Four steps to take before trying to apply A.I. to your data:

Build your data and analytics skills. Data is the DNA of Marketing. Regardless of role, every marketer needs a solid base in data. Whether you hire it or outsource it, you need strong data and analytics skills to address the increasing need to generate insights from the constantly increasing volume of data and the expanding number of measures and metrics being used to assess and drive Marketing performance. The CMO Council laments

AI-enhanced, ML, adaptive metrics, decision-making, dashboards

Marketing analytics — measuring and analyzing marketplace activity and marketing performance to improve decision-making — are employed for 37.5 % of business decisions, but less than 2% firms say they have the right people in place to leverage the information.

Create a Data Management strategy and a consistent set of standards. The purpose of data is to facilitate decisions. Therefore, you need clarity around which decisions are applicable for A.I.. Then you can build your data management strategy to address how data will be acquired, prepared, and normalized. You will want to define how models will be developed, tested, and deployed.

Fix your data silos. For any tool to be properly deployed to help you transform data into insights, the systems that house your data need to be connected. It’s difficult to create a seamless customer experience when data is siloed.

Establish an analytics center of excellence (CoE). Your analytics center of excellence is comprised of a team of business and technical professionals who enable the ability to drive best practices around methodologies, tools, models, and techniques. The objective of any CoE is to improve effectiveness and gain efficiency across to the different business units.

Remember, the end goal is to be able to extract patterns from data so you can make intelligent and actionable decisions and improve operational excellence.

Is A.I. the next norm for strategic Best-In-Class Marketing Organizations? Only time will tell. In the meantime, stay tuned for continued insights and tips from our experts. Subscribe to our blog for instant updates and check out our How Data Moves You from One-Size Fits All to Segmentation-Based Marketing recording.

FAQ:

(written by Penn of Sintra.ai)

Q1: What is the central message of Part 2 in this two-part series on A.I. in Marketing?
A: The central message is that A.I. is most valuable when it improves decision-making through intelligent use of data—but A.I. only works as well as your data readiness, analytics capability, and operating discipline. In other words, tools are not the starting point; capability and data foundations are.

Q2: Why is there growing pressure to use data, analytics, and A.I. in Marketing?
A: Because Marketing is being asked to prove contribution, optimize investments, and improve decisions with measurable business impact. External research reinforces the urgency: nearly 40% of companies struggle to convert data into actionable insights, and leaders are calling for more sophisticated ways to connect activities to business metrics. At the same time, demand for predictive performance analytics is rising as organizations attempt to anticipate future scenarios using past and present performance data.

Q3: What does the budget trend suggest about analytics becoming a priority?
A: It suggests analytics is moving from a supporting function to a strategic investment area. One cited study projects analytics spending increasing from 5.5% of Marketing budgets to 18.1% over the next three years—an indicator that organizations expect analytics (and, by extension, A.I.-enabled analytics) to materially improve performance and decision quality.

Q4: What is the “ultimate power” of A.I. in Marketing?
A: To drive better decisions by extracting patterns from large volumes of data. As framed in the draft, “data is unquestionably the domain of A.I.”—which matters because the volume of data generated by customer and prospect interactions is overwhelming without intelligent methods to interpret it.

Q5: What can intelligent tools help marketers do, once foundations are in place?
A: They can help teams react faster to market changes, optimize mix models, and improve processes that influence customer buying patterns—enabling more effective strategies, stronger performance, and competitive advantage.

Q6: Why are “shiny new tools” not the answer by themselves?
A: Because many organizations have already invested heavily in MarTech stacks, yet still struggle to keep pace with customer expectations and to convert data into insights. The issue is not the number of tools; it is whether marketers understand what data they have, how to use it, and how to connect systems and standards so insights can be generated reliably.

Q7: What does the research imply about Marketing’s data readiness gap?
A: That marketers are often less data-savvy than they believe. The cited perspective argues many marketers do not know what data is available or how to use it effectively, even as omnichannel complexity increases and customer expectations for frictionless experiences rise.

Q8: What four steps should Marketing take before applying A.I. to its data?
A:

Build data and analytics skills. Data is the “DNA” of Marketing. Regardless of role, marketers need foundational data literacy, and organizations need sufficient analytics talent (in-house or outsourced) to convert growing data volumes into decision-grade insights.
Create a data management strategy and consistent standards. Start with decision clarity: define which decisions A.I. should improve, then establish how data will be acquired, prepared, normalized, and governed—and how models will be developed, tested, and deployed.
Fix data silos. Connect the systems that house customer and performance data; siloed data undermines both customer experience and analytics effectiveness.
Establish an analytics Center of Excellence (CoE). Build a cross-functional team of business and technical professionals to standardize methodologies, tools, models, and techniques—improving effectiveness and efficiency across business units.

Q9: What is the practical end goal of these steps?
A: To extract patterns from data that enable intelligent, actionable decisions—improving Marketing performance and operational excellence. A.I. is not the objective; better decisions and better outcomes are.

Q10: Is A.I. becoming the new norm for Best-in-Class Marketing organizations?
A: The draft’s position is appropriately measured: it is trending in that direction, but the determining factor will be whether organizations build the data, analytics, and governance foundations required to use A.I. credibly and effectively.

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