Ben Nichols coauthored this article.

Key takeaways:

• AI has eliminated the data advantage—but created decision overload.
• The real competitive edge is no longer insights, but decision-making speed and clarity.
• Pharma organizations struggle with alignment, risk aversion and unclear decision rights.
• Winning pharma marketing teams act on signals faster and learn through in-market experimentation.

There’s a version of the life sciences marketing transformation story that sounds like progress. Teams have more insights, more tools and more channels to reach customers than at any point in the industry’s history. AI is beginning to generate insights at a speed that would have been unimaginable five years ago. By almost every measure, marketing organizations are better resourced, better informed and better equipped than ever before.

The problem is that the same is true of every competitor. The infrastructure gap that once separated leading organizations has largely closed. AI tools, data platforms and insights capabilities that were differentiators three years ago are now table stakes. When every team has access to the same data and the same AI models, the strategies they produce begin to converge. The organizations that pull away won’t be defined by the data and technology they have. They’ll be defined by what their marketers do with it.

And yet, across our roundtable conversations with chief marketing officers (CMOs) and chief commercial officers (CCOs) at both top-20 and midsize pharma companies, a different pattern keeps surfacing. The pace of insight generation is accelerating but the ability to make clear, timely decisions is not keeping pace. Marketing leaders are not struggling to generate options; they are struggling to choose between them. One leader said:

“I have been looking at our workforce lately and my estimate is more than half of our marketers were hired for a job that doesn’t exist anymore. They were hired to manage agencies and to produce stuff, but more and more we need them to think …. and to get better at making choices at the speed of insights, which is not a strong muscle today … we have to reimagine what it means to be a marketer.”

We believe this is the defining commercial challenge of the next phase of pharma marketing strategy. And we don’t think more data, more tools or more processes will solve it.

How does data overload hinder decision clarity in marketing?

It should feel counterintuitive that better insights slow down decisions. But that’s what CMOs are seeing.

Access to insights is no longer the constraint. Often, it has become the source of friction. Leaders describe being pulled in multiple directions by competing signals, market data pointing one way, engagement metrics pointing another, customer and field feedback somewhere else entirely. The volume of inputs has grown faster than organizations’ ability to reconcile them.

The result is hesitation. Teams that once moved from insight to action are now moving from insight to deeper analysis while waiting for certainty that never quite arrives. As one CMO told us directly:

“I am increasingly looking for marketers with a really good eye for insights … we have many launch brands, there’s a lot of noise in our categories and I need marketers who know how to get to action quickly. Many of our marketers prefer to rely a single research study to prove a hypothesis, rather than being able to work across many sources to really find signals and start experimenting.”

The democratization of data hasn’t produced better decision-making. Instead, it’s produced more of it across every competitor simultaneously. Many marketing organizations have simply ignored new tools and techniques for the “tried and true,” whereas other marketing teams have ordered “deep dives” that tend to yield more paralysis than action. The teams that are succeeding are finding signals faster, conducting faster A/B tests and rapidly learning alongside their insights counterparts.

The competitive advantage no longer belongs to the team with the best data. It belongs to the team that knows which signals to trust and acts without waiting for complete certainty.

How is AI changing pharma marketing roles?

As AI takes on more of the analytics and insights workload, the marketing process is also evolving. The traditional marketer, often developed through sales and trained to optimize within a known playbook, isn’t automatically equipped for this. Analysis and synthesis are different skills. Breaking things down is not the same as putting them together. And with AI increasingly capable of handling the former, the latter is where human judgment becomes the differentiable asset. As one executive told us:

“We used to hire exclusively for expertise … in consumer, in key accounts in oncology marketing … increasingly we’re looking for marketers with a strong collaborative muscle, curiosity and the ability to ask the right questions, which is a misunderstood skill needed in the new world.”

Many organizations have responded by democratizing access to insights, such as self-service tools, marketing mix simulations or competitive “war gaming.” But access hasn’t produced engagement. The tools sit unused, not because marketers lack curiosity, but because it’s not clear what they are supposed to do when an insight arrives. If the job is still to build the plan, manage the calendar and get content approved, more signals just create more noise in that very rigid process. The operating model has to change what the marketer is accountable for, not just what they have access to.

Marketing leaders need to adjust how they incent and coach their teams, from activity-based goals to more experimentation and outcomes-based goals.

Why does risk aversion slow execution in pharma marketing?

The second constraint is cultural, and it is deeply embedded.

Most pharma marketing organizations are structured to minimize risk before action. Business cases are designed to ensure decisions are correct before execution begins. Alignment processes exist to build consensus before anyone commits to a direction. These aren’t irrational choices. They reflect the high financial and reputational stakes of the industry. But they increasingly create a structural mismatch with the speed at which markets, customers and competitive dynamics are moving.

We heard a version of this in our discussions that was unusually candid:

“I’ve been working to instill this notion that if failure is not an option, then neither is success. And so I think that is, to me, a real concern about the way some of our marketers have grown up.”

The contrast with other industries is sharp. In our Q1 CMO roundtable, we brought in a CMO from a major entertainment studio, an organization accustomed to operating at what we might call blockbuster speed. Their approach to campaign development looks almost nothing like pharma’s, with multiple creative directions developed in parallel, versions moved into market quickly and underperformers cut fast, all built on the assumption that the goal is not to get it right the first time, but to learn faster than the competition.

The time invested in prevalidation isn’t producing better outcomes, it’s producing safer ones. And in a more dynamic environment, playing it safe is only safe until a competitor stops doing the same.

Strategic trade-offs in pharma leadership

We’re not arguing that pharma should operate like an entertainment studio; the regulatory context, the stakes and the customer relationships are fundamentally different. But we do think the gap is wider than it needs to be, and that leaders have more room to shift the balance than they typically exercise.

The core trade-offs we see leaders navigating right now:

1. Trust data versus rely on judgment. These are not opposites, but organizations tend to treat them that way. The leaders who are navigating this well are those who use data to frame the decision and judgment to make it rather than using data as a substitute for judgment or dismissing data in favor of intuition.
2. Validate up front versus learning in the market. This is where we see the greatest opportunity for pharma. The shift from long prevalidation cycles to faster in-market testing does not require abandoning rigor, it requires redirecting it. The goal is not less discipline, it is applying discipline to the right things at the right time.

But none of those shifts happen without explicit permission from leadership. Most teams default to consensus and caution not because they lack capability, but because the incentives and decision rights they operate under make risk aversion the rational choice. The leaders making real progress are those who have changed the conditions, not just the conversations. They have clarified what marketers can move on without approval, redesigned incentives around outcome quality rather than activity volume and signaled explicitly that bold bets are expected and course correction is something to celebrate, not to explain away.

Organizations that cannot make faster, clearer decisions risk turning real investment in data and AI into incremental efficiency gains rather than meaningful commercial impact. The infrastructure improves, the dashboards multiply, the meetings to review them grow longer and the market moves on regardless.

The insight was never the problem. It was always about what you do with the insight and when.

Redefining marketing roles in a data-rich environment

What is not keeping pace is the marketer’s relationship to those insights—how they are interpreted, weighted, acted on and owned. This has direct implications for who gets hired, how roles are designed and what marketers are actually accountable for. We see three shifts that matter most.

1. Redesign the work, not just training. Roles still structured around content production and approval cycles reinforce old behaviors regardless of what upskilling is layered on top. The shift is from producing work to owning outcomes and focusing on prioritization, scenario assessment, resource allocation and course correction. That can’t be addressed through a training program alone. It requires mapping where marketers spend time today and changing what they’re measured on. Behavior follows incentives, not curriculum.
2. Measure decision quality, not activity volume. This moment requires a marketer who isn’t an annual planner, but someone who evaluates scenarios, makes resource calls with imperfect information and adjusts quickly when signals shift. Winning brands aren’t making more decisions. They’re making fewer wrong ones and reallocating faster when it matters. The difference between top and bottom performers is increasingly explained by decision speed, not promotional spend.
3. Expand the mandate beyond the promotions. As self-serve analytics become standard, marketers increasingly own data access and interpretation directly, not just within their brand, but across medical, access, field and digital. The cross-functional relationships must shift from coordination to co-ownership of patient and business outcomes. This is closer to general management than traditional brand management, and traditional marketers have not been developed for it.

The ecosystem has caught up: data is abundant, AI is pervasive and tools are no longer differentiators. What hasn’t kept pace is the marketer’s relationship to those insights: how they are interpreted, prioritized and acted on.

The insight was never the problem. The decision is.