We couldn’t agree more with your perspective. Common roadblocks we see are data quality or data accessibility. The right data, strong hygiene and governance practices, and a continuous feedback loop to refine the model are key to a project's success when working with our clients to implement AI solutions. It doesn’t matter if it’s leveraging in-platform AI capabilities like Salesforce or Marketo or a bespoke application, a solid data foundation is key to long term success and delivering truly personalized experiences.
This Marctech article really resonated with me. I hear clients tell me all the time that they want to use more AI, especially when it comes to personalizing their marketing experiences across email and web. I love hearing this because it has the potential to create a highly personalized experience that connects with customer needs and increases the potential to drive business results. The reality is that the data fundamentals are not always in place and efforts often fall short of their potential. AI isn’t a magic bullet. It needs the tried and true data practices to feed the engine. Capturing customer needs, creating content to address those needs and stitching it together so it’s available when customers want it is still the foundation a marketer needs. What has been your experience with this? Are you also running into data challenges when it comes to AI-driven marketing automation? https://lnkd.in/eSFWaxkF
Data readiness is the missing foundation of AI-powered marketing | MarTech
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I love the firehose analogy they use - we can't just throw everything into a pot and hope something smart comes out. We've got to create a strong foundation based on the outcomes that we want and THEN add the AI tools. AI can add value but it's only as smart as we make it.
This article resonates a critical truth: AI is only as strong as the data foundation it runs on. Having worked globally in tech, I’ve seen how the “firehose” approach overwhelms teams and undermines trust. The real differentiator is intentional governance, transparency, and human stewardship, building curated pipelines that turn raw signals into reliable intelligence. Data readiness isn’t just a technical exercise; it’s the cornerstone of scaling AI with confidence.