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Who Owns Your Data?

AI adoption is moving quickly, but many organisations are still working out who is responsible for the data that makes it possible.

Businesses are investing in artificial intelligence to improve efficiency, make better decisions and create new opportunities. As AI becomes more embedded in business operations, the conversation is increasingly focused on the people needed to build, implement and manage these technologies.

But before organisations start hiring AI engineers, data scientists and machine learning specialists, there is a more fundamental question to answer: who owns the data these teams will be working with?

AI capability depends on more than the technology itself, it relies on the quality, accessibility, structure and governance of the data behind it. Without clear ownership and accountability, even well-resourced AI teams can face challenges before their projects get off the ground.

1. AI Readiness Starts With Data

The growth in AI adoption has made data readiness an increasingly important consideration for businesses.

Research from Gartner highlights the scale of the challenge. Its survey of data management leaders found that 63% of organisations either lacked or were unsure whether they had the right data management practices in place to support AI, which raises an important distinction between adopting AI and being ready to use it effectively.

An organisation may have the technology, budget and technical expertise to introduce AI tools, but that does not necessarily mean its underlying data is ready. Information may sit across disconnected systems, be inconsistently maintained or lack clear ownership.

These issues can affect the reliability of AI outputs and make it harder for businesses to scale their use of the technology.

2. The Confidence Gap

Businesses are becoming increasingly confident in their ability to adopt AI, but confidence in the technology does not always translate into confidence in the foundations supporting it.

Research from Precisely and Drexel University's LeBow College of Business has highlighted the importance of data integrity in business decision-making. Data quality, governance and accessibility remain important challenges for organisations looking to make better use of their information.

Similarly, Grant Thornton's research into AI adoption and governance points to a growing need for businesses to understand the risks associated with implementation, rather than focusing solely on the potential benefits.

The implication is that AI investment needs to be considered alongside the maturity of an organisation's data practices.

3. Who Is Responsible for Data Ownership?

Data ownership can become complicated as organisations grow and their technology environments become more complex.

Different teams may be responsible for collecting, managing, storing and using information. IT teams may oversee infrastructure, data teams may manage pipelines and analytics, while legal, compliance and risk functions focus on how information is handled.

However, responsibility can become less clear when data is shared across departments or used to train and operate AI systems.

A data owner should provide clear accountability for how information is managed, maintained and made available. This is different from simply having a team responsible for the technical management of data.

Without that distinction, important decisions can fall between departments, leaving organisations without a clear point of accountability.

4. The Risks of Getting It Wrong

When data ownership and governance are unclear, the consequences can extend beyond technical performance.

Poor-quality or inconsistent data can affect the reliability of AI-generated outputs. Incomplete information can introduce gaps or bias, while inadequate controls can create challenges around privacy, security and regulatory compliance.

These risks become more significant as AI systems are used to support business-critical decisions.

Organisations therefore need to consider not only whether their data can be used by AI systems, but whether it is being managed responsibly and whether the appropriate people are accountable for it.

5. Building the Right Operating Model

Establishing clear data ownership is not simply a technology decision. It requires collaboration between technical, operational and governance functions.

Data teams play an important role in maintaining data quality, architecture and accessibility. Privacy and compliance specialists help ensure that information is handled appropriately, while risk professionals provide oversight of potential exposure and control requirements.

The right structure will depend on the size, sector and complexity of the organisation. However, the responsibilities need to be understood before AI initiatives become embedded in business operations.

This also means considering how data governance fits into the wider AI operating model, rather than treating it as a separate activity.

6. The People Behind AI Readiness

As organisations invest in AI, demand for specialist technical talent will continue to be an important part of the conversation. However, the people needed to support successful AI adoption extend beyond engineering and machine learning.

Businesses may need to consider expertise across:

These responsibilities may sit within existing teams or require additional specialist hires. The important point is that organisations understand the capabilities they need before expanding their AI functions.

7. Before You Start Hiring, Get the Foundations Clear

Hiring an AI team can feel like the most visible step towards becoming an AI-enabled organisation. But recruitment should follow a clear understanding of the business's existing data capabilities and where the gaps lie.

Before beginning the hiring process, businesses should consider how their data is currently managed, whether ownership is clearly assigned and whether the right governance structures are in place.

They should also understand which responsibilities can be managed by existing teams and where additional expertise is needed.

This helps ensure that new hires are joining an environment where they can make an impact, rather than being expected to resolve fundamental ownership and governance issues without the necessary support.

8. Bringing Data and AI Expertise Together

AI adoption is creating demand for a broader range of specialist skills. Technical expertise remains essential, but organisations also need the data, governance and risk capabilities that allow AI to be introduced and managed responsibly.

At Orbis, this sits across our Data and Risk, Operations & Compliance communities. We work with businesses to understand their specialist hiring requirements and connect them with the expertise needed to support their technology and transformation objectives.

As organisations continue to invest in AI, getting the right people in place means looking beyond the technology itself.

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