“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
- Automated Customer Interactions: Generative AI can power customer service solutions that offer unprecedented personalisation while streamlining operations. Recent surveys indicate that customers in some sectors prefer AI’s efficient and targeted responses over human responses.
- Marketing Innovation: GenAI suddenly makes life in the Marketing world much less resource and time-intensive. If you can think it, you can produce it! You want a celebrity in your marketing campaign, no problem. For Nike’s 50th anniversary, we could see Serena Williams and a younger version of herself battling it out on the tennis court. Avatars can now also provide personalised and interactive messages. Imagine running campaigns that are not only data-driven but also continuously optimise themselves. Generative AI can produce innovative, personalised marketing content at scale.
- Accelerated Healthcare Research: Generative AI has dramatically cut down drug development and medical research cycles in the pharmaceutical and healthcare sector. It’s not just about speed; it’s about enabling new avenues of research that were previously unthinkable.
- Data Governance and Quality: In a recent use case, a financial institution substantially leveraged Generative AI to improve its data quality. By creating simulated but realistic data sets, the institution could test the robustness of its fraud detection algorithms under various conditions before actual deployment. This enabled them to pinpoint weaknesses in their governance framework, tighten control mechanisms, and gain more reliable risk assessment insights.
But what are the challenges of Generative AI?
GenAI brings with it several distinct challenges that businesses must be vigilant about:
- Lack of data integrity that produces untrue or biased outcomes
- Unethical use that may lead to societal bias or human rights infringements
- Copyright and intellectual property infringements and opportunities
- Security and Privacy Rights
- ESG Impact
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:
- Data Quality and Integrity: Ensuring the data is unbiased and accurate
- Accountabilities and owners: For the data itself but also the development specifications and the outcomes
- Transparency: Where is the data being sourced from, how are the outcomes being used, and who can access these?
- Ethical use: Does it have a negative impact, or is it a threat to people’s security and rights?
- Human Oversight: AI doesn’t and shouldn’t fix its data problems to ringfence what the tool can and cannot produce, as hallucinations of the AI can become the norm.
Starter for ten: Is your organisation ready?
- Do you understand your business capability landscape adequately to scope where AI can improve your business performance?
- Do you have a clear map of your information flow and data dependencies?
- Does your business have a working Data Strategy or is it currently just a document sitting on the intranet?
- Are your data platforms up to the mark?
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 hello@theoaklandgroup.co.uk.
Zareene Choudhury and Lea Gorgulu Webb are senior consultants here at Oakland