In my previous post, I argued that it is a strategic trap for the enterprise to wait for the emergence of an AI Utility before building an AI Factory to deploy its intelligent agents at scale. The utility provides the raw power (tokens). However, the AI Factory the process, the people, and the technology stack that utilize that power. The enterprise’s proprietary knowledge must be reflected throughout the AI Factory and is required for realizing impactful and enduring value.
While planning how AI can provide their enterprise with impactful and enduring value, its board and senior executive team often ask: “Why build an AI Factory or even a single AI application? We didn’t build a SaaS Application Factory or even the CRM application we use. We licensed Salesforce’s. Why couldn’t we apply our SaaS Playbook to address and scale our AI needs?”
These are key questions every enterprise, regardless of industry, should be asking. The answer lies in understanding that AI is not just the next generation of enterprise software. It represents a fundamental shift in how the enterprise approaches its business processes.
The SaaS Playbook vs. The AI Reality
For the last 20 years, the “Buy vs. Build” enterprise software debate has been settled. The enterprise licensed cloud-based application and infrastructure software to address its needs. In doing so, it implicitly outsourced the business processes automated by the licensed applications. The customer record became a commodity. The parcel record became a commodity. The SKU record became a commodity.
The result of this transformation was a series of cloud-based standardized enterprise Systems of Record.
Now, software vendors are pitching the same logic for AI applications. “Don’t build your own AI application, or agent. Rent ours. It’s smarter, cheaper, etc.”
But here lies a dangerous trap. An agentic application isn’t just a System of Inference (predicting text) and Action (executing a script). It is a System of Agency. Moreover, whereas today many systems of record are standalone and siloed, enterprises must plan for a time when their agentic applications will start to collaborate to execute the complex workflows that are part of their business processes.
The Difference Between Action and Agency
A state-of-the-art enterprise application today, e.g., a CRM application, is a System of Inference and Action. It:
- Infers: Predicts a customer will churn.
- Acts: Executes a rule (or script) written by a human: “If churn risk > 80%, send a discount.”
A System of Agency is different. It accepts a Goal (e.g., “Maximize Customer Lifetime Value”), and autonomously reasons and decides on the Strategy it will use to satisfy the goal. For example, it might reason: “This customer always consumes new content on the first day it becomes available. The customer has never used any of the discount offers we previously offered. Therefore, this customer responds better to new content than discounts. I will send a trailer for a new show instead of a discount coupon.”
The reasoning is the result of the enterprise’s proprietary knowledge about the customer, including the customer’s response to past campaigns. It is part of the enterprise’s knowledge base about how to maximize customer lifetime value based on each individual customer’s characteristics and behaviors across a variety of scenarios and even businesses.
A generic “Churn-Prevention Agent” offered by a vendor, will likely be less effective for the enterprise because it uses a general–purpose churn-prevention knowledge base. In other words, the agent optimizes for the ‘average’ customer, not the enterprise’s customer. Furthermore, this approach locks in the enterprise to the vendor’s knowledge model. This approach can erode margins, create unexpected liabilities, and lead to vendor lock-in.
Defining the AI Factory
An AI-first enterprise must be capable of seamlessly moving from a prioritized list of candidate AI solutions addressing important use cases to prototypes of these solutions to scaled applications that can be deployed across the enterprise. For this to work, the enterprise must establish an AI Factory.
The AI Factory integrates people, processes, data, and the AI Stack. The people come from various departments including the impacted business units that will be utilizing the AI applications to be developed, the technology organization that oversees the data and will develop the applications, and even the finance organization that will be providing the funding and measuring the financial effectiveness of the resulting solutions.
The processes (detailed in the next section) codify the enterprise’s approach on how to identify and select the right use cases to focus on, how to experiment with data and models in the process of identifying the ones that will effectively address each selected use case, how to build and deploy the AI applications that will incorporate the data, models, and other forms of knowledge identified during the experiments, and how to to orchestrate different AI applications to execute complex workflows.
The AI Stack includes the technologies the factory uses. The enterprise decides which of the stack’s layers it will own and which it will rent. The enterprise may decide to own the entire technology stack, including all its AI models, as Recursion Pharmaceuticals and Ginkgo Bioworks do. Alternatively, it may use a hybrid model, as AI-first companies Intuit, Walmart, and Visa do. They combine a third-party foundation model, in both cases OpenAI’s model, with their proprietary AI models.
The Factory’s Processes: Selection, Experimentation, Production, Orchestration
The AI Factory performs four critical processes that the enterprise must own:
- Selection: In the rush to apply AI, many enterprises do not apply rigorous criteria of which use cases and existing business processes require “AI treatment” and which new, AI-first processes must be created and use cases addressed. As a result, many efforts are disconnected and produce weak results. Building the AI Factory requires establishing the selection criteria. This is an effort that must involve leaders from across the enterprise and not only a single organization, be it business, technology, or finance.
- Experimentation: Today, most enterprises decide on and implement AI application prototypes on an ad hoc and siloed basis. AI-first enterprises make experimentation a key process of the AI Factory. They conduct many experiments before deciding which applications to scale. Data connection and unification schemes, knowledge representation approaches, and AI models are being tested. A common enterprise misconception is that AI applications must always utilize one of the massive (and expensive) Foundation Models, e.g., GPT-5. This often proves to be overkill. In many use cases, a Small Language Model, or a task-specific Vision Language Action Model, is a better selection.
- Production: The result of the experimentation to both identify the value of the application and the components that will comprise it. Agentic applications may be commoditized (in which case they can be licensed) and strategic (which must be developed internally and owned). For example, like many other enterprises, airlines license agentic applications that automate software development. However, they develop the AI applications used for flight scheduling. The flight scheduling agent of United Airlines reasons differently from Delta’s.
- Orchestration: The AI Factory acts as the anti-silo engine. It must ensure that the appropriate agentic applications are properly orchestrated and logically interconnected. When a manufacturer’s marketing agent launches a campaign, it must signal the production agent to ramp up inventory, and the distribution agent to secure freight capacity.
The Pragmatist’s Guide: You Don’t Have to Build Everything
Creating the AI Factory may appear daunting. AI-first early adopter enterprises, like Intuit and Walmart approached it as an existential imperative, build it and constantly refine it. Enterprises that belong to the late majority category may take a more measured approach. However, the important message is that today’s enterprise, starting with the Fortune 500, cannot avoid developing an AI Factory. The factory may incorporate rented technologies, e.g., AI compute, public foundation models, and even certain agentic applications. But the enterprise must own the applications it identifies as strategic that incorporate its proprietary knowledge.
Conclusion
The “SaaS Playbook” enabled the enterprise to quickly adopt best‑of‑breed cloud applications, standardize on vendors’ “good enough” business processes, and use lightweight integrations and governance to gain speed, lower upfront costs, and reduce IT friction. It fostered faster experimentation, easier scaling, and access to continuously improved capabilities. However, it produced fragmented data silos, weakened the idea of a single customer system of record, and pushed enterprises to conform to vendor workflows rather than preserving distinctive process advantages.
The AI Factory is about owning agency to retain and enhance value.
If the enterprise treats AI as a utility, it becomes a tenant. In a similar way to what happened with SaaS applications, it pays rent to hyperscalers and over time loses its memory and nervous system. The AI Factory enables the enterprise to become a landlord. It capitalizes on its proprietary knowledge and builds its nervous system, both of which define its competitive advantage.
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