“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.”
That illuminating passage was written by ChatGPT (should we be surprised that even artificial intelligence (AI) calls for cleaner data?). Yet, while amusing, and with many of us having had a good play around with the technology, it really is time to consider generative AI as a key strategic asset for your business.
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 through custom AI systems.
What Are Some Key Gen AI Enterprise Use-Cases?
There are thousands of generative AI use cases out there – every organisation and its challenges are different after all. Yet, these are some of the most common that our AI consultants have worked with over the past few years:
- 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.
- Gen AI in marketing: GenAI suddenly makes life in the Marketing world much less resource and time-intensive. If you can think it, you can produce it! If 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.
- AI in healthcare: Generative AI has dramatically reduced drug development and medical research cycles in the pharmaceutical and healthcare sectors. Accelerated healthcare research isn’t just about speed; it’s about enabling new avenues of research that were previously unthinkable.
- AI for data governance: 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.
What are the Challenges of Generative AI?
In order for it to provide return on investment, generative AI 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. What’s more, there’s no getting around the fact that underlying data assets need to be fit for purpose to set foundations for compliant tools.
How can AI be used in Data Governance?
In 2023, Gartner polled 2,500 executives, asking what the primary focus for GenAI initiatives in their businesses had been. Customer experience and retention (38% of all initiatives), and revenue growth (26%) came out on top, followed by cost optimisation (17%) and business continuity (7%).
However, all this begs 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.
Bluntly put, “Garbage in – Garbage out”.
AI tools need to be managed as data tools. To make them fit for purpose, companies must have rigid oversight of the processes, policies, and governance of the data involved (both at ingress and egress) and the tool’s performance itself.
An AI data 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.
Is your organisation ready for generative AI?
There are four key questions every organisation should ask itself before embarking on a generative AI project:
- 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 task?
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.
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.
The next important question, though, is: where does Generative AI fit into your unique data landscape?
How Oakland can help you drive value from generative AI
Our 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 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, have a chat with one of our team.
Zareene Choudhury and Lea Gorgulu Webb are senior consultants here at Oakland
Frequently asked questions
Want to know more about generative AI? Here are answers to some common questions. Don’t see the answer you’re looking for? Get in touch with one of our team.
What is Generative AI?
Generative AI is a type of AI focused on content creation. Gen AI systems are trained on existing data and use it to create original content such as text, images, videos, code, or audio, based on the patterns inherent to the training data. This is different to traditional AI, which only uses the data to make predictions or decisions.
The technology has grown significantly in popularity since November 2022, when ChatGPT was released to the public. While the initial novelty and hype have somewhat subsided, the potential for the technology to drive productivity gains has meant many organisations are adopting generative AI in their operations. A global INSEAD survey of business alumni in 2024 found that just over half of respondents’ organisations were using generative AI and only 21% had no plans to use it in the future.
How Does Generative AI Work?
The workings of generative AI can differ significantly from one use case to another, but in general, they are created using a four-step process that utilises neural networks, and data architectures like transformers:
- Data Collection: Significant volumes of data relevant to the desired output are collected. For example, if the goal is to generate customer service responses, transcripts and training materials might be used in the dataset.
- Training: The AI model is trained on this dataset using machine learning algorithms. This allows it to notice and replicate patterns in the data.
- Generation: Once trained, the model can generate new data that draws on and mimics patterns in the training data.
- Fine-Tuning: The model can be fine-tuned for specific applications or industries, ensuring the generated content meets particular standards or requirements.
Is generative AI machine learning?
Generative AI is a system enabled by the broader concept of machine learning. Machine learning algorithms allow generative AI systems to learn from data and then apply these learnings to complete tasks. Generative AI specifically uses those learnings to create original content.
There are three main ways machine learning algorithms are trained:
- Reinforcement learning – Where the machine learning model is trained using rewards and penalties based on its actions. This reward system hones its understanding.
- Supervised learning – Where data sets are labelled, giving the algorithm insight into the meaning and relationships between different data.
- Unsupervised learning – Here, the algorithm is given unstructured data without any human input, and allowed to discover relationships and patterns on its own.
Generative AI systems are often developed with the final unsupervised method. This allows them to be more ‘creative’ with their outputs than other machine learning algorithms.
Is generative AI deep learning?
Generative AI is provided using deep learning techniques which involve layered architectures (so-called neural networks) to identify and remember complex patterns in data. Deep learning techniques include things like:
- Transformers: Transformers are a neural network architecture that transforms an input sequence (a prompt) into an output sequence (content) based on the learned relationships between the components in the two sequences. They are a key part of large language models like ChatGPT.
- Generative Adversarial Networks (GANs): This system is formed from a generator, which creates fake outputs that mirror real data, and a discriminator, which tries to guess which are fake or real. The discriminator receives rewards or penalties (via reinforcement learning) if it’s correct or incorrect. GANs can create very lifelike outputs but can be unstable.
- Variational Autoencoders (VAEs): These unsupervised models understand the structure of data using an encoder, decoder and loss function. The encoder compresses data and gives it a summary (of the image, text, audio, etc.). The system creates a bank of these summaries logged to each piece of compressed data, ready to draw on – red flowers, happy faces, etc.
The user then provides a prompt, which is given to the decoder. It then tries to reconstruct the data as accurately as possible, based on the summary characteristics. The loss function then measures how much data was lost during reconstruction, distributing data smoothly. In practice, this all means that VAEs can create highly imaginative content (happy red faces, surrounded by petals), but that might be blurry or garbled, due to the loss function.
