AI Marketing & MarTech
Evaluating and Choosing AI Marketing Tools
The AI marketing tool market changes every few weeks, and I have learned to distrust demos. A tool that looks powerful in isolation often turns out to be a bottleneck once placed in a real process. To avoid being led by the hype I always use the same four criteria before introducing a tool to a client, the same ones I apply when bringing AI into enterprise marketing processes. In this article I explain them one by one.
First criterion: it integrates with the existing stack
The first question is not what the tool can do, but whether it talks to what the client already uses: CRM, CMS, analytics platform. A tool that requires a second parallel system, with data no one reconciles, adds work instead of removing it. This criterion comes from direct experience with the martech stack, from the Salesforce Marketing Cloud Administrator certification to the Optimizely Experimentation Certified Core Strategist role I use every day: a tool isolated from the rest of the system almost always turns out to be a bottleneck.
Second criterion: cost scales predictably
Many tools look cheap in the demo plan and become expensive at real volume. Before adopting a tool I check how the cost scales with actual use, not with the demo's: number of runs, users, integrations. A cost that explodes the moment the project works is a deferred problem, not an avoided one.
Third criterion: it allows a human step where needed
A useful AI marketing tool is not one that removes people, but one that puts them in a position to work better. In the projects I built, from the Enel dubbing to the podcast for Toyota and Intesa Sanpaolo, the value came from automating the repetitive part and leaving people the decision on what actually ships. A tool that does not allow you to insert that checkpoint is only suited to content where a mistake costs nothing, and in enterprise marketing that content is rare. I go deeper into this in the two enterprise cases of AI video and podcasts.
Fourth criterion: it lets you measure impact
The last criterion is perhaps the most overlooked: does the tool let you measure the effect of what it produces, or only produce it? A tool that generates content in bulk but does not let you connect that output to a business metric leaves unanswered the only question that matters, whether it is working. The limiting case is multilingual localisation, where the stakes on cost are high: how I evaluate a project like that is in the article on how to cut localisation costs with AI. How I hold tools, cases and workflows together is instead in the guide to AI for marketing.
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