McKinsey estimates that agentic AI could power two-thirds of current marketing activities. But for most marketing departments, "AI agents" remain an abstract buzzword. I want to describe what they look like in daily practice, because when you move past chat prompts into autonomous loops, the speed of executing a multi-brand strategy shifts completely.
Moving past the chat window
Most marketers use AI as an advanced search engine or a draft writer: you type a prompt, you get a response, and you copy-paste the result. This is a manual, single-turn transaction. If you want to research a competitor, draft an email, and format a report, you have to prompt the tool three separate times, shifting context and cleaning up formatting between each step.
An AI agent operates differently. It is software configured to achieve a goal autonomously by executing a sequence of steps, evaluating the results at each stage, and deciding how to proceed. It has access to tools—such as search engines, database queries, and code executors. You give it a high-level goal, and it plans, researches, writes, and refines the deliverables without human intervention until the final review.
An agentic workflow in practice
Across my portfolio of five fitness and lifestyle brands, I run a weekly agentic workflow to manage competitor pricing and promotional alignment. In retail fitness equipment (like Spartan Fitness) and e-bikes (like Vintage Iron Cycles), prices and supplier offers change constantly. Keeping track of multiple competitors across different regions used to require hours of manual auditing.
Today, I run an agentic system that executes the following loop:
- Phase 1: Auditing & extraction. The agent visits a defined list of competitor websites, extracts their current promotional banners and pricing for key models (such as commercial treadmills or utility e-bikes), and flags any changes from the previous week.
- Phase 2: Gap analysis. It compares their pricing against our portfolio brands, highlights where we have lost our competitive margin, and identifies specific product lines that require promotional support.
- Phase 3: Creative drafting. It drafts three distinct promotional hooks for each flagged product, tailoring the copy to the specific brand voice (e.g., technical and performance-oriented for Spartan; lifestyle and heritage-focused for Vintage Iron).
- Phase 4: Formatting. It structures the final recommendations into a clean spreadsheet and drafts the HTML and text blocks for our Klaviyo email templates, formatted and ready for testing.
The entire loop runs in the background. I do not spend my morning copying and pasting data between tabs; I spend it reviewing a structured report and deciding which promotional recommendations to execute.
The shift in marketing labor
For three years, I have integrated AI daily to expand what a lean team can deliver. The biggest realization during this time is that agents do not displace creative thinking; they displace coordination and administrative overhead. The hours marketers spend pulling CSVs, formatting decks, chasing copy updates, and aligning templates are the hours that are going first.
By automating the execution loop, a single senior marketer can handle a portfolio of five brands that would typically require a full department. But this leverage only works if the human is focused upstream. The agent can draft copy, but it cannot define your brand's positioning. It can pull pricing data, but it cannot negotiate co-op margins with international equipment suppliers. It can draft email layouts, but it cannot build the boardroom trust required to align stakeholder budgets.
The distinction between the future of marketing and the present is simply literacy. Marketers who continue to treat AI as a copywriter will find themselves constrained by the speed of manual prompts. Those who treat it as an autonomous execution layer will have the leverage to run whole portfolios themselves.