SOCi report: 52% of marketers cite privacy as top concern in Retail AI adoption

Privacy concerns about AI are top of mind for marketers at U.S. companies. This finding comes from SOCi, a co-marketing cloud platform for multi-location enterprises, which has released Part IV of its AI Marketing Transformation Index.

When asked about their concerns regarding AI in marketing, “privacy concerns” topped the list at 52%. This was followed by “over-dependence on technology” (46%), “making data ‘AI-ready’” (41%) and “AI potentially replacing my role” (36%). Other concerns included integration with current systems (35%) and ensuring ROI (30%).

When asked if their company “has a clear and actionable plan for AI integration into their marketing strategy,” 36% said “no,” with 10% of that group stating "no and we don’t know where to start." Alternatively, 40% said they have “a clear plan and are executing it well,” while one-quarter (22%) admitted they had a plan but “are struggling with the execution.”

“Effective AI integration in marketing demands not just technological capability but also strategic clarity,” said Monica Ho, chief marketing officer at SOCi. “Our study reveals a gap between awareness and action, underscoring an urgent need for a roadmap to better navigate AI adoption.”

“Privacy concerns often stem from how companies collect, store and use consumer data for marketing purposes,” Ho said. “With AI's ability to analyze large datasets, there's an increased risk of sensitive information being mishandled or used in ways that violate consumer privacy. Establishing clear internal guidelines and policies around AI usage in marketing can help ensure ethical practices and compliance with laws.”

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In terms of established usage and privacy guidelines for AI technology, 39% confirmed having guidelines in place, while 35% are currently in the process of developing these guidelines. However, 22% do not have any usage or privacy guidelines in place.

“Establishing comprehensive AI usage and privacy guidelines is paramount for businesses,” Ho said. “It's a crucial step not just for compliance but also for building trust with consumers and ensuring responsible utilization of AI.”

Additional findings from the study include:

• Integration Challenges: When asked about the challenges faced when integrating AI into marketing strategies and workflows, the top concern was “lack of understanding of AI,” cited by 47%. This was followed by "difficulty in aligning AI with marketing goals" (44%), “inadequate training or education” (43%) and "budget constraints” (36%). Only 1% reported other challenges, while 7% are not integrating AI at all.
• Resource Allocation Needs: Regarding resources needed for better AI integration, the most significant need was for “more time to understand and implement AI,” highlighted by 62%. This was followed by “more trained personnel” (53%), “more budget” (44%) and “clearer strategies from leadership” (32%).
• Personnel Readiness for AI: Concerning dedicated marketing personnel for overseeing AI models, 39% have specialized staff, 21% do not and 37% are in the process of training or upskilling their staff.
• Confidence in Using AI: When evaluating confidence in leveraging large language models and generative AI to support marketing efforts, three-quarters (75%) are confident, with 26% of that group very confident and 49% somewhat confident. In contrast, 19% aren't confident, with 14% not very confident and 5% not confident at all.
• Ease of Use: Regarding the ease of implementing AI, 25% said using AI was difficult (3% “Very difficult,” 22% “Somewhat difficult”), while 40% had an easier experience (27% “Somewhat easy,” 13% “Very easy”). The largest group, 35%, were neutral, indicating that a clear consensus on AI implementation ease is yet to be established.

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“Today's findings reveal a big gap in AI adoption for marketing,” Ho said. “While 3-in-4 are confident in using AI, challenges like understanding AI, aligning it with marketing goals, and resource constraints persist. This calls for a strategic focus on education, planning, and resource allocation to maximize AI's potential.”