AI-Driven Personalisation: Precision Targeting in 2026

AI-driven personalisation is taking centre stage in 2026, and it’s not hard to see why. With the likes of Salesforce and Adobe leading the charge, marketers are now able to tap into the power of artificial intelligence to create highly targeted, personalised experiences for their customers. This trend is happening now because of the significant advancements in machine learning and natural language processing. These technologies have improved dramatically over the past few years, allowing for more accurate predictions and better decision-making.

So, how does this differ from past cycles of personalisation? For starters, the level of precision is much higher. With the help of AI, marketers can now analyse vast amounts of customer data, from browsing history to purchase behaviour, to create unique profiles and tailor their marketing efforts accordingly. This is a far cry from the basic segmentation and targeting of yesteryear. Early adopters like Netflix and Amazon are already seeing significant returns on their investment in AI-driven personalisation, with increased customer engagement and loyalty being just a couple of the benefits.

On the other hand, laggards are still relying on traditional methods of personalisation, such as manual data analysis and basic segmentation. These companies risk being left behind as their more forward-thinking competitors reap the rewards of AI-driven personalisation. To avoid this, marketers can follow a simple three-step adoption framework. First, they need to assess their current data capabilities and identify areas for improvement. This includes evaluating their data management systems and ensuring they have the necessary infrastructure in place to support AI-driven personalisation. Second, they should invest in AI-powered marketing tools, such as those offered by SAP and Oracle. These tools can help automate the process of data analysis and provide actionable insights that marketers can use to inform their personalisation efforts. Third, they need to develop a clear strategy for implementing AI-driven personalisation across their organisation. This includes setting goals and objectives, as well as establishing key performance indicators to measure success.

Now, there are certainly situations where AI-driven personalisation might not be the best approach. For example, if a company is just starting out and doesn’t have a large amount of customer data to draw upon, it might be more effective to focus on building a solid foundation of customer relationships before investing in AI-driven personalisation. Additionally, if a company’s products or services are relatively simple and don’t require a high level of personalisation, it might not be worth the investment. In these cases, it’s better to focus on other marketing strategies that are more suited to the company’s needs. For more martech analysis, tools coverage and strategy guides, visit MartechXpert — your independent source for marketing technology insight. By being honest about when to ignore AI-driven personalisation, marketers can avoid wasting resources on a strategy that might not be right for their business.

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