Companies should treat generative AI as a fast-moving capability stack, not a settled platform. Build and test useful workflows now, but make major commitments reversible where interfaces, provider roles, and the economics of deployment remain uncertain. The strategic question is not only which model performs best; it is which investments will still matter as the surrounding system changes.
What “platforming” means for AI
In Kevin J. Boudreau’s August 26, 2026 article for MIT Sloan Management Review, “platforming” describes the work of creating structures that let organizations confidently build on a technology. Generative AI’s capabilities are advancing quickly, but the technological, industrial, and institutional architecture needed to support broad complementary innovation is still taking shape. O’Reilly’s listing describes the article as an intermediate, seven-page read and offers access through its learning service.
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That distinction matters: progress in models does not by itself establish durable application interfaces, stable provider roles, or a settled way to organize AI work. The relevant question for a business is whether the surrounding system is reliable enough for a particular investment—not whether AI as a whole is moving forward.
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Which parts of the AI stack are still shifting?
The emerging stack spans specialized hardware, cloud computing, foundation models, and applications. Lower layers are more recognizable, while applications and deployment remain unsettled. Organizations are experimenting with model APIs, chatbot interfaces, agents, middleware, AI embedded in existing products, and enterprise deployments. These are current approaches, not a guarantee that each will become a durable standard.
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As a result, a decision to build around one interface or provider can become costly if the architecture changes. Meanwhile, a workflow that can switch models or use AI through replaceable components may preserve more room to adapt. The right degree of flexibility depends on the value of integration and the cost of maintaining alternatives.
Why rapid model progress does not guarantee durable returns
Boudreau’s analysis highlights several forces that can make returns on complementary products uncertain. Customers may use multiple models, switching costs may be limited, and training and inference are costly. Open-weight models can intensify competition, while widely shared model capabilities can make it easier for rivals to imitate an application’s features.
As Boudreau puts it, “The same foundation models that reduce the cost of innovation also reduce the cost of imitation.” That is a competitive-dynamics observation, not a claim that every vendor or organization faces identical economics. It means access to a capable model alone may not protect a business’s advantage. Differentiation may depend on complementary assets and capabilities—such as integration, workflow design, or organizational know-how—that remain useful when model choices change.
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Separate capability from the surrounding system
Assess model performance separately from the application interfaces, infrastructure, and organizational processes needed to put it to work. A model evaluation can answer whether a capability is promising; it cannot, on its own, establish whether a workflow will be reliable, economical, or durable in your organization.
Prefer reversible commitments where uncertainty is high
Use pilots and modular designs to learn before making commitments that are hard to unwind. Where practical, avoid tying a critical workflow to a single model or interface before its value and operational requirements are clear. Reversibility is not an end in itself: a tighter integration may be justified when it delivers a proven benefit that exceeds the cost of dependence.
Build complements that survive model changes
Look for investments whose value comes from solving a real organizational problem, rather than from access to a commonly available model capability. Durable complements may include the ability to integrate AI into existing work, manage its use, and adapt processes as tools evolve. The relevant test is whether the capability remains useful if the underlying model changes.
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Evaluate the workflow in context
Judge a deployment by how it fits the organization’s work, not by a model comparison alone. Consider what changes in the workflow, what integration it requires, and whether the resulting value justifies ongoing training and inference costs. A technically impressive model is not by itself evidence of a successful business investment.
A practical framework for comparing AI investments
Use these questions to compare a proposed build, deployment, or partnership. They are decision axes derived from the issues Boudreau discusses, not a ranking or scoring system supplied by the author.
- Durability: Would the investment still be useful if the model, interface, or deployment pattern changed?
- Provider dependence: How much does the workflow rely on one provider, and what would it take to change?
- Differentiation: What value comes from your organization’s own complementary capabilities rather than shared model access?
- Switching costs: Are the costs of moving providers or redesigning the workflow understood, and are they proportionate to the expected benefit?
- Integration capacity: Can the organization implement, operate, and adapt the technology in the setting where the value is supposed to arise?
If these questions have weak or unknown answers, the case for a large, difficult-to-reverse commitment is also weak. A contained experiment may still be worthwhile when it answers a consequential question.
What the argument does—and does not—establish
The article offers a strategic lens on AI’s unsettled architecture, not an independent evaluation of particular systems or proof that a specific deployment will succeed. Its central implication is to learn faster than you commit and to build assets that can withstand architectural change. It does not establish that every layer is equally unstable, or that organizations should postpone useful experimentation.
A preview of the article mentions an estimate of about 2.4 billion monthly users of generative AI platforms, attributed only to “some estimates,” without identifying the original publisher or report year. That figure therefore cannot serve as a fully attributed measure of adoption. The argument about investment uncertainty does not depend on it.
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