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The Biggest Threat to Your Ability to Use AI? Your Broken Data Foundation
The Biggest Threat to Your Ability to Use AI? Your Broken Data Foundation
Recent SOCi data found that 82% of marketers are familiar with generative AI tools, and 65% have incorporated them into their marketing technology (MarTech) stack. Marketers have seen the potential of AI and are incorporating it into marketing strategies. The question is, are they seeing results? As a marketer yourself, you may be using AI. If you’re not seeing results, your broken data foundation may be holding you back. For those already seeing results, kudos! Assessing your data foundation may lead to further success. Within this blog, we’ll discuss how marketers leverage AI, why a fragmented tech stack can hurt your AI efforts, and what you should do about it.How Marketers Are Using AI Today
Before diving into the broken data foundation, let’s briefly revisit how marketers use AI today. As you can see, marketers use AI in various ways, emphasizing how easily a broken data foundation can occur. A broken data foundation means information fuelling your AI is compromised. It often occurs when the underlying data supporting your strategies and decisions is flawed, incomplete, or unreliable. Imagine constructing a skyscraper on a weak foundation — it's a recipe for disaster. Let's take a look at how it can hurt your efforts down the line.How Fragmented Tech Stacks Can Hurt Your Efforts
Fragmented technology stacks can hurt AI usage through operational inefficiencies and increased costs. Data integration becomes daunting when diverse systems are developed independently to address specific challenges. The lack of communication between solutions, such as a social platform, a listings solution, and a CRM tool, results in siloed data and processes. The solutions your brand uses can solve specific problems independently but often cannot integrate with other platforms, which can be competitors. This fragmentation hinders operational agility, such as:- Lack of fluid communication because of poor integration
- Disjointed workflows due to toggling between various applications
- Inconsistent data resulting in data inaccuracies and discrepancies