Unlocking Gen AI’s potential: Three essential strategies for personalization success 

Take a look at today’s social media feeds and news headlines, and you’ll begin to sense the market is feeling a bit overwhelmed by the noise around generative AI. Speak with personalization teams and business leaders, and you’ll discover that AI is being adapted faster in cultural conversation than it is being implemented in businesses and personal processes. Let’s cut through the noise: Not only can your teams integrate AI into their workflows today and optimize both their results and efficiency — for their individual careers and overall business impact — but they likely  should ASAP. 

Though AI has widened the possibilities of what a single person can do in terms of creation and analysis, it still relies on that one person’s expertise. For example, though a marketer can use GenAI to create hundreds of different copy options to A/B test for in a new campaign, that marketer still needs to write a prompt that communicates information regarding a brand and its audience and edit its outputs to ensure they feel natural and rooted in genuine human empathy. And though a marketer can use advanced machine learning tools to anticipate and fulfill customer needs, it can only do so with pinpoint accuracy if their data insights are error-free. 

In sum: Marketers need to implement generative AI into their workflows for any real gains to be made. While all this prep work and QA’ing can feel too expensive and difficult for personalization teams, it’s imperative to do the work now. But luckily, there’s much that can be done to make the journey feel less daunting. Here, three key strategies that I’ve seen work for personalization practitioners, teams and leaders. 

There are already many AI-powered recommendation tools baked into your operating system (we’ll get to them later), but to create maximum impact, you’ll need to collect and utilize your data insights effectively. AI capabilities hinge entirely on the quality and quantity of data insights it’s exposed to, so your team needs to make sure all data is relevant and consistent. 

It’s not an easy task but it’s worth it. As a company scales their personalization program, its product feed is often less-than-perfect, especially if the brand focused on time to market over integrity of data insights. To maintain a competitive advantage, some retailers may have to completely reorganize the structure of their product feed, purging any duplicated, irrelevant or inconsistent attributes and adding those that are timely. While this can be an incredibly technical, time-consuming and complicated project, it’s also the only way to improve AI-powered recommendation quality and ensure integrity as the program scales. 

While teams may want to delay this work, the time to tidy up the product feed is now as it will guarantee a competitive advantage. With the introduction of stricter data privacy regulations such as the EU AI Act and the U.K. AI Code of Practice, it’s now essential to ensure not only the quality of data insights but also compliance and transparency in how customer data insights are collected and used. Teams should regularly audit their pipeline of data insights for privacy, security and bias, and document their sources of data insights and processing steps to meet regulatory requirements.

Marketers can supplement their workflows with text-based AI tools  or image-based tools  to create different copy and visual variations for A/B testing.  The most common challenge teams face around AI adoption is knowing how to nail the perfect prompt — and this is easily solved with proper training. You need to be explicit to create any usable asset that aligns with your brand and goals. The context you provide for a text-based AI tool is just as important as the type of tool you’re using. As we know, AI is still a fallible technology but continuing to experiment with writing AI prompts will give you a better sense of the level of specificity the tool requires to suit your needs.

Marketers need practical tips for using AI, and academic guides  from top academic institutions are an excellent jumping-off point¹. You can provide any number of directions to the tool, like what you do or do not want to be included and how you want it to be presented. Feedback is essential, too. If you aren’t satisfied with an output, let the tool know so it can correct the mistake. And if you’re stuck on creating a prompt altogether, ask AI to help generate one for you. When given the appropriate context and direction, AI can yield incredible results.

Prompt engineering has rapidly evolved, with new tools like prompt libraries, prompt marketplaces and automated prompt optimization platforms (like PromptLayer or FlowGPT) now available. Marketers should explore “prompt chaining” (linking multiple prompts for complex tasks) and multi-modal prompts (combining text, image and code). Many leading AI platforms now offer built-in prompt templates and analytics to help teams iterate and improve faster. 

Beyond GenAI, there are advanced AI-powered tools that can plug directly into your personalization provider and improve the user experience and product recommendations. For example, in a world where customers seek highly personalized digital experiences, sophisticated generative AI-powered chatbots can create a conversational commerce experience that mimics the in-store consultative experience, using machine learning capabilities that identify and surface visually similar products. You can also improve recommendations with deep learning that processes data insights to understand consumer behavior trends and patterns across your customer base. 

The AI tool landscape is expanding rapidly. Industry-specific AI solutions are emerging for retail, finance, and travel. Multi-modal AI (combining text, image, and data insights) is now mainstream, enabling richer personalization experiences. 

When evaluating new tools, consider not only their capabilities but also their compliance with data privacy and ethical AI standards. 

As AI continues to revolutionize how marketers can interact with consumers, there’s pressure to leverage it as much as possible to stay efficient and ahead of the game. Experimenting with what’s available will take time and patience, but once you cut through the fluff, the benefits you will see will far outweigh any growing pains, plus help you discover more innovative and elevated personalization strategies. I hope these practical examples give you a place to start in your everyday role.