CDOs take centre stage
When it comes to organisational transformations through the implementation of new AI tools, there are few people better placed to take charge than CDOs. It is the role of the CDO to ensure a data-driven decision-making process that enables innovation and strategy alignment with business objectives, all while safeguarding ethics, security, and privacy. To achieve success, a CDO must address data literacy and culture in the organisation through intelligent and poised leadership while proving competitive advantages and sustainable growth.
“The growing reliance on data-driven insights, rapid advancement of AI technologies, and rising emphasis on data ethics and governance has created a dynamic landscape for CDOs,” said Hunt. “There is now 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. Furthermore, 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.”
CDOs need to look at their role as a catalyst for change in addition to being the ones that protect data ethics and champion data-driven change. For organisations that have the size and scope, delegation to deputies can be highly beneficial, allowing the CDO to focus their attention on maintaining buy in from decision makers and guiding the journey.
A delicate balance for AI adoption
There is always a race to prove ROI and POC when it comes to large investments in new technologies, but there must be a sincere focus on upholding ethical standards and data privacy – this cannot be compromised by speed.
“CDOs currently face a shifting status quo, driven by several key factors,” said Hunt. “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.”
When it comes to adopting AI, CDOs need to work with the business and their teams to accelerate data readiness. There must be a high level of accuracy and organisation with cataloguing and formatting to facilitate a smooth integration.
Strategic priorities for CDOs need to include:
- Connecting AI initiatives to tangible business value and alignment with organisational goals and KPIs.
- Safeguarding ethics, security and privacy through 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.
Once these priorities have been addressed, another issue of ownership then needs to be tackled. There is an ongoing debate regarding AI ownership in organisations, but the common consensus is that CDOs need to own the guardrails and policies for AI deployment. This means that CDOs should be responsible for the way ethics and use are implemented and managed while ensuring alignment with different departments. This is only achieved through collaboration and strong communication.
How to have a smooth transition
Data leaders need to prioritise data quality and governance as a first step, creating organisation-wide data literacy programmes to ensure all users understand how and why quality and governance are the bedrocks of success. This will also develop a strong data culture and a data-driven decision-making series of operations across all levels.
Elsewhere, CDOs need to make sure that data initiatives align with business objectives to deliver tangible outcomes. This is essential for demonstrating the value of data investments and ROI of the data function and is arguably most important when utilising new tools such as generative AI (genAI).
“GenAI is a crucial tool to tackle new problems and foster innovation as industries transform in 2024,” said Hunt. “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.”
Knowing how to accurately use genAI huge asset for risk management. These tools can increase accuracy and reliability in generating insights, streamline processes to increase productivity, and enhance quality.
Elements for AI governance
Ultimately, to ensure the effective, ethical, and responsible use of AI, strong AI governance is needed, and this can come in multiple forms. Some organisations use steering committees and dedicated functions such as chief AI officers, while others operate under a combined custody approach between cyber and privacy teams – and some have a blend of all the aforementioned. The main thing for success in this area is for an organisation to have the right skills, training, and procedures in place to effectively support its unique demands.
Data readiness and innovation
It should come as no surprise that CDOs and data leaders are tasked with accelerating data readiness, aligning AI initiatives with business value, and fostering a culture of data-driven decision-making. When combined, these successes lead to innovation and the development of new solutions and approaches.
AI is highly lauded for its ability to tackle problems and create value, but it comes with a unique set of challenges for quality, privacy, transparency, and ethics. These challenges need to be addressed before any integration and adoption or else the organisation will fail to extract the most from its investments. The landscape is rapidly changing, and CDOs are the ones that can guide the journey to achieve positive outcomes through complex and investment-heavy AI adoption.
To hear more about overcoming data challenges with AI adoption, contact Alex Hunt.
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