Copy That: Teaching AI to Speak the Right Language
Scalable approach to creating higher-quality AI content—improving accuracy and strengthening client relationships.
Bronnie stared at the client’s email on their laptop screen. The subject line read: “This isn’t working.” It was from the marketing lead at a major retail client who had entrusted AdVeritas (a fictional agency known for blending creativity with cutting-edge AI) to deliver their latest national campaign. The AI-generated copy was supposed to be witty, persuasive, and consistent with the client’s brand identity. Instead, the campaign had produced a mix of messages that felt disjointed: some clever and sharp, others rambling or even slightly off-message. What should have been a showcase of AI-powered efficiency had become a source of frustration for the client and a stress-inducing headache for Bronnie and their team.
At AdVeritas, the promise of AI had been tantalizing. Faster campaign production. Lower costs. The ability to deliver highly personalized copy to different audiences at the push of a button. But the reality was proving far more complicated. Editors were working overtime to fix errors, client trust was slipping, and the agency’s reputation for reliability was beginning to show cracks. Bronnie (as the fictional strategy director responsible for the account) could feel the weight of the problem pressing harder with every client call.
The Pressure Cooker Inside the Creative Machine
The pressure wasn’t only coming from within. Competitors like Brandwagon (another fictional agency, notorious for splashy claims and attention-grabbing case studies) were loudly touting their AI systems as capable of flawless, brand-safe copy on demand. Clients were listening. Bronnie’s inbox was filling with subtle, and not-so-subtle, questions: “Why can’t your system do what theirs seems to be doing?”
At the same time, external pressures were mounting. Advertising regulators and industry watchdogs had begun scrutinizing AI-generated campaigns more closely. A single misleading claim in a headline, or a tone-deaf slogan generated by the model, could trigger fines or a PR nightmare. Bronnie knew the stakes: it wasn’t just about producing good copy anymore; it was about ensuring that every single line of text met strict standards of accuracy and tone.
The irony was hard to miss. The agency had invested heavily in AI to reduce reliance on human labor, but now the opposite was happening. Editors and compliance specialists were spending so much time rewriting AI drafts that the supposed efficiency gains had evaporated. Campaigns were delayed. Budgets were stretched. And the people on Bronnie’s team (once energized by the promise of new technology) were burning out trying to bridge the gap between what the AI delivered and what the client expected.
When Doing Nothing Isn’t an Option
Bronnie recognized that ignoring the problem wasn’t an option. If the agency stayed the course, several consequences loomed on the horizon. First, there was the very real risk of losing the retail client, whose account made up nearly a third of the agency’s annual revenue. Replacing that business would take months, if not years, and the loss would reverberate across the entire firm.
Even if the client didn’t walk, the reputational damage could be lasting. In an industry where trust is the currency, even a hint that AdVeritas couldn’t deliver reliable AI-driven campaigns would be enough to send other clients exploring alternatives. Brandwagon and other competitors would seize on the opportunity, pitching themselves as the safer, smarter choice for AI-powered creative work.
There was also the cost problem. Continuing to patch over AI’s shortcomings with more human intervention would erode the very margins that the investment in AI was supposed to protect. Bronnie could already see the financial reports showing higher labor costs and shrinking profits (a trend that, if left unchecked, would eventually force painful cuts or restructuring).
Most troubling of all, failing to address the issue would leave AdVeritas behind in the AI race. The firms that cracked the code on scalable, reliable AI-generated content would not just win more clients; they would redefine the market. Bronnie couldn’t shake the feeling that the agency was standing at a crossroads: act decisively now, or risk becoming a cautionary tale of a company that bet big on AI but never figured out how to make it work.
Curious about what happened next? Learn how Bronnie applied a recently published AI research (from Meta and NYU), built a smarter playbook for AI copy, and achieved meaningful business outcomes.