Today’s life sciences organizations are tasked with delivering personalized, impactful content on a tight budget—all while juggling evolving customer expectations and regulatory hurdles. A best-in-class, end-to-end content ecosystem with a robust operating model is no longer a “nice to have.” It’s a strategic necessity.
Building the operating model foundation
As capabilities evolve and are added across the content lifecycle, the integration of people, processes, and technology becomes increasingly critical for effective execution. The choice of operating model is shaped by a range of drivers such as efficiency, innovation, agility, governance, and scalability. These can help influence how organizations deploy and manage capabilities.
There are several operating model archetypes to consider, each with distinct implications:
Distributed: Marketing capabilities are embedded across functions and teams, promoting agility and local responsiveness.
Influenced: A brand team leads content decisions but receives guidance and leading practices from a strategic content team or function.
Integrated: Core capabilities (e.g., measurement, operations) are centralized, while non-strategic tasks are outsourced for efficiency. For example, global teams can develop foundational content and components, while local teams retain flexibility to adapt, deploy, and measure content for their specific markets.
Centralized: Content supply chain capabilities are centralized within a standalone team but remain aligned with brand objectives, maximizing efficiency, governance, and scale.
Selecting the right operating model depends on an organization’s priorities—whether the focus is on speed, cost savings, innovation, control, or the ability to scale and adapt to future needs.
A prime example
Generative AI (GenAI) is a prime example of how the operating model directly influences the content supply chain. As life sciences organizations seek to harness GenAI for content creation and content lifecycle automation, they face important decisions. These include determining whether these tools should belong to agencies, marketers, or content hubs, and whether GenAI can be most useful for creating net-new assets or scaling derivative content. These choices are not one-size-fits-all, but rather are determined by the ways that teams, tools, and processes are built within each organization’s content operating model.
Potential benefits of getting it right
Some of the key benefits we see when life sciences organizations choose the best operating model for their teams can include efficiency at scale, cost savings, and greater effectiveness.
Efficiency at scale: In our work with clients, we’ve seen that centralized, data-driven operations can help to cut turnaround times by up to 30 days and boost content reuse by up to 70%. Fewer middlemen and faster approvals can result in more content that is delivered faster and at a lower cost.
Cost savings: We’ve seen in our client work that life sciences leaders help drive savings on agency spend by approximately 30-50% when they streamline creative capabilities and operations in-house through an optimized operating model.
Consistency and compliance: An integrated operating model ensures consistent messaging and branding across channels, while making it easier to adhere to Medical Loss Ratio (MLR) requirements.
Looking ahead
In a market where speed, personalization, and resource optimization are crucial to maintaining competitiveness, a powerful content supply chain operating model is the differentiator that can separate industry leaders from the competition. Now is an important time for life sciences leaders to assess their current capabilities, identify gaps, and take bold steps to future-proof their content operating model—helping them reach the right audiences, with the right message, at the right moment.
As leaders in innovation, our Deloitte Digital teams have developed a propriety accelerator that helps our clients holistically assess their content supply chain capabilities through the lenses of both efficiency and effectiveness. By leveraging organizational data and applying proprietary coefficients across the content ecosystem, this accelerator not only helps prioritize high-impact opportunities but also informs the design of an optimal operating model tailored to each life science organization’s unique needs.
About the authors
Bill Carter is a principal in Deloitte's Life Sciences & Health Care practice, specializing in marketing content supply chain. He leads global life sciences and health care organizations through large-scale, transformative initiatives involving process re-engineering and digital enablement to meet strategic and operational objectives.
Andrea Cusano is a managing director in Deloitte's Life Sciences & Health Care practice with over 20 years of experience in digital marketing transformation, customer/client experience and behavior, and customer engagement. She develops digital transformation strategies that leverage data and analytics, customer behavior, and brand strategy.
Morgan Spitalnick is a senior manager in Deloitte's Strategy & Operations practice focused on business model transformation and growth strategy for life sciences clients.
Andrea Matoni is a manager at Deloitte focused on leading marketing transformation for C-level executive clients by bringing marketing strategy, data, technology, and creative together to enhance the digital customer experience, improve business value, and achieve measurable marketing ROI.
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