Now that the AI boom is in full swing, it’s a good time to start looking ahead at what this new technology will mean for us all. As Head of Data and AI at Triple, I’ve seen how eager companies are to leverage AI solutions. But then reality sets in, and so many companies struggle to get things off the ground.

Clearly, there’s a gap between AI ambitions and successful implementations. And I think that gap will continue to exist as long as companies look at AI as a magic solution. In reality, it starts with a firm foundation in high-quality data, which is a non-negotiable requirement for any successful AI implementation.

Don’t get me wrong: I am a big believer in the power and potential of AI. In many ways, I think it’s actually underrated, if you can believe that. We’re still in the early days, and as this technology develops, I’m sure we’re going to discover use cases for it that we can’t even imagine yet.

Still, the more I work with AI, the more I see that it’s not just about algorithms and automation. It’s about redefining the role of data in our strategies, our business models and our lives.

Data isn’t the new oil

For years, one of the common mantras we’ve been hearing in the tech industry is that “data is the new oil.” While this phrase captures the growing value of data in the digital age, it oversimplifies the reality. Unlike oil, which is consistently valuable and versatile, data can be a double-edged sword. Good data can drive innovation, but bad data can lead to poor-quality outcomes. The analogy falls short because it misses a critical point: data’s value depends almost entirely on its quality.

You can’t make something valuable out of low-quality data, just as you can’t produce a high-quality product from inferior materials. For instance, data on daily commute patterns offers far more insight than simply knowing when a traffic light turns on or off. Similarly, understanding someone’s annual income provides deeper context than just knowing their age. In each case, the value of data hinges on its quality and relevance.

AI’s reliance on data is profound. Think of AI as a high-performance engine, and data as its fuel. But unlike oil, which always powers an engine effectively, the wrong kind of data can cause an AI system to stall or even backfire. This reality underscores why businesses are now in a race to secure the highest quality data available, not just the largest quantity.

The data race is on: Legal battles and ethical concerns

The internet has always been a vast repository of information, a digital library filled with everything from cat content to scientific articles. But as AI becomes more advanced, the value of this data is being scrutinized like never before. Companies are now in a fierce race to collect the best data, and this competition isn’t just technical — it’s legal and ethical as well.

As AI continues to expand, our next big challenge is to clearly define data ownership: Who owns the data that powers AI? The companies that collect it? The individuals who generate it? Or the AI companies that use it.

Take the rising tide of lawsuits related to illegal data scraping. Companies like OpenAI have found themselves in legal hot water, accused of using data without proper authorization. In 2023, Getty Images accused them of unlawfully scraping millions of copyrighted images to train a generative AI model without permission or compensation. Getty argued that this unauthorized use not only violated copyright laws but also undermined the value of its business by potentially flooding the market with AI-generated images that could compete with its licensed content.

OpenAI isn’t the only potential offender here. Many AI models rely on publicly available data scraped from the web. While current laws in the US, UK and Europe allow this kind of data harvesting, the ethical implications are murky. For instance, companies like Reddit and Stack Overflow have struck deals with OpenAI, allowing their data to be used for AI training in exchange for compensation. But this raises even more questions: What is the true value of this data? And how do we ensure that those who contribute to this data are fairly compensated?

Monetizing data in the AI era

Because it causes us to rethink the value and role of data, AI also opens new approaches towards data monetization. Traditionally, websites and platforms have generated revenue through advertising, relying on a steady stream of visitors to their sites. But as AI becomes more capable of delivering information directly to users, bypassing traditional websites, these revenue streams are under threat.

Think about the deals between OpenAI and platforms like Reddit and Stack Overflow, which I mentioned above: These platforms have become crucial data sources for training AI models. In return, they’re being compensated for their data. But this shift is leading to a decrease in organic traffic for many websites. Experts are already predicting that AI-driven search tools like Google's AI Overview could decrease organic traffic by up to 64% for some sites. This new reality is forcing businesses to rethink their monetization strategies, moving from a model based on attracting visitors to one focused on selling access to high-quality data.

Suppose you’re in charge of data at a company that supplies industrial equipment and publishes detailed maintenance guides. Traditionally, these guides might have been a value-added service. However, in an AI-driven world, your company might license its maintenance data to AI firms, which develop predictive tools for the industry. While this might reduce direct demand for your company’s guides as customers turn to AI-driven solutions, it could also open new possibilities for delivering and monetizing your company’s expertise.

The new internet: From websites to AI experiences

As AI continues to advance, the internet is on the verge of the biggest change since its inception. Websites, once the primary sources of information, will increasingly be bypassed by AI tools that deliver answers directly to users. This shift raises fundamental questions about the future of the internet and the role of websites.

Think, for example, of a website like the celebrity chef Jamie Oliver’s, which offers not just recipes, but a rich, personalized experience through high-quality videos and content, attracting over 3 million visitors each month.

Now that AI can deliver the exact same recipes without the need for users to visit the site, what will happen to those personalized experiences? Will AI eventually also start showing Jamie Oliver’s videos? Or AI-generated versions of them? And if so, who will own the rights to the content? By providing instant, personalized answers to user prompts, genAI tools like ChatGPT are ironically undermining the personalized, branded experience of visiting a website.

This transformation isn’t just about content — it’s also about transactions. Take Google Shopping, for example. This platform has helped modernize e-commerce by allowing users to compare prices, shipping costs and discounts across multiple retailers. As AI becomes more integrated into platforms like Google Shopping, the way consumers shop online will continue to evolve, with AI providing increasingly personalized recommendations and insights.

So, once again, the question arises: what purpose will commercial websites serve in an AI-driven world? If AI can provide users with everything they need, from information to shopping recommendations, why would the user still need to visit a website? One possibility is that websites will soon evolve into more hyper-personalized platforms, offering richer experiences that AI alone can’t replicate. For instance, while AI might be able to provide recipes, even very creative ones, it might not be able to top the unique experience of watching Jamie Oliver cook, tell stories and make a mess in the kitchen.

AI in everyday life

As AI becomes more embedded in the technologies we use every day, its influence on business models and strategies will only grow. Apple’s upcoming iOS 18, for instance, is set to include AI as a core feature, allowing users to access information directly through AI-driven tools. This integration is a clear sign that AI is moving from being a standalone product to becoming an integral feature of the digital experience.

We can see a similar shift in how AI is being used for everyday tasks. For example, Ikea uses AI to provide product visualizations, personalized recommendations and customer support on their website, creating new ways for consumers to interact with the brand. This is just the beginning of how AI will reshape e-commerce and beyond, offering users increasingly personalized experiences based on their data and preferences.

Looking ahead: Dreams and reality

AI is already improving how many companies operate, and there are still so many new use cases just waiting to be discovered. But for every AI success story, there are still too many failures. Now’s the time to start tackling tough questions about data ownership, ethical use and the need for adaptable business models. Above all, every company that’s thinking about leveraging AI should focus first on improving the quality of their data. This is the key to ensuring that machines can learn in the most effective and accurate ways.

The future of AI and the future of data are fully intertwined. I’m sure we’ll see plenty of new challenges and questions as we move forward, but if we start concentrating on getting the basics right, our actions today will have a lasting impact.

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