Large language models (LLMs) have reached new levels of capability and accessibility, leading to the perhaps biggest boom in AI chatter since voice assistants first came onto the scene. For marketers, there are a lot of exciting conversations about everything that generative AI will unlock: hyper-personalisation, AI-generated marketing personas guiding media strategy, GPT interfaces for data analytics and insights and other efficiency-driving advancements.

But to take advantage of all that generative AI has to offer, marketers need to first build a solid data foundation together with a person-based identity solution. Without quality data, they will not get the expected results.

What exactly are LLMs and how do they factor into AI?
LLMs are a type of machine learning-trained model for language understanding. The spotlight has been on generative AI: a special use case for LLMs that underpins recent AI advancements like the consumer-facing ChatGPT and other GPT models. These generative AI tools have recently become available to the average person to test and use due to a significant increase in parameters available to the GPT model: from 175 billion to 1 trillion and growing.

For marketers, there are a lot of buzzy conversations about everything that generative AI will unlock: hyper-personalisation, AI-generated marketing personas guiding media strategy, GPT interfaces for data analytics and insights and other efficiency-driving advancements.

But to take advantage of all that generative AI has to offer, marketers need to get their data ducks in a row. It is essential to have a solid data foundation together with a person-based identity solution before generative AI can play any kind of substantial role in bolstering campaign efficiency and effectiveness or driving deeper insights.

There is no replacement for quality data. You can apply the best, smartest, most sophisticated models to mediocre data — but without quality data, you will only ever get mediocre results. Capturing and cultivating a first-party data asset and then enriching it with high-quality third-party data lays the right foundation for AI to work its magic.

Predictive or Generative AI: What’s the difference?
Predictive AI helps marketers decide whom to reach, where to reach them when to reach them and what to say to predict the most successful outcome. Predictive AI uses statistical algorithms and historical data patterns to analyse data and forecast outcomes. It is sometimes referred to as “predictive analytics”; however, the difference between predictive AI and predictive analytics is that predictive AI is autonomous, whereas predictive analytics relies on human intervention.

In contrast, generative AI helps marketers create: It generates content. This includes written (like ChatGPT) or visual (like Dall-E) formats—across a wide array of needs, like writing an essay, generating potential headlines for an article or converting text copy to an image.

Generative AI is unlikely to replace creative work; rather, it will be a force multiplier that accelerates creative ideation and generation. It will always be important to keep a human in the loop of the creative cycle for myriad reasons, like brand safety and standards, copyrights, and privacy.

How do the two work together?
The consumer is the beneficiary of predictive AI decision-making, but they don’t necessarily know it because everything is happening behind the scenes. Predictive AI helps marketers connect with in-market consumers in a relevant, non-repetitive way that they appreciate. Predictive AI is used in marketing to define whom to message, where to reach them when to talk to them and what to say.

What generative AI can add to this process is the ability to create that message, ad or email dynamically and in response to the identified person’s preferences, wants and needs. It becomes the final step in the process, but you need the predictive foundation to provide the inputs for the generative AI side.

Ultimately, predictive and generative AI could work together in the same way that art and science work together. Think of predictive AI as the science — the methodology that enables deep insights and data-driven decisions using machine learning techniques to produce better business outcomes. It’s powerful but not terribly sexy. Generative AI is like the art — it can bring a more human understanding of data and insights through content generation that can evoke emotion. When put together as the art and science of AI, marketers can drive deeper insights and better decisions across platforms and channels.

How to build a strong predictive AI foundation
When it comes to using the power of predictive AI, time is of the essence. AI learns and improves over time. There’s no way to speed up this process. The best solutions should have a sizable amount of time training their predictive AI models already under their belt. The benefit of more training time means that those solutions are faster and notably more accurate than solutions with less experience.

Consider the example of a marketer locating an in-market customer for a product using predictive AI. Over time, as the model evolves and improves, it can inform technology, such as an ad server, to find in-market customers faster and more accurately. It’s the difference between messaging someone on the verge of a purchase versus someone who has already purchased.

Marketers should look for solutions with real-time model updates—not those that take weeks or months—because it allows for real-time customer connection at scale.

Optimising data for best AI functionality 
AI needs the right data to train it, but its outputs are only as good as the inputs. Here are three ways to get AI-ready:

• Data foundation: You need to enrich your first-party data to make it viable for AI applications in a privacy-safe way. Apply hygiene to it: cleanse, structure and reinforce. Think of your data as a stream, with AI requiring a continuous feedback loop to be effective.  
• Access to data at scale: Connect your data to a people-based identity. You need data about your customers everywhere they are, not just from your interactions. If you don’t have that robust identity resolution in place, connecting the dots just isn’t possible. 
• Readiness: Make your data asset accessible to AI methodologies. This part is greatly aided by partnering with a marketing solution provider that can effectively apply AI to your data.

Prioritising consumer privacy and data ethics
Amid all the generative AI hype, it’s crucial marketers don’t neglect the importance of centring consumer privacy and data ethics.

Generative AI makes it possible to create an entire ad, from copy to visuals to call-to-action, in real-time. Connecting predictive and generative AI could determine which creative will have the most emotional impact while also reducing waste and driving efficiency. This hyper-responsiveness must be balanced against brand safety, content appropriateness and legality, ideally with a human in the loop.

To ensure data privacy and ethics compliance, a team of real people should monitor any generative AI outputs. This team needs to ensure everything meets brand standards and does not infringe on copyright. Jodi Daniels, a data privacy consultant, said it best in a recent Forbes article: “A business could be put in a precarious position if it uses generative AI in a way that uses consumer data that runs counter to contractual obligations.” 

And, just like strong AI, it’s vital that those using it never stop learning and stay up to date on the latest information. Only then will they be able to truly harness the power of artificial intelligence.