What are the new priorities for CDOs in the era of Generative AI?

Generative AI (GenAI) burst into public consciousness with the launch of OpenAI’s ChatGPT chatbot in November 2022. Since then, it has been adopted rapidly by both businesses and consumers, dramatically impacting and automating a wide range of content-driven processes.

Like all types of artificial intelligence, Generative AI relies on access to high-quality, reliable training data, particularly when used within an organization. This creates new opportunities – and challenges – for Chief Data Officers (CDOs) and other data leaders. What are the new priorities for CDOs in the era of generative AI?

Generative AI creates new content from existing data and information. This could be text, images, video or code, delivered in response to a prompt from a user or AI agent. GenAI is trained on large language models (LLMs) and uses its learning to create relevant responses and content. Examples of content include original artwork, translations, sales, and marketing material or even computer code. It differs from traditional AI which focuses on analyzing and predicting future events based on predefined rules.

Generative AI can transform the world of business. It has the potential to increase efficiency, boost productivity and create new ways of operating. Statistics demonstrate the size of the opportunity, and the rapid and continuing growth of generative AI adoption:

• 95% of US businesses say they are using GenAI, up 12% from a year ago, according to Bain & Company.
• In September 2025, ChatGPT had 800 million weekly active users, doubling from 400 million in February 2025. It processes 2 billion queries every day.
• Investment continues to grow. Forrester research found that 67% of AI decision-makers plan to increase GenAI spending over the next year.
• Bain found that 80% of use cases met or exceeded business expectations, with 60% of satisfied companies seeing measurable benefits.

Businesses and CDOs are deploying GenAI across departments, with a particular focus on AI adoption in areas such as:

• Customer Service – delivering faster, personalized, and automated interactions to customers, adapting content and tone to match individual customer needs.
• Marketing – automating the creation of text content, videos and graphics based on brand voice.
• Human Resources – streamlining processes such as recruitment, onboarding and training while delivering personalized responses.
• Data analysis – GenAI’s ability to summarize long documents or datasets automates reporting and shortens time to insight.
• Software development – writing code based on prompts, debugging and creating accurate technical documentation.
• Knowledge Management – making knowledge available to the right people, in the right format, at the right time, based on their needs and skill levels.

To benefit from generative AI, organizations are increasingly redesigning their processes and workflows, transforming their structures, and automating previously manual activities. This boosts efficiency, enhances creativity, and increases personalization for customers and employees alike.

GenAI is extending the role and activities of CDOs. Traditionally they were responsible for managing and protecting data, ensuring security and compliance. Generative AI brings new priorities that add to their roles, bringing both opportunities and challenges.

Essentially, deploying generative AI is not the same as simply adopting a new technology or tool. To deliver its benefits requires a strategic shift based on thoughtful planning and alignment with an organization’s broader objectives, guardrails against risk, relevant training, and culture change, and above all the availability of reliable, user-friendly data to enable ethical AI. The table below shows the impact of GenAI on CDO priorities:

New CDO responsibilities and priorities

Looking into more detail, new Chief Data Officer priorities require a focus on:

• AI-driven data strategy – ensuring data underpins the optimal deployment of generative AI across the organization, in line with business objectives.
• Ethical AI and compliance – building trust inside and outside the organization that GenAI is acting ethically and in line with regulations and standards.
• Data security for AI models – protecting the security of data used within generative AI, particularly personally identifiable information, and company intellectual property (IP).
• AI-powered decision making – enabling the availability of trusted, reliable data to drive improved decision making by both AI agents and humans.
• ROI and demonstrating value – measuring the impact of GenAI projects on key business metrics and objectives.
• Upskilling and workforce enablement – hiring new skills in data teams and retraining all employees to embrace generative AI in their working lives.
• Managing risk and governance – working to understand, monitor and minimize GenAI risks, especially around inaccuracy, cybersecurity, bias, and IP infringement.

Essentially, CDOs need to ensure that everyone (whether machine or human), can easily discover, access, and trust the right data, within the right governance guardrails.

The roll-out of generative AI brings a range of new ethical and regulatory challenges. These go beyond enforcing traditional data governance, raising the stakes around protecting information and ensuring it is used ethically across the organization. CDOs need to successfully manage risks around:

• Data privacy: especially how customer data is used for training models and responding to prompts
• Intellectual property: ensuring that internal IP is protected, and that AI doesn’t infringe other people’s IP
• Bias and fairness: putting in place safeguards that AI outputs don’t show bias for or against particular groups, and that they treat everyone fairly
• Prompt governance: ensuring consistency, reproducibility, and security in prompt and output management.
• Explainability: CDOs need to be able to provide a full audit and lineage trail of what data is used to feed AI, where it is from, and how it has been deployed

Generative AI is being increasingly used by employees in every department, either officially or simply through accessing free tools such as ChatGPT. This dramatically increases the risks of governance or ethics breaches. The CDO must therefore lead in establishing AI governance frameworks, that include clear policies on how data is used to train models or respond to prompts, AI risk registers, and regulatory compliance, particularly with new legislation such as the EU AI Act.

CDOs have always been responsible for ensuring that organizational data is turned into business insights. This was often delivered through regular reporting or dashboards, created and maintained by the data team, covering areas as diverse as sales figures, customer retention, operational performance, or production figures. Along with reports, data teams had to provide training and support to ensure that managers without technical skills could access and understand this information.

Generative AI changes all of this. Employees can query data themselves using everyday language and get fast, accurate answers. For example, they could simply ask a GenAI chatbot to create a graph showing sales of a specific product over time, split by region. These AI-powered insights enable data democratization, providing self-service capabilities

Generative AI has the potential to create a complete step-change in how organizations operate. However, to realize this potential, businesses need to change their structures and skills.

At a senior level, CDOs have to collaborate more closely and effectively with CIOs, ensuring that the right infrastructure is in place to support GenAI workloads. They also must work with newly emerging Chief AI Officers, splitting responsibilities and providing them with the accurate, reliable data they need to innovate and deliver value.
To thrive in the GenAI era, CDOs must also ensure their teams and the wider business have the right skills in place, including:

• Developing cross-functional literacy in machine learning and AI skills across their teams
• Recruiting and retain talent across data science, prompt engineering, and AI ethics
• Understanding, selecting and fine-tuning AI models, and providing them with quality data pipelines
• Fostering a data + AI culture across the organization. They have to educate the business on GenAI’s capabilities and limitations, especially around good governance and prompt engineering

To be ready for successful GenAI adoption, CDOs must:

• Understand risks and changes to governance, especially around ethics
• Take a more strategic approach, focused on business value
• Ensure they have the right skills and technology in their teams
• Collaborate across the organization, building a culture of AI literacy
• Focus on delivering high quality, understandable data to drive GenAI projects

With GenAI, CDOs need to guard against:

• Bias in the outputs from prompts due to skewed data or algorithms
• Confidential/personal data being used without permission in GenAI
• Data security being breached
• Inaccuracies and hallucinations due to poor quality data
• Unethical uses of GenAI within the organization
• Legal challenges due to the infringement of other people’s IP
• Loss of own IP to external models
• Opaque processes that do not show transparency or lineage
• Not meeting regulatory or legal requirements

Countries and regions are increasingly looking to ensure generative AI does not have negative consequences for consumers, employees, or wider society. Regulations vary widely across the world, with this fragmentation adding to compliance burdens and costs. Legislation is impacting generative AI-drive strategies around:

• Compliance and audit, including proving that results are not discriminatory or biased
• Transparency, traceability and explainability of model training methods, data sources and outputs
• Ethical collection and usage of data. Responsible AI processes and results
• The ability for consumers to opt out or request that their data is not used for AI training
• Safeguards on the collection, storage, and usage of personal information for model training, often with explicit requirements around gaining consent
• Proof that companies respect copyright and Intellectual property usage
• Data sovereignty and model choice, including cross-border data transfer mechanisms

Generative AI transforms the CDO from a data custodian into an AI ecosystem orchestrator. In their new role they must blend data strategy, ethical AI governance, and cross-enterprise innovation, while still managing data reliability, lineage, and compliance. They are at the heart of AI-driven strategy, helping to assess and progress the organization’s AI maturity in order to deliver its benefits. CDO priorities in the generative AI era are changing and evolving, with their success measured not by how much data is governed, but by how much business value is created from it.