Why Businesses Need To Go Back To Basics
Kevan Savage is a partner and the Marketing practice leader at Alexander Group.
AI is the new competitive edge for marketing and communications leaders, and the race is on to implement the technology in the savviest, most effective way possible. Thanks to generative and agentic AI, consumers are discovering and interacting with brands in unprecedented ways, and traditional playbooks like SEO and counting clicks aren’t as effective as they once were. Much like fighting fire with fire, marketers are now feeling pressured to wield AI to succeed in the AI era.
But in trying to keep up with competitors, I’m seeing many companies put the cart before the horse. They’re eager to take advantage of AI’s ability to analyze data, predict trends and make informed recommendations—but behind the scenes, they’re still struggling to capture and operationalize data in the first place.
Without data integrity and maturity, AI won’t help anything. It will just deliver a bad result faster.
The Importance Of Data Integrity
As a go-to-market consultant, I’ve worked with countless marketing leaders. We partner together to identify organizational shortcomings and build road maps toward the right solutions. One of the most common challenges I see is in fundamental data strategy.
Many companies are still operating with persistent data silos. Teams can’t collaborate to the extent they’d like to. Data integrity is inconsistent or even questionable. As a result, businesses have trouble building strategies that align with reality and generate the desired outcome.
This isn’t a problem only marketers face; it’s one innumerable businesses across industries and geographies are challenged with. And based on my experience, it must be addressed before AI will actually be able to take root and start making a difference at scale. Otherwise, we’ll just be pointing a different tool at the same set of bad data.
How To Know When You’re Ready
So how can marketing leaders know when they’ve crossed a critical threshold of data maturity and are ready for AI? What’s the litmus test? Here are the minimum capabilities you should shoot for:
• Account Hierarchies And Relationships: You should be able to map out how different accounts are structured and related to each other. This can inform sales strategies, targeting and messaging.
• Contact To Account- Or Site-Level Data Management: Every contact should be mapped appropriately to avoid duplicative or inaccurate engagement.
• Mapping Behavioral Data To Buying Groups: Teams should be able to trace the throughline from individual customer preferences and actions to broader persona strategies and campaigns.
Data Integrity As A Growth Constraint
From my observations, staying competitive in the market now requires the successful integration of AI. But AI success is contingent on a strong data foundation. In other words, data integrity is an important linchpin for growth and lasting success.
With the right building blocks in place, marketers can achieve new levels of efficiency and intelligence with AI. Some of the capabilities I have seen include:
• Customer Segmentation And Targeting: AI can intelligently delineate persona groups and be used to design bespoke campaigns for each segment.
• Content Creation And Personalization: Based on segmentation, AI can give recommendations for the how, what and when of prospect engagement.
• Lead-Scoring And Predictive Engines: AI can rank leads, making recommendations for prioritization based on the estimated likelihood of a close.
There’s no shortage of AI tools on the market, and the technology certainly isn’t going anywhere. I believe marketing leaders should absolutely make it a priority to adopt AI, but not at the expense of business continuity or success. Take the time to build your data foundation first, then start scaling AI the right way.
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