Oakland Group

Harnessing Generative AI for Data-Driven Decision-Making

“In the boardroom, one fact remains constant: data is the lynchpin of contemporary business. How many organisations can genuinely claim to have fully operationalised their data assets, though? If your data strategy still resides on the periphery of business operations, rather than being its driving force, you’re in good company.” writes ChatGPT – How wonderful that even AI calls for cleaner data! 

The New Horizon: Generative AI as a Strategic Asset 

Generative AI isn’t just another buzzword to add to your corporate vocabulary—it’s at the forefront of intelligent decision-making. You may already be familiar with analytical AI technologies like Machine Learning, but Generative AI offers something more: the ability to create new, actionable insights by synthesising vast realms of data. 

Generative AI in Action: Use-Cases 

But what are the challenges of Generative AI? 

GenAI brings with it several distinct challenges that businesses must be vigilant about: 

Although there is currently no explicit law or legal framework to regulate AI use, the EU and UK are intent on releasing these soon. There is no getting around the fact that underlying data assets need to be fit for purpose to set foundations for compliant tools.  

AI: A Data Governance Perspective 

Gartner recently did a poll showing that the primary focus for GenAI initiatives in businesses has been Customer Experience/ Retention and Revenue Growth.   

The follow-up question: How well did these initiatives do? 

A decade ago, dashboards were considered the pinnacle of data-driven decision-making. The limitations, however, became apparent when executives realised that the quality of underlying data was often inadequate. For AI implementations, the governance requirements are similar but exponentially more complex. Businesses must ensure that data governance policies are robust enough to manage the capabilities and risks of AI-driven decision-making processes.  

“Garbage in – Garbage out” bluntly put. 

AI tools need to be managed as data tools. To make the tool fit for purpose, companies must have rigid oversight on the processes, policies and governance of the data involved (both at ingress and egress) and the tool performance itself.  

An AI Governance framework should include: 

Starter for ten: Is your organisation ready? 

If you can answer yes to these questions, piloting Generative AI initiatives could be the next logical step. In today’s data-driven world, harnessing the power of AI is no longer an option; it’s a necessity. The question is: Where does Generative AI fit into your unique data landscape?

Generative AI is not just a buzzword; it’s a game-changer. It can transform how you handle data, making your operations smarter, faster, and more efficient. Whether you’re looking to automate tasks, enhance decision-making, or innovate your products and services, Generative AI is the key to unlocking these opportunities.

Oakland’s experts understand how Generative AI can seamlessly integrate into your organisation’s data strategy and governance framework.

We understand that every organisation is unique. That’s why we don’t offer one-size-fits-all solutions. Instead, we take the time to understand your specific goals, challenges, and data ecosystem. Then, we craft a customised strategy that aligns with your business objectives, ensuring you get maximum ROI.

Data governance is the cornerstone of effective AI implementation. Our team specialises in developing robust data governance programs that ensure your data is secure, compliant, and ready for AI-driven insights. With our guidance, you can confidently navigate the complex world of data regulations. If you’re struggling to envision where Generative AI could fit into your data strategy or are ready to implement a data governance program that sets you up for AI success, contact us at .

Zareene Choudhury and Lea Gorgulu Webb are senior consultants here at Oakland