Product Lead, Agentic Ads Platform

The Problem

Every large advertiser runs the same loop: strategist reviews performance data, decides what to test next, briefs a designer, waits for assets, hands them to a media buyer who builds campaigns across Meta and Google and TikTok, launches, monitors, repeats. The process is slow because creative production, campaign management, and performance analysis happen in three separate systems, and insights travel between them through decks, Slack threads, and weekly syncs.

Companies like Nike, Uber, and PepsiCo spend millions per month on digital ads and employ entire teams just to keep this loop turning. Most teams run 10 to 15 creative tests per month because every test requires manual work across multiple tools and teams. The result: most ad spend runs against stale creative, performance degrades from fatigue, and the gap between "what's working" and "what gets made next" is measured in weeks.

Why Agents Change This

Advertising is uniquely high-leverage for AI agents because nearly every step in the workflow is now automatable, and the steps compound:

• Research. Agents can continuously monitor competitor ad libraries, auto-classify creative by format, hook, and message, and surface positioning gaps. What used to require a strategist spending hours in Meta Ad Library happens in the background, always current.
• Creative production. Generative models can now produce ad-quality images and video. The constraint has shifted from "can AI make this" to "can AI make this on-brand and on-spec for each platform." That is a solvable engineering problem, not a research problem.
• Campaign structure. Building properly structured campaigns (naming conventions, audience splits, placement targeting, budget allocation) across five ad platforms is tedious, rules-based work. Agents can do it faster and more consistently than humans.
• Optimization. Detecting creative fatigue, reallocating budget toward winners, and knowing when to rotate in fresh variants is pattern recognition across structured data. Agents excel at exactly this.
• The compounding effect. When one agent handles the full chain (research, production, activation, analysis, iteration) as a single workflow, the cycle time collapses from weeks to hours. A team that runs 15 tests per month can run 50+, not because any individual step got faster, but because the coordination cost between steps drops to nearly zero.

Today: A performance marketing team testing new concepts on Meta and TikTok spends 2 to 3 weeks per cycle.

With agents: The marketing lead describes the objective. An agent analyzes recent performance, identifies the highest-value concepts to test, generates on-brand variants across formats, builds campaigns, launches after human approval, monitors results, detects fatigue, and queues replacements. Same team, 5x to 7x the testing volume, because the human bottleneck becomes strategy and judgment, not execution.

Why Hightouch, Why Now

Hightouch built the Agentic Marketing Platform: one system where AI agents own entire marketing workflows across lifecycle (email, SMS, push through Braze, Iterable, Salesforce, Adobe), advertising (creative production and activation across paid channels), and product experiences (real-time web and mobile experimentation). All three surfaces share the same agent infrastru