How Great Marketing Leaders Are Turning Nonstop Disruption Into a Competitive Edge

Marketing teams have never had more information, or more tools for generating it. AI can spin up dashboards, summarize a quarter of campaign data, and surface patterns faster than anyone on the team can read them. What none of that does on its own is make the next decision any clearer. The volatility that produced the flood, in technology, consumer behavior, economics, and competition all shifting at once, isn't a phase that ends, which means the marketer staring at three new dashboards is being asked to move faster on less certainty. Without the right foundation and approach, piling more automated marketing workflows on top of that pressure can simply add noise the team then has to cut through.

Monica Smith is the Founder and CEO of MSI The Agency, the integrated growth and marketing agency she has led since 1999, and the creator of MSI Generate, a DecisionOps platform she built from decades of retail and consumer-behavior work. Over more than 30 years she has guided more than 200 brands across retail, healthcare, utilities, and government, scaled businesses from under $100 million to more than $750 million, and earned five patents along the way. Her read on AI in marketing starts with what she sees leaders getting wrong at the first step.

The most common error, she says, is treating AI as one more layer to stack on a team that's already saturated. "You don't want to just put dashboards on top of that for the marketing person. You want to organize the information and help them orient themselves faster, with less cognitive load and a better understanding of what that information means," says Smith. Orientation is the word she keeps returning to, and she treats it as a leadership job rather than a software feature.

Orientation as the leadership job

Smith frames the current environment as sustained instability, what she calls "the age of the chop," where the disruptions don't resolve into a new normal. "We used to think a disruption is coming and we're going to get through it. But disruption is here to stay," says Smith. Technology, consumer behavior, economics, talent, and competition are all moving at the same time, and she argues the leader's task is no longer to defend against that but to accept it and steer teams through it quickly.

That reframing changes what good leadership looks like day to day. What matters, she says, is how the information is organized so people can act on it without being buried under everything the tools now produce at scale. "Chaos exists. That's the disruption. Let's orient the team as fast as we possibly can with the information we've been given," says Smith. It's a skill she doesn't think shows up on a syllabus. "Great leaders will demonstrate new leadership skills that probably aren't being taught in business schools. It will come more through entrepreneurial experience than anything else," says Smith.

Connecting the data teams keep apart

The mechanics of orientation, in Smith's model, start with breaking down the walls between functions that rarely share a view. She wants marketing information sitting beside sales, finance, merchandising, and location data so a team can see cause and effect instead of one slice of it. "It's breaking the silos and bringing in the information that helps you understand cause and effect across a few different areas: sales and marketing, finance, merchandising, and geographic location," says Smith. Done well, she argues, that combined picture is what lets a team connect spend to outcomes rather than reporting activity.

Her go-to example is retail real estate, where a location decision reads very differently once consumer movement is layered onto it. "You need active intelligence to help organize people's orientation, to say not only that a location is good because it's anchored next to a Michaels or a ShopRite, but that these are the consumers migrating in and out of that geographic location," says Smith. That kind of combined signal, she notes, is exactly what AI now makes available in bulk, at the same time consumers lean on AI to make their own buying calls.

Closing the loop on test and learn

Once teams can see across the data, Smith's next requirement is speed of response, a cycle she calls decision ops. The idea is to act on the organized information, capture what happens, and route it straight back into the models. "You want people to take the information they have, organize it appropriately, and make the decision. Then let the AI read those results and quickly feed them back in so that you can test and learn through that responsiveness," says Smith. The pace of that loop, more than the sophistication of any single tool, is what she thinks separates teams that keep up from teams that stall.

She's careful to strip the mystique out of it. For all the new vocabulary, the underlying discipline is one marketers already know. "It's no different than old-school R&D. We're just calling it something a little differently," says Smith. What's changed is that the testing can now happen at the speed information moves, which is why she treats the levels of marketing autonomy a team is ready for as a practical question rather than a theoretical one.

Building the history before building forward

Before any of that pays off, Smith wants two things settled. The first is an accurate record of how the business actually got here, and the second is a shared agreement on how the organization will use AI at all. "The first step is using AI to create and capture the organizational history as accurately as possible. The second is creating rules of engagement for it: 'How do we want to engage with this information, and how should we use it?'" says Smith. Skipping that groundwork, she warns, is how a company ends up with expensive tools and no shared sense of what they're for.

The pressure to skip it is real, because the directive from the top usually arrives as a slogan. "We have to get into AI, we have to get into AI. But that means a lot of different things to different people," says Smith. Her answer is to treat the technology as access to information, structured and unstructured, that only pays off when people are oriented to use it, whether that means the judgment AI still lacks or a clear internal policy before any external rollout. "Right now we're so AI-centric, but I really do think it's about humanity's ability to embrace it," says Smith.