AI is making us faster. But are we getting better?

I’ve been thinking a lot about productivity lately. Specifically, how much time we’re all spending trying to save time.

We’re automating tasks, experimenting with AI tools and finding new ways to get things done faster. And in many cases, it’s working. Work that used to take hours can now take minutes.

That’s exciting. But it raises a question I don’t think we’re spending enough time on: What are we actually doing with all that time we’re saving?

I recently attended MAICON 2026 (the Marketing Artificial Intelligence Conference), where much of the conversation centered on how quickly AI capabilities are advancing. One idea that stuck with me was the distinction between keeping humans in the loop and putting humans in the lead.

It got me thinking about what AI transformation really looks like inside an organization. Because while technology is moving quickly, the way we organize our work, share information and make decisions hasn’t necessarily caught up.

And that’s where I think the bigger opportunity lies.

Productivity isn’t the same as progress

Most conversations about AI in business start with efficiency. How many hours can we save? How many processes can we automate? How much more can our teams accomplish?

Those are fair questions. As someone who works in operations, I’m certainly not going to argue against making things more efficient, but efficiency is only part of the equation.

Take something as straightforward as reporting. An operations manager might spend a significant amount of time gathering data from different systems, organizing it and building a dashboard that helps leadership understand what’s happening across the business. AI can make parts of that process dramatically easier. Information can be organized, visualized and surfaced more quickly, giving teams faster access to what they need.

But the real opportunity isn’t just producing the dashboard in less time. It’s giving that operations manager more room to interpret the information, spot potential problems, understand what’s driving performance and help the business make better decisions.

‍The value isn’t simply in getting to the information faster. It’s in what we do once we have it. And I think that principle applies well beyond operations.

‍Humans in the lead, not just in the loop

One of the recurring themes at MAICON was the importance of human involvement as AI becomes more capable. We’ve all heard the phrase “human in the loop.” And for good reason. Human oversight matters, particularly when the work involves judgment, accountability or decisions with real consequences. But there’s a difference between having someone review what AI produces and having someone actively lead what AI helps make possible.

At VSA, this distinction is especially relevant to creative work. AI can be a powerful tool for exploring, visualizing, iterating and bringing ideas to life. But the strategic insight, imagination, cultural understanding and creative direction behind meaningful work come from people.

The same is true in operations. AI might help identify patterns in business data, but it takes human context and judgment to determine which patterns matter, what trade-offs are worth making and how to bring people together around a decision.

In both cases, AI supports the work. People give it direction.

That doesn’t mean every task requires the same level of human involvement. Some processes should be automated. Others need thoughtful oversight. And some decisions should remain firmly in human hands. The challenge is knowing the difference.

‍The operating model is the opportunity

Here’s where things get interesting. Making an individual employee more productive doesn’t automatically make an organization more effective.

Imagine a team that can complete an analysis in minutes instead of days, but still needs to navigate the same approval process, wait for the same meetings or reconcile information across disconnected departments.

The work has accelerated. The organization hasn’t. That’s not necessarily a technology problem. It’s an operating model problem. AI transformation isn’t just about adopting new tools. It’s about rethinking how work moves through an organization.

Who needs to be involved in a decision? Where does information get stuck? Which approvals protect quality, and which exist because that’s how we’ve always done things? How do teams share what they’re learning rather than solve the same problems independently?

These are operational questions, but they have strategic implications. For creative organizations like VSA, the answer isn’t to prioritize speed at the expense of thoughtful work. It’s to create the conditions where teams can spend less energy navigating unnecessary friction and more energy doing the work that matters.

That might mean better access to information, clearer decision-making responsibilities, more connected workflows or a more intentional approach to determining where AI fits into the process. The goal isn’t to make every part of the business move at machine speed. It’s to make sure the organization can actually benefit from the capabilities it now has.

‍What should we do with the time we get back?

I think this is the question leaders should be asking as they build their AI strategies.

Because when AI creates capacity, we have choices.

We can use that capacity to do more of the same work. We can reduce costs. Or we can reinvest some of that time and attention in the things that are harder to automate: developing people, solving more complex problems, strengthening client relationships, exploring new opportunities and making more thoughtful decisions.

There’s no single right answer for every organization, but there should be an intentional one. And that requires us to measure more than productivity.

Instead of only asking how much time a new tool saves, we should also be asking whether it improves the quality of decisions, creates better experiences for clients and employees, or helps teams deliver stronger business outcomes.

Those are harder things to measure than hours saved. They’re also much closer to what transformation is supposed to accomplish.

‍The next phase of AI transformation

Another thing I took away from MAICON is that we’re still early in understanding what AI will mean for the way businesses operate.

Technology will keep evolving. Our tools will get better. And the opportunities to automate and accelerate work will continue to grow. But I don’t think the organizations that benefit most will necessarily be the ones that adopt the most tools or produce the most output.

They’ll be the ones that are thoughtful about how they put those capabilities to work—how they connect people, processes, information and ideas to create something more valuable. Because ultimately, the promise of AI isn’t just that it can help us do the same work faster. It’s that it gives us an opportunity to rethink what deserves our time in the first place.

And for me, that’s where the real transformation begins.