The Surge of Generative AI
CDOs agree that generative AI, most recognisably in the form of tools like ChatGPT and MidJourney, marks a pivotal moment in the history of data-driven decision making. The deployment of these technologies has garnered immense interest and is, without a doubt, leading to a transformation in how businesses perceive AI and utilise data. For the CDO, opportunities to capitalise on this interest are in plain sight:
1. Closing the Data Literacy Gap
One of the most significant advantages of generative AI is its role in demystifying AI and enhancing data literacy across various organisational levels. The tangibility of the tools has meant that data has again caught the attention of senior executives and given CDOs a new opportunity to secure sponsorship to advance the company data strategy.
“Thanks to ChatGPT, our executive team has a renewed interest in our AI strategy and has requested us to present it to them once again.” Laia Collazos, Chief Data & Analytics Officer, Coca-Cola EuroPacific Partners.
2. Catalyst for Enhanced Productivity
Of course, generative AI tools, alongside proprietary AI solutions, have the potential to revolutionise productivity, particularly in functions like customer service, finance, and HR. By automating routine tasks and providing insightful analyses, employees are both empowered with more information and freed up to focus on more strategic activities.
“We’re taking an offensive approach and looking at use cases, especially for manually intensive jobs. We want to enjoy a first mover advantage and are working to identify the big wins we can realise by deploying enterprise versions of ChatGPT.” Geraldine Wong, Chief Data Officer, GXS Bank.
3. Educational and Training Advancements
Generative AI is paving the way for re-educating business stakeholders about essential data management practices. This is crucial for the successful implementation and optimisation of AI models in business processes. CDOs are aware that they have a duty to communicate responsibly on the technology’s potential impact and carefully balance harnessing enthusiasm while imposing guardrails.
“We’ve used the buzz to create a video series on generative AI, underlining the message that if your data is bad, the outcome will be bad. It has highlighted the need to create a mandatory training on how to use AI safely.” Nirali Patel, Group VP, Data Strategy, Liberty Global.
4. Real-World Business Applications
The practical application of generative AI in everyday business operations gives users a new understanding of the potential of AI to turbocharge every day tasks.
“The hype around AI is actually liberating because it’s closing the data literacy gap. We’re now having conversations about how to leverage it in their own business and functions, as opposed to talking more generically about using data to improve the business.” Xavier Lagardère, Chief Data Officer & VP Innovation, Lufthansa Group.
The Flip Side: Threats and Challenges
While the sudden arrival of generative AI has caused tremendous excitement, it has also provoked a lot of trepidation. CDOs are right to anticipate the pitfalls this technology may place along their pathway:
1. Rapid Deployment Pressures
Along with the wave of enthusiasm, CDOs can expect a wave of demand from business users who will now have heightened expectations of AI capabilities. The pressure to meet these demands and to stay competitive in the AI race can lead to rushed and potentially flawed implementations of AI models and may challenge CDOs on the prioritisation of their current deployments.
“The challenge is how to be reactive to the business. It’s our responsibility to clearly understand the value they are intended to deliver and to truly master and implement these technologies in the best way possible.” Didier Mamma, VP Data Value Creation & Data Lab, Decathlon.
2. Ethical and Privacy Concerns
The adage “with great power comes great responsibility,” is particularly true for AI. The deployment of generative AI raises significant ethical concerns, including data privacy and potential misuse. These challenges demand that CDOs place a heightened focus on governance and ethical AI practices.
3. Quality and Governance Hurdles
Ensuring data quality and accuracy now becomes paramount to preventing flawed outcomes from AI decisions. While, at Data Leaders, we see that data governance is gaining greater traction, there is still a long road ahead for there to be full confidence in the quality of data leveraged for AI. This adds additional complexity with CDOs needing to balance the pace of AI adoption with the readiness of data.
“We have an educational responsibility, and we are going to face challenges. We need to be very pragmatic. We need to get people to feel comfortable and are ready to deploy.” Francisco Montalvo, Chief Data Officer, Telefónica.
4. Demonstrating Value
Another major challenge upon which CDOs agree is how to quantify and articulate the value AI brings to an organisation. This includes defining clear metrics for return on investment and aligning AI initiatives with business outcomes.
5. Responsibility for AI Failures
If the CDO is to lead on AI, they will be accountable for any failures with potentially significant repercussions. This highlights the need for cautious and considered implementation strategies while balancing the demands of the organisation.
Striking the Right Balance
As is clear from the above points, the deployment path for both generative and proprietary AI is not straightforward. During the panel, CDOs expressed what they believe are key factors in the successful deployment of AI.
1. Collaborative Leadership
CDOs see their role as pivotal in navigating the AI landscape however recognise that a collaborative leadership effort is essential to the success of organisational-wide adoption of AI. CDOs must work hand-in-hand with other C-suite executives especially CIO and Legal Counsel to ensure its alignment with business objectives and ethical standards.
2. Incremental Implementation
Rather than an abrupt integration, a phased approach to implementing AI allows for better control and understanding of its impacts. This rolling approach will require the CDO to carefully manage expectations while consistently communicating success along the way to sustain support.
3. Ethical AI Frameworks
Developing and adhering to ethical AI frameworks is crucial. This means defining and setting clear guidelines for data usage, privacy, and security to mitigate risks associated with AI deployment as well as the critical aspect of explainability in AI systems.
4. Continuous Learning and Adaptation
Just as the data landscape has continuously evolved over the last three decades, the AI landscape is changing at an unprecedented pace. CDOs will need to continuously stay up-to-date and adapt and refine their strategies to keep their organisations agile.
The deployment of generative AI presents a complex mix of opportunities and threats, both to the organisation and the CDO. While the potential gains are huge, failures will have significant repercussions making clear roles and responsibilities for stakeholders in the AI ecosystem absolutely critical. More than any other, the CDO role is the most pivotal with the potential to impact almost every aspect of the AI strategy.
While the dust is still settling from generative AI’s big bang impact, I believe that it’s essential that CDOs proactively step up to lead the AI strategy from the front; this way, they can maintain a line of sight to opportunities and pitfalls while navigating the evolving terrain and ultimately steer their organisation towards a future where AI is used ethically, and effectively for transformative success.
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