From AI table stakes to AI advantage: Building competitive moats

If everyone is special, then no one is.” That line, adapted from the movie The Incredibles, captures the essence of a key issue with AI today. While AI adoption has exploded (nearly nine in ten organizations now use AI in at least one business function), most companies are deploying the same large language models (LLMs) to improve productivity. If everyone has the same advantage, it’s not really an advantage.

This is a trap we’ve seen before. During the digital-transformation wave, companies rushed to develop websites and apps, but competitive advantage—the distinct set of hard-to-replicate assets and operating models a company creates that earns superior returns over time—didn’t automatically follow.

The lesson: Apps and tools can be copied. Value comes from building advantages that are hard for competitors to replicate—that is, competitive moats. That’s a critical lesson to bear in mind as CEOs and boards consider how to capture their fair share of the enormous value just from generative AI that’s at stake.

To better focus that thinking, the authors identified moats—six strategies and three capabilities—that can provide a competitive advantage. These moats shouldn’t be particularly new to business leaders, but AI has shifted the dynamics in each of them. Understanding what that means is the path to leaping from AI as table stakes to AI as an abiding competitive advantage.

Strategic moats.  While a strategic moat is difficult for others to replicate, it should also represent an area where the business already has an advantage. This view can help CEOs determine which area to focus on and how to invest.

Economies of scale: Infrastructure to harness speed and scale.  If cognitive work represents high costs in your business, focus on what you need in place to build the infrastructure so economies of scale from AI work in your favor. Senior leaders should consider consolidating volume across business units or geographies, building shared AI platforms, or using M&A to push additional volume through the same AI stack. 

Privileged data: Treating data like an asset class.  Manage data as a strategic asset class. That starts with prioritizing the data that underpins your differentiation and instrumenting systems to capture, enrich, and maintain this data at scale. Demonstrate responsible data stewardship, such as stringent data protection measures to avoid future regulatory constraints.

Embeddedness: Making switching expensive.  What this means for you: For vendors, understand where the points of deep workflow integration are, and ensure services have feedback loops so performance improves and value increases with use. For customers, every workflow you hand over to an embedded AI system is a bet on the vendor’s road map, pricing trajectory, and continued existence. Negotiate data rights and portability up front, ensure you retain access to that learning in some usable form, and maintain data protection standards.

Network effects: AI as the network architect.  Look for opportunities to create network effects that you previously dismissed as too costly or risky. If you have an existing network, ensure your models improve matching quality, reduce noise, and increase trust with every transaction. As transactions increasingly flow through AI agents, be clear about who owns the agent and who captures value when agents transact.

Business model disruption: Shifting who owns the customer and how value gets priced.  If you’re at risk of being disintermediated, identify what your economic leverage points are to protect and enhance customer relationships. If you bill by time or throughput, identify how to switch to outcome-based pricing.

Constrained assets: AI meets the physical world.  As AI commoditizes knowledge, focus on where your company controls the physical systems that competitors cannot easily replicate. The goal is not simply to apply AI to existing assets, but to build physical networks whose value compounds when combined with intelligence.

Capability moats.  A capability moat is an organizational strength that is difficult to build but enables a company to repeatedly translate AI into sustainable advantage.

Rewiring for velocity: Increasing speed of learning and deployment.  Treat organizational velocity as a strategic differentiator, not an operational metric. Measure your clock speed from idea to proven value to scaled deployment—and remove the bottlenecks that slow it down. Companies looking to truly rewire their organization have to start by building conviction across the entire C-suite, target domains where they have economic leverage, and commit both real resources and their top people to lead the transformation.

Regulation and compliance: Integration into AI solutions.  Treat compliance as strategic infrastructure by embedding auditability, transparency, and governance directly into your AI systems. This helps regulatory compliance scale with your growth. Clarify which parts of your value proposition depend on regulated activities and where you may face gray-zone competition.

Trust: Your anchor for the customer relationship.  Winning companies treat trust as a speed enabler. Identify the core sources of trust in your businesses (safety, fairness, reliability, transparency) and embed them directly into your AI systems through automated governance and policy-as-code controls. Integrate risk and compliance guardrails early in AI solution development.

The forces unleashed by AI have shifted the locus of competitive advantage. For boards and CEOs, this suggests a clear agenda:  Align on your moats—and make trade-offs explicit.  Build the enabler systems that support your strategic moat.  And Govern the moat like a core business, not a set of experiments.

In the age of AI, competitive advantage won’t come from having the cleverest model. It will come from being the organization that turns common models into uncommon moats faster than anyone else.

show less