AI & Marketing

AI Applied to Marketing: Where It Delivers Real Value and Where It Is Still Hype

Artificial intelligence in marketing is both one of the most hyped topics of recent years and one of the most useful tools — if you know where to apply it. This article separates the signal from the noise.

It is hard to open LinkedIn or attend a webinar without hearing that 'AI is going to change everything'. Part of that is true — part is hype from tool vendors and trainers. The problem is that many companies are implementing AI in marketing because of trend pressure, without a real evaluation of where it adds value and where it merely adds complexity.

AI is not a strategy — it is a tool. And like any tool, its value depends on whether you use it for the right job. Used well, it can multiply team productivity and improve personalisation at scale. Used poorly, it generates mediocre content in bulk and adds layers of automation that nobody understands.

Where it genuinely adds value today

Content draft generation, bid optimisation on advertising platforms, email marketing personalisation, automated segmentation with sufficient data and large-scale text analysis are areas where AI has a real and measurable impact. In these tasks it frees up time, improves consistency and allows work at a scale that was not previously possible.

• ✓Content drafts and variants with human review
• ✓Automatic bid optimisation in Ads
• ✓Segmentation and personalisation with historical data
• ✓Analysis of reviews, surveys and mentions at scale

Where caution is advised

Brand strategy, differentiating creativity, decisions with a lot of qualitative context and high-ticket relational sales are areas where AI produces mediocre results or actively causes harm. Caution is also warranted with chatbots that handle complaints or first interactions in sectors where trust is paramount. The practical rule: the more judgement and context matter, the less you should delegate to AI without oversight.

What a company needs before applying AI with judgement

AI works well when the problem is well defined, the data is reliable and oversight exists. If there is no clarity about what you want to solve, if data is scarce or poor quality, or if nobody in the company is going to review what the tool produces, the result will be mediocre or counterproductive. Before implementing AI, what is worth reviewing is whether the marketing foundations are solid.

AI and agents: the right approach

AI agents allow more complex flows to be automated with greater autonomy, but they also have more failure points and require more careful supervision. For an SME, the most useful approach is usually to start with specific, well-defined uses rather than implementing complex agent systems. Complexity without clear purpose is a cost without return.

Frequently asked questions about AI in marketing