What is The AI Data Layer?
The AI data layer is the connective infrastructure that determines what information an in‑house AI model can access, when, and in what form. Sources from which data is pulled, the pipelines used to move and process the data, and the retrieval mechanism that delivers it are all parts of the AI data layer.
Simply put, the AI data layer stands between raw organizational data and the AI systems that act on it. Each in‑house AI model or related tool, I’d argue, uses the structured information available (data context), which is then interpreted through business logic and intent (decision context) to perform tasks.
As such, the AI data layer isn’t just neutral infrastructure, like cloud storage or automation software. The specific setup of the AI data layer can determine what an AI system knows about your customers, market, organization, and other factors that influence decisions.
For marketing teams using AI‑assisted personalization, segmentation, or intelligence tools, operational control over the data layer becomes important. Biased data will yield biased results and can directly impact a company's profitability.
Why Are AI Tools Different?
The same argument can be made for almost any department. For example, human resources must have some control over their data within AI‑driven systems to avoid hiring bias; the finance team needs it to avoid fraud and comply with regulations; and, without data team access, most AI tools won't work at all.
Biased, incomplete, or poorly structured data lead to suboptimal outcomes at best, and, at worst, to financial losses or legal consequences. Tools like cloud storage or a CRM can be vastly improved with knowledge of their operating context. AI tools cannot function properly without managing the environment in which they work, unlike software such as cloud storage. An AI system’s outputs are determined by its context, which sets it apart from most other SaaS tools that have come before.
Since data and decision contexts are dependent on data layer infrastructure, we cannot fully delegate it to a team that doesn't own the outcome. How data is sourced, cleaned, weighted, and retrieved shapes what the AI model concludes. While the AI data layer is important to all departments, I’ll argue that marketing heads should be the first to act.
Of course, there are technical aspects of maintaining the data pipeline that other departments, aside from IT, simply don't have the competence to handle. Some sort of balance between infrastructure ownership and data strategy ownership must be created.
Decision Rights
In organizational theory, the concept of decision rights defines who has the formal authority to make specific choices on specific issues within a company. The goal is to have a practical framework, and in the case of AI implementation, it requires separating at least two domains.
- Infrastructure ownership: security standards, integration architecture, compliance requirements, vendor evaluation, and data pipeline maintenance.
- Data strategy ownership: what to collect, from which sources, how often, how to ensure quality, and for what purpose.
The distinction is important since it refers to different competencies. While IT should have the infrastructure control within its domain, it shouldn’t dictate strategy because of it. These decisions require context that only dedicated business units can own.
Data ownership questions are central to many ongoing debates around AI‑driven personalization, customer data platforms, and automated ad campaigns. While the decision rights framework isn’t yet fully fleshed out, marketing teams are already taking on greater responsibility for data quality standards.
It’s quite clear when we look into what motivates marketers to be proactive. Marketing sits close to the actual business performance metrics that help to evaluate whether AI’s output is correct. Metrics such as campaign profitability, as well as constantly shifting customer intent and market positioning, are within the domain of marketing.
As a result, the financial consequences of data layer failures will be reflected quickly in marketing KPIs. Conversion, retention, campaign ROI, and other performance metrics will reveal failures in the AI data layer. The risk already resides there, so the decision rights should follow.
The Chief AI Officer (CAIO)
One structural response to the decision rights problem is a dedicated executive role of Chief AI Officer (CAIO). According to a recent IBM survey, it’s among the fastest‑growing executive titles. Although the reasons for establishing it are often just FOMO, a PR move, or signaling to investors, there is a genuine strategic need for the executive branch to expand into new areas.
Even if we separate infrastructure ownership and give it to the Chief Technical Officer (CTO), the strategy might not have a clear owner. Product‑facing AI features are often owned by the Chief Product Officer (CPO), while marketing, legal, and other executives will also want a say.
The CAIO exists to close the gap and balance business judgment with technical credibility by acting as a coordinator between different departments. Some Fortune 500 companies have already established this role to drive AI investments, implementations, and technical details.
Before rushing after the trend, companies must decide whether AI is central to their competitive strategy. If it isn’t, or if the company is small, a better option might be to implement a shared accountability model, which places departments like marketing on an equal footing when deciding on AI implementation.
Shared Accountability Model
The shared accountability model might offer a more immediate solution for managing AI implementation in the company. An often‑discussed structure in enterprise contexts involves three layers of data, IT, and AI infrastructure teams. In practice, it often takes the form of an AI Governance Council.
Something we’ve been working on at IPRoyal is developing a long‑term AI committee composed of active AI users from many departments, rather than leaving it entirely to developers. Not only has it helped promote an AI‑driven work culture, but it has also enabled much more frictionless adoption, even among skeptics, than we’ve seen in the SaaS industry.
Decision rights are defined across each layer to create a cross‑functional team that formalizes the separation of infrastructure and data strategy decisions. The council creates a forum where both sets of decisions get coordinated without either party overriding the other.
A marketing representative in such a council then holds formal rights over how customer data is structured and sourced for relevant AI tools. As such, marketing teams can avoid being reactive and take ownership of the AI data context themselves.
Every other department also needs decision rights over its business domains. HR sets quality standards for data feeding recruitment AI, finance defines compliance requirements for fraud detection models, and so on.
Within the IT guardrails, each business unit owns the data strategy layer for the tools it uses. Shared accountability won’t resolve every tension, as they exist everywhere where business domains overlap. Yet, it’s often a simpler solution than restructuring the company for a CAIO role.
Conclusion
The AI data layer is context‑sensitive, so its configuration changes based on who is using it and for what purpose. The ownership questions shouldn't be left to a technological default, because its operation affects every team that uses it.
It’s an especially important problem for the marketing teams, since their performance is directly tied to the company's profitability. But they are often left by the wayside, only as users, not as owners – something we’re working on innovating on at IPRoyal
About Author
Julius Narkus is the Chief Marketing Officer at IPRoyal, a leading residential proxy provider. He is a B2B growth leader who moved from studying law to helping startups scale through marketing. As Head of Demand Generation at US health‑tech company Modern Health, he built and scaled the demand engine from the ground up, driving roughly a third of the company's $100M+ annual pipeline. He's known for creating sustainable, efficient marketing operations and spearheading company‑wide AI enablement, treating internal AI adoption as a product launch.