Everyone’s becoming faster with AI. But are we getting better?

AI is accelerating productivity. Whether this is improving business performance is another question entirely. At a recent Brand Rebels gathering in Oslo, we asked a room of CMOs whether their brands had become more distinctive after two years of AI. Not one hand went up.

That same tension surfaced at our first New York Brand Rebels event on October 28, bringing together marketing and brand leaders from healthcare, financial services, industrial B2B, construction, hospitality and consulting.

Everyone is using AI. Everyone is producing more. But is any of it making a meaningful difference?

Governance is getting away from us

IBM research suggests 77% of senior technology leaders feel AI governance is slipping out of their control. It runs the gamut: employees uploading confidential spreadsheets to personal AI accounts, departments developing their own workflows, vendors selling AI tools directly into the business without marketing’s knowledge. And, apparently, some of the worst offenders are the most senior executives.

The response so far has been more policies, more committees, more controls. But governance isn’t just about rules. It’s about behavior.

Some organizations are finding practical solutions. AI agents trained to flag off-brand content. Risk-scoring tools that identify what genuinely needs human review. Enterprise platforms connected to company knowledge, making personal AI accounts less attractive. Others are establishing cross-functional processes to evaluate use cases before investing.

The common remedy may just be to make the right behavior easier than the wrong behavior.

An answer isn’t an idea

Back to those CMOs in Oslo. AI is brilliant at producing polished work, but polished doesn’t mean original. And faster doesn’t necessarily mean smarter.

In New York, there was considerable frustration with work that had clearly been handed to AI and passed straight on, sometimes without even removing the telltale formatting.

The danger isn’t just mediocre output but rather losing the ability to recognize it. The better approach agreed by all is to start with human imagination and then use AI to challenge, stretch and sharpen the thinking.

The same goes for data. Dashboards that once took a month can now be produced in hours. But if a purple ad performs best, that doesn’t mean every ad should be purple.

Context matters. Experience matters. Judgment matters.

And there’s a bigger question for leaders: how do we develop those skills in junior employees if AI is doing so much of the work they once learned from?

Just because you can doesn’t mean you should

Few subjects generated more debate than AI-generated people. Some brands have drawn a firm line: no synthetic faces, no AI voiceovers. Real people, including actual employees, in their communications.

Others are more pragmatic. AI imagery is becoming indistinguishable from photography, and disclosed synthetic content is already performing well in paid social.

But this isn’t simply a question of capability. It’s a brand decision. If authenticity is central to your brand promise, synthetic people may undermine it. Even if nobody can tell the difference.

There was greater consensus around AI working behind the scenes: editing, retouching, lighting and production efficiencies.

Customer service presents a similar dilemma. AI can make interactions faster and easier. But twenty prompts in a circular decision tree does not provide satisfactory customer service. Sometimes the most valuable thing a brand can offer is a knowledgeable human being.

More output. Same bottlenecks

Perhaps the biggest challenge is translating individual productivity into organizational performance. Marketing teams can now produce significantly more content, but the people responsible for reviewing, approving and activating it haven’t necessarily become any faster.

And what about the thousands of employees who don’t sit behind desks? One leader described a workforce of frontline technicians and drivers whose daily routines have barely changed. That’s where the real opportunity lies: not simply making office workers more productive, but improving how the entire business operates.

This is all easier said than done. Scaling AI means tackling fragmented systems, inconsistent data and departments that don’t always work together.

Meanwhile, boards are becoming less patient. The open checkbook is closing. AI investment increasingly needs to demonstrate commercial returns.

Some organizations are introducing quarterly reviews, shortening vendor contracts and tying investment more closely to measurable outcomes.

The real competitive advantage

AI capability is becoming increasingly accessible. Everyone has access to much the same technology.

But differentiation doesn’t come from producing more content, deploying more tools and creating another AI-generated strategy deck. It comes from knowing what to do with the technology, where to apply it, when to question it. And when to trust human experience over machine-generated answers.

Governance needs to be practical. Brand values need to guide decisions. And productivity needs to be measured by business outcomes, not volume of output.

Above all, leaders need to protect and develop the judgment that makes good work possible in the first place.

AI has made speed a commodity. Judgment is where the advantage lies.