AI Agents for Supercharged Marketing
Anton Ansalmar
I build Mobile App(Swift|SwiftUI|Kotlin), AI-powered Growth for products & services | Marketing Growth Strategist | Architecting data-driven user experiences
In today's crowded marketplace, marketing isn't just about creativity – it's about harnessing data and emerging technologies to drive results.
Many companies are building AI agents for their marketing teams, audio and video production teams, and all the creative people that can use a hand.
For example:
Canva: is using Vertex AI (from Google) to power its Magic Design for Video, helping users bypass editing steps while creating shareable, engaging videos in a matter of seconds.
Procter & Gamble: They used Imagen to develop an internal gen AI platform to accelerate the creation of photo-realistic images and creative assets, giving marketing teams more time to focus on high-level planning and delivering superior experiences for its consumers.
With creative AI agents, anyone can become a designer, artist, or producer. We will have so many AI agents deployed across sectors and industries in the future.
Key Advantages of AI Agents
Why Product Managers Should Care
This isn't just about prettier ads. AI-powered marketing insights reveal deep customer preferences, informing product development and feature prioritization. That's the kind of alignment that boosts ROI across the board.
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Core Functions of the Agent:
The Future of Marketing
I believe specialized AI agents like this are the next frontier for CMOs. It's about augmenting your team's brilliance, not replacing it. Early adopters will have a major competitive edge.
How Marketing Teams Would Use It
Stay tuned for more insights and information on this exciting development in the workplace!
Follow me on twitter @antona23
I help businesses make smarter decisions through AI implementations
10 个月AI marketing tools are extremely powerful for generating creatives. Predictive analytics for marketing is a harder nut to crack, as it requires a proper funnel, attribution, and data standardization. Without these elements its difficult to train a model. I've been toying with some best practice guidelines for synthetically created data through LLMs. Would love to pick your brain about it some time.