AI becomes infrastructure when teams can reuse and improve it across product areas—not merely when one product adds an AI feature. In an interview with Tom Allen published by The AI Journal on 13 September 2026, Ankit Roy describes the organizational and technical choices behind that shift, and why he favors measured automation over automation for its own sake.
What distinguishes an AI feature from AI infrastructure?
Roy draws the distinction around scope and reuse. “A feature is built for a specific workflow, within the context of that one product. The model is trained and shaped around that use case, so the solution stays tied to it.” By contrast, he says, “Infrastructure is something different. It’s more like a framework or a library that can be applied to similar workflows across different product areas.”
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A feature might help a person complete one task inside one product. Infrastructure provides a shared foundation that other teams can adapt to related tasks. Reuse does not mean every team must use an identical model or interface; it means teams can build on common foundations instead of independently solving the same underlying problems.
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That difference matters because a locally useful feature can be quick to ship and easy to reward, while reusable infrastructure requires broader coordination and takes longer to establish. Roy says teams need to recognize common patterns across workflows before investing in a shared layer. The interview offers no benchmark proving that one approach always produces better competitive results; the case for infrastructure is its potential to support reuse and continual improvement across products.
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Which design choices help AI work become reusable?
Roy’s recommendations begin with the people and workflows the system is meant to serve, not with a platform built in isolation.
Validate that the need is genuinely shared
Talk with prospective users and teams before implementation. Establish whether workflows have enough in common to justify a shared foundation, and identify where teams will need room to customize it. Without that validation, a central platform risks encoding one product team’s assumptions rather than serving multiple users.
Make ownership explicit
Agree who is responsible for the platform and how consuming teams can shape it. Roy describes a contribution model in which teams that use the foundation can influence its development. This helps avoid treating infrastructure as a side project owned by one product group while expecting other teams to depend on it.
Align data and interfaces
Shared infrastructure needs consistent expectations for input data and output schemas. If each consuming team has to translate incompatible formats or guess what a component returns, reuse becomes harder and reliability more difficult to assess.
Build a foundation teams can extend
Roy favors a foundational layer that other teams can customize. The practical aim is neither a one-off implementation nor a rigid system that prevents adaptation: provide common capabilities while allowing product teams to fit them to their workflows.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should teams set boundaries for AI automation?
When a system moves from offering a recommendation to taking an action, teams need to understand the human task, the surrounding risks, and why people currently do the work. Roy argues that automation should target a sensible productivity improvement within risk limits. “I don’t think the goal should ever be full automation for its own sake.”
Start with recommendations and well-understood cases
Teams can begin by letting AI make recommendations rather than act independently. Route uncertain cases to people, then test a limited set of high-confidence actions. Expand the system’s authority only as evidence accumulates; confidence alone is not a substitute for understanding the impact of an error.
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Classify actions by how easily they can be undone. Roy says reversible actions are generally safer candidates for earlier automation, while irreversible actions need more careful routing and safeguards. The appropriate boundary also depends on what could happen if the action is wrong.
Keep people in the operating loop
Human escalation is part of the design, not a temporary inconvenience to remove. Define which cases need review, how they reach a person, and how feedback informs the system. Roy also calls for drift detection: behavior and signals can change, so a boundary that was appropriate at launch may need adjustment.
How should teams evaluate and maintain shared AI?
Roy recommends measuring whether a system achieves its intended outcomes, using context and ground truth to evaluate results. Where feasible, human evaluation should complement automated measures. A measure that worked for one scenario may no longer capture quality when the use case or user behavior changes, so teams should revisit evaluation criteria as conditions evolve.
Operational safeguards belong alongside evaluation. Teams should test for harmful outcomes, monitor changing behavior, and use human feedback to identify gaps that a static launch check might miss. For shared infrastructure, this work needs to continue across the teams and workflows that depend on the foundation—not stop when the first feature goes live.
What does Roy expect to matter as AI models converge?
Roy’s forecast is that organizations will distinguish themselves less by access to model capability alone and more by how well they integrate AI into context, make use of proprietary data, operate safely, earn user trust, measure outcomes, and keep improving. These are his views in the interview, not independently verified predictions or guarantees of competitive advantage.
His framing also keeps AI in its proper role: “The real value for a product was never just having AI, it’s still about solving the core user problem, with AI acting as a helper on top of that.” For teams weighing a platform investment, the relevant question is therefore whether a reusable layer helps solve real user problems across workflows—and whether the organization can own, evaluate, and operate it responsibly.
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