AI Gave The World Infinite Content—Now What?

Forbes

4 hours ago

Tejas Manohar is the cofounder/co-CEO of Hightouch.
Just a few years ago, generative AI (GenAI) felt more like a curiosity than a tool. We asked language models to write love letters in the style of tech bros or explain quantum physics to a 5-year-old. Visual platforms responded to prompts like "a dragon in a business suit, pixel art style" or "a Renaissance portrait of a barista." The results, while novel and amusing, were rarely practical for business.
That has changed. By the end of 2024, GenAI outputs became sharper, more polished and increasingly indistinguishable from human-created work. In 2025, with tools like GPT-4, Midjourney, Runway and Canva AI becoming widely adopted, content creation is no longer the bottleneck it once was.
Soon, marketing teams will be able to generate dozens of creative options in minutes. However, this shift introduces a new problem: With so much content, how do we decide what to use, for whom and when?
Most marketers are now using GenAI to create assets. While Salesforce reports that 76% of marketers use AI to generate content, the processes for deploying that content haven't evolved. The typical workflow still involves pasting AI-generated copy into spreadsheets, testing a couple of variants, manually picking a winner and repeating it all.
That might work in the short term, but it's not scalable. More importantly, it doesn't improve over time. More content is not the solution unless there's a system to decide which content to use and how.
Imagine an orchestra where every musician trained at Juilliard, but there's no conductor. That's what marketing looks like in a GenAI world without decisioning. There's creativity, but no coordination.
Marketers today face a flood of assets, but the bigger challenge is figuring out what to send, to which audience and when. These are not creation problems. These are decisioning problems. And we're still trying to solve them using tools and mental models—journey builders, marketing calendars and simple A/B tests—built for a world where content is scarce.
Traditional workflows assume that you'll create a handful of subject lines, define a few segments and test some variations. But GenAI doesn't create one or two options—it creates hundreds. Suddenly, you're staring at thousands of possible combinations across messaging, timing, audience and channels.
Marketers can't test every option. They can't manually orchestrate every journey, and they certainly can't rely on batch-and-blast methods anymore. A new approach is needed.
For many organizations, AI decisioning has become a key part of their AI strategy. This new category of technology sits between content creation and content delivery. It enables marketers to deploy AI agents that make real-time decisions about which content to send to which user. These systems use reinforcement learning (the same type of machine learning behind self-driving cars and streaming recommendation engines) to optimize for business outcomes like conversions, retention or lifetime value.
Think of how platforms like Google and Meta Ads operate. You set your goals, upload creative assets and the system optimizes combinations to deliver results. Now imagine that same model applied to email, push, in-app messaging and CRM. That's what AI decisioning aims to achieve, only this time with transparency and control built in.
To adopt AI decisioning effectively, companies need to get the basics right first. That means clarifying goals, improving data access and identifying where manual decisions slow things down. Start small by pinpointing bottlenecks in your workflow, whether that's testing content, segmenting audiences or managing channels.
Silos are a major hurdle. When teams like marketing, data and product work in isolation, decisioning falls flat. Aligning around shared goals, metrics and timelines helps break down these walls and ensures AI systems have the inputs they need to be effective.
The best way to begin is with a focused use case, such as optimizing subject lines or send times. Prove value quickly and then scale. AI decisioning is not about replacing everything at once; it is about creating a system that learns and improves over time.
Used together, these technologies form a closed-loop system. GenAI generates content while AI decisioning systems select the right assets for each user based on performance data. As results come in, those insights feed back into the content generation process, allowing both creation and decisioning to improve continuously.
GenAI acts as the input layer, creating at scale. AI decisioning functions as the optimization layer, learning what works and when. Combined, they create a flywheel where content fuels decisions and decisions enhance future content.
But none of this works without human oversight. Marketers still need to be involved. AI systems must be transparent, auditable and accountable. Teams need to know how decisions are made, what experiments are running and have the ability to approve content and manage risks.
In the coming months, content bottlenecks will fade as GenAI becomes even more integrated into daily workflows. But that's only the first step. The true differentiator will be how effectively teams can deploy the content they generate to drive meaningful results.
The winners in the next era of marketing won't be the ones who generate the most creative assets. They'll be the ones who build systems that know what to do with them and can adapt in real time.
So keep prompting and creating. But remember: the next meaningful shift in marketing won't just come from creation—it will come from smarter decisioning.
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