How Agentic AI Workflows Are Connecting Marketing and Product Management
Brace yourself for what I’m about to say: A business running without agentic AI should be considered outdated.
Business workflows have evolved significantly over the past few years. Agentic AI is now transforming these workflows, with one of its key use cases being to act as a bridge between product and operations.
The agentic AI market is set to surge from USD 7.29 billion in 2025 to USD 139.19 billion by 2034, growing at a 40.5% CAGR.
Incorporating agentic AI into business functions is not a nice-to-have option anymore. It’s necessary in today’s competitive tech landscape.
You may be wondering, how can my business tie product management to marketing using agentic AI? I’m glad you asked—let’s dive in.
The missing piece: Feedback
Although most teams within a business do a great job coordinating when it comes to communicating user pain points and feedback, this process is much easier and more efficient when it’s automated, which is exactly what agentic AI can do. An AI agent can monitor user behavior, pulling in data from websites, emails, and CRM systems. It offers more dynamic and real-time insight into user behavior for continuous product improvement, unlike waiting for quarterly meetings between product management and marketing teams.
For example, your marketing team is struggling to accurately gauge which prospect is more likely to buy your product, and the product management team doesn’t know which feature is non-negotiable for the prospect. Agentic AI collects user behavior data and surfaces which prospect is more likely to buy the product and which feature the teams should bet the deal on.
Multi-agent orchestration
It isn’t sufficient to have just one agent working on a task or reporting on campaigns. To leverage an agentic AI workforce, multiple agents should communicate with each other to create an orchestration of well-executed tasks. They can be deployed to evaluate performance across your platforms, suggest improvements, and provide insights.
Let me give you a basic analogy: Three friends are helping you sell lemonade at your lemonade stand, and they are all assigned a task. Friend A monitors how many people stop by your lemonade stand, friend B makes signs for the stand, and friend C interacts with customers. Instead of them operating individually and reporting back to you at the end of the day, they’re constantly communicating with each other to improve lemonade sales.
Friend A notices that not many people are stopping by your stand, so asks friend B to make the sign more catchy. Friend A also sees that a lot of kids are around, so friend C starts advertising that the lemonade is sweet. While all this is happening, friend A whispers to you, “People are asking for strawberry flavor—maybe you should make some?” This is how multiple agents orchestrate a process and suggest features and improvements to the product management team that they didn’t know were needed.
A final word
With the introduction of agentic AI, there’s sure to be a significant business impact in tech. Review cycles have been cut in half, enabling faster campaign rollouts and quicker responses to market changes. Now is the time to incorporate agentic AI for efficient turnarounds between marketing and product management.
