each domain to fish rather than feeding them from the center, is where true enterprise-scale AI begins to take shape.
One Fortune 500 company recently discovered how natural this transition can be. Facing a fundamental business model shift from selling SKUs to delivering integrated solutions that cut across traditional organizational seams, they adopted a product domain model to strengthen collaboration between business and technology, increase visibility, and align priorities across the enterprise.
Once the model was in place, they turned immediately to AI. Because the structure was already built for alignment and empowerment, the pivot was seamless. In their B2C business unit, half a dozen product teams with empowered product owners and cross-functional resources could evolve into AI-empowered teams in an accelerated fashion. The central data and AI hub continued to provide the standards, platforms, and governance required for responsible adoption, while domain-based AI centers of excellence emerged within each product domain to drive adoption, education, and technical execution.
Those domain centers became both catalysts and guardians, spreading AI literacy, providing engineering horsepower, and ensuring security and governance standards were applied consistently. And because the organization already operated with agile ways of working and quarterly planning routines, coordinating AI priorities across teams became a natural extension of how the enterprise already worked. The result was not a reinvention of the operating model, but an evolution of it.
For organizations still living in a project world, the good news is that product and AI transformations can happen in tandem. As 2026 approaches, a year many are calling the “scale or fail” moment for enterprise AI, leaders should view this as a window to reshape the foundation that will determine long-term success.
It will not be easy. Real change rarely is. But for companies willing to invest in building durable, domain-aligned structures that connect technology to business outcomes, the payoff is profound. If a year of transformation is what it takes to unlock a decade of advantage, then the juice is well worth the squeeze.
