How AI agents are transforming marketing strategies for brands
Marketers must transition from campaign-focused tactics to brand-driven, cross-functional experience design to succeed with both human customers and AI agents.
The Shift from Campaign-Led to Brand-Led Strategies
Marketing is undergoing a significant transformation as autonomous AI agents become integral to consumer interactions. Brands must pivot from traditional campaign-led approaches to a more holistic, brand-led strategy that emphasizes cross-functional experience design. This shift is critical to engaging both human customers and AI-driven systems effectively.
Why the Change Matters
- AI agents are influencing purchasing decisions: With AI tools like chatbots, voice assistants, and recommendation engines playing a larger role in customer journeys, brands need to ensure their messaging aligns with these platforms.
- Human-centric design remains key: While AI is a growing force, human customers still demand personalized, seamless experiences. A brand-led approach ensures consistency across all touchpoints.
- Cross-functional collaboration is essential: Marketing teams must work closely with product, tech, and customer service teams to create cohesive experiences.
How Brands Can Respond
- Prioritize brand consistency: Ensure messaging and values are clear across all platforms, whether AI or human-driven.
- Leverage data-driven insights: Use AI to analyze customer behavior and refine strategies in real time.
- Invest in experience design: Focus on creating intuitive, engaging interactions that work for both humans and AI agents.
The Future of Marketing
As AI continues to evolve, brands that embrace this shift early will gain a competitive edge. The key lies in balancing automation with authentic human connection.
For more insights, visit Ad Age.
Copyright © 2025. Crain Communications, Inc. All Rights Reserved.
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About the Author
Dr. Sarah Chen
AI Research Expert
A seasoned AI expert with 15 years of research experience, formerly worked at Stanford AI Lab for 8 years, specializing in machine learning and natural language processing. Currently serves as technical advisor for multiple AI companies and regularly contributes AI technology analysis articles to authoritative media like MIT Technology Review.