Chief data officers and data leaders face the challenge of data readiness, ethical AI deployment and influencing organisational transformation. Alex Hunt and Nikhil Asthana set out the challenges of AI adoption and some practical steps to take now. 

The growing reliance on data-driven insights, rapid advancement of artificial intelligence (AI) technologies, and rising emphasis on data ethics and governance has created a dynamic landscape for chief data officers (CDOs). There's heightened pressure to ensure data readiness for AI adoption, as highlighted by the need to maintain data quality, address privacy risks, and promote transparency in AI models. The deployment of generative AI tools has brought both opportunities and challenges, requiring data leaders to educate executives on essential data management practices while balancing the need for fast AI model delivery with ethical considerations.

A pivotal role to play in AI adoption

CDOs play a pivotal role in driving organisational transformation through data-driven decision-making, innovation and strategic alignment with business objectives, while also safeguarding ethics, security and privacy. The ability to overcome these core challenges, address data literacy gaps and foster a culture of data-driven innovation is crucial for driving sustainable growth and competitive advantage. The capacity to lead an organisation through these complexities and challenges is key.

By embracing the role as catalysts for change, guardians of data ethics and champions of data-driven decision-making, CDOs can position organisations for success in an increasingly data-centric and AI-driven business environment.


Getting the balance right on AI 

The need to deliver AI models quickly must be balanced with the imperative to uphold ethical standards and data privacy.

CDOs currently face a shifting status quo, driven by several key factors. The increasing prevalence of generative AI tools, rapid evolution of data technologies, and focus on data ethics and governance are all challenging traditional data management practices and reshaping the role of CDOs within organisations.

There's a heightened urgency to re-educate executives on the essential data management practices required for deploying AI models. This shift towards AI-driven decision-making and innovation presents both opportunities and challenges for CDOs.

One of the strategic priorities for CDOs and data leaders in adopting AI is to accelerate data readiness, ensuring that the data is accurate, complete, and formatted in a way that can be easily processed by AI systems. This can be done by implementing company-wide data quality programmes and governance frameworks.

Other strategic priorities include:

  • connecting AI initiatives to tangible business value – this requires aligning them with organisational goals and KPIs
  • safeguarding ethics, security and privacy – this involves transparent and responsible AI deployment practices
  • promoting data literacy across all organisational levels – to ensure safe and effective use of AI tools and models
  • driving innovation and competitive advantage through data-driven decision-making and transformative initiatives.


Nikhil discusses the four key CDO priorities in the industry, discussing where to allocate time and resources.

There is debate over ownership of AI within organisations. The common idea is that CDOs should own the guardrails and policies for AI deployment, ensuring ethical and responsible use, while collaborating with business units to align AI initiatives with strategic objectives.

However, a collaborative approach involving cross-functional teams, is essential to leverage AI for driving innovation and achieving business goals.


Alex Hunt gives guidance on who should be accountable, when and why.

Preparing a smooth transition for AI readiness

You can overcome the data challenges posed by AI in a number of ways, such as prioritising data quality and governance, implementing companywide data literacy programs to enhance understanding and utilisation of data and fostering a culture of data-driven decision-making across all organisational levels.

By addressing these challenges proactively, you can drive data readiness and enhance organisational data capabilities. You can also ensure that data is leveraged effectively to support strategic decision-making and drive sustainable growth.


Nikhil provides clear guidance around how to overcome those challenges, and how to implement and manage the culture change needed for success.

Ensuring that data initiatives align with business objectives and deliver tangible outcomes is essential for demonstrating the value of data investments and ROI of the data function.

Generative AI (GenAI) is a crucial tool to tackle new problems and foster innovation as industries transform in 2024. It's important to think about the different use cases for various industries, and especially the applications for risk and compliance professionals. Industry research has many documented examples, such as customised healthcare plans for patients, or crime scene investigation for police.

For risk management, knowing how to accurately use GenAI can be a huge asset, increasing accuracy and reliability in generating insights, streamlining processes to increase productivity, and enhancing quality.


Alex outlines the offensive and defensive strategies of GenAI.

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Practical steps: key AI governance elements to apply

Good AI governance is essential to ensure that AI is used effectively, ethically and responsibly. Organisations are setting up a variety of governance structures, from steering committees and dedicated functions with chief AI officers, through to combined custody between cyber and privacy teams; as well as this, organisations need to have the right skills, training and procedures in place to effectively support the requirements.


Alex guides on what good AI governance looks like, taking into consideration regulatory requirements and his experience of leading practices, and discusses pragmatic steps on how you should get your organisation ready for AI.

By integrating data ethics principles into AI models and practices, organisations can build ethical AI systems, foster trust with stakeholders and demonstrate a commitment to responsible AI deployment that benefits all stakeholders.

Learn more about how our Data assurance and analytics services can help you


Nikhil gives clear details of how data ethics should be integrated into AI models.

Driving data readiness and innovation

Accelerating data readiness, aligning AI initiatives with business value, and fostering a culture of data-driven decision-making and innovation are all key tasks for CDOs and data leaders.

While AI offers new possibilities for tackling problems and creating value, it also poses challenges around data quality, privacy, transparency and ethics. CDOs will need to take on these challenges while also embedding good AI governance, educating executives, balancing AI model delivery with ethical considerations, if they are to lead organisations through a shifting landscape of innovation and change.

For more insight and guidance, contact Alex Hunt or Nikhil Asthana.

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