INSIGHT

AI-Enabled Commercialization

Why AI changes commercialization most when it improves how organizations make decisions, coordinate work, and scale expertise, not when it simply adds more technology.

By Juan A. Flores – Published June 3, 2025

Commercialization Has a Coordination Problem

Life sciences commercialization has become substantially more interconnected. Commercial, medical, market access, digital, analytics, regulatory, and affiliate teams contribute to outcomes that no single function controls, while customer journeys span more channels, stakeholders, data sources, and interactions than traditional commercial models were designed to manage.

AI arrives in an environment that was already difficult to coordinate. That matters because adding powerful technology to fragmented processes does not automatically improve commercialization. In some cases, it simply allows organizations to produce more analysis, content, recommendations, and activity without resolving the underlying coordination problem.

The opportunity is more fundamental. AI can change the economics of how commercial organizations process information, deploy expertise, coordinate repetitive work, and support decisions. The challenge is turning that capability into better commercial performance.

Start With the Work, Not the Technology

The wrong starting question is often, “Where can we use AI?” A better one is, “Where is commercialization unnecessarily slow, fragmented, repetitive, or dependent on work that technology could perform differently?”

That changes the conversation. A team spending substantial time gathering and synthesizing information has a different opportunity from one struggling with fragmented customer engagement, slow content processes, inconsistent affiliate execution, or poor visibility across markets.

AI-enabled commercialization should therefore begin with the commercial problem and redesign the work around it. Sometimes AI will remove steps, sometimes it will accelerate them, and sometimes the greatest improvement will come from simplifying the process before introducing AI at all.

AI Can Change How Expertise Scales

Commercial organizations have traditionally scaled capability by adding people, centralizing specialist teams, creating shared services, or building additional organizational layers. AI introduces another possibility: some knowledge-intensive work can scale without increasing organizational structure at the same rate.

Research can be synthesized faster, large bodies of internal knowledge can become easier to interrogate, and initial analyses and scenarios can be developed more quickly. Repetitive preparation can be reduced, allowing experienced professionals to spend less time assembling information and more time interpreting it.

That does not eliminate the need for expertise. It changes where expertise creates the most value. When AI performs more of the information-heavy work, experienced people can concentrate on judgment, challenge, customer understanding, prioritization, and decisions.

For international life sciences organizations, that shift can be particularly valuable because scarce expertise often needs to support multiple markets without turning every capability into a large centralized function.

Omnichannel Is a Good Test of the Difference

Omnichannel provides a useful example because many organizations have already invested heavily in platforms, channels, data, content, and analytics. Yet adding technology has not always produced a genuinely connected customer experience.

The problem is often operational rather than technological. Customer information sits in different systems, content processes move at different speeds, functions have different objectives, affiliates operate with different levels of maturity, and nobody owns the entire journey from insight to coordinated action.

AI can improve segmentation, synthesis, content preparation, next-best-action support, knowledge retrieval, and analysis of customer signals. But if the surrounding operating model remains fragmented, those capabilities can create more disconnected activity rather than better engagement. AI-enabled commercialization therefore requires integration across decisions and workflows, not merely integration across technologies.

The Global-to-Local Model Can Work Differently

One of the most interesting opportunities lies in the relationship between global, regional, and affiliate teams. Traditional models often force a difficult compromise: centralization creates consistency and scale but can become distant from local reality, while decentralization creates proximity but can duplicate work, capabilities, and infrastructure across markets.

AI can alter that balance. Common knowledge, analytical support, reusable workflows, content foundations, and specialist capabilities can be made more accessible across markets, while local teams retain responsibility for context, stakeholder understanding, regulatory realities, and decisions requiring local judgment.

This does not make global-to-local coordination disappear. It can, however, allow organizations to reconsider which capabilities genuinely need to be replicated and which can be shared more intelligently.

More Output Is Not the Same as Better Commercialization

Generative AI makes producing things remarkably easy. Commercial organizations can create more content, more analyses, more customer variations, more summaries, and more recommendations than before. That creates a new management problem: abundance.

When production becomes cheaper, prioritization becomes more important. A commercial organization does not necessarily benefit from generating fifty customer variations if five would create the relevant impact, or from producing continuous analysis that nobody converts into a decision.

AI-enabled commercialization therefore requires discipline about where additional scale creates value. The objective should not be maximum AI utilization. It should be better commercial choices and more effective execution.

Governance Has to Become Part of the Workflow

Life sciences cannot treat governance as something that happens after AI has produced an output. Medical, legal, regulatory, privacy, data, security, and commercial considerations can affect how AI is used, what information it can access, what it can generate, and how outputs can be acted upon.

The practical solution is not necessarily more governance layers. It is to design appropriate controls into the workflow from the beginning, with requirements proportionate to the risk and consequence of the activity.

A low-risk internal synthesis task should not necessarily be governed like customer-facing content or a decision involving sensitive data. Effective AI-enabled commercialization needs enough governance to make responsible scale possible without treating every use case as identical.

AI Discoverability Adds Another Commercial Dimension

AI is also beginning to change commercialization outside the organization. Customers, partners, investors, healthcare professionals, and other stakeholders can use AI-mediated tools to research companies, products, categories, evidence, and areas of expertise.

That creates a connection between commercialization and AI discoverability, but it should not be confused with the whole AI-enabled commercialization agenda. Discoverability concerns how an organization and its information can be found and represented. AI-enabled commercialization is the broader question of how AI changes the commercial operating model itself.

The two intersect when changing information behavior affects customer journeys, educational pathways, category understanding, or how an organization enters consideration. That intersection deserves attention, particularly as AI-mediated discovery becomes more established.

The Human Role Changes Rather Than Disappears

As AI takes on more research, synthesis, preparation, analysis, and coordination work, the human contribution becomes more concentrated around context and judgment. Commercialization still depends on understanding customers, interpreting incomplete information, balancing competing priorities, navigating organizational realities, and making decisions for which someone must ultimately be accountable.

That is why I do not see AI-enabled commercialization as a progression toward autonomous commercial organizations. I see it as an opportunity to redesign them so that technology performs more of the work technology is good at, while experienced people remain closest to the decisions where experience genuinely matters.

This is the practical meaning of Human-Led. AI-Enabled.

The JUYMO Perspective

At JUYMO, we approach AI-enabled commercialization as an operating-model question rather than a technology program. The starting point is the commercial outcome, followed by the decisions, workflows, capabilities, governance, and organizational relationships required to deliver it.

AI can then be applied where it creates genuine leverage: reducing low-value work, making knowledge more accessible, increasing analytical capacity, improving coordination, or allowing scarce expertise to scale further. The purpose is not to make commercialization look more technologically advanced. It is to make commercialization work better.