For decades, life sciences enterprise leaders have debated a familiar strategic question:

Should we build our own technology solutions or buy a SaaS platform?

Historically, many enterprise organizations chose to build bespoke solutions to gain greater control and tailor them precisely to internal processes. For many years this made sense, especially in organizations that had the talent required to build such systems.

Now, with the rapid maturation of purpose-built, vertical SaaS platforms and the emergence of agentic AI, this decision is more complex.

At first glance, the rise of AI may seem like the ultimate justification for building internally. After all, AI can generate code, automate building data pipelines, orchestrate workflows, manage operational processes autonomously and automate complex tasks.

This suggests that organizations can simply prompt AI to build the software they need. If AI can generate applications and automate development, accelerating productivity, why subscribe to a SaaS platform?

But the reality of this decision is more nuanced.

While AI can generate components of a system, it doesn’t fully address the complexity of enterprise life sciences software or the specific skillset required to develop it. Building and customizing a vertical platform requires a combination of domain knowledge, regulatory understanding, robust data models, scalable architecture, appropriate governance frameworks and operational resilience.

AI can’t replace the accumulated industry knowledge embedded in mature, purpose-built platforms. However, it can help automate processes enabled by vertical SaaS platforms to deliver additional operational efficiencies and accelerate business outcomes.

This hybrid approach brings together bespoke agentic AI and vertical SaaS platforms, offering many advantages:

1. Out-of-the-box domain expertise

Industry and functional knowledge embedded in mature platforms is reflected in preconfigured KPIs, predefined industry- and company-specific workflows and ontology coded with domain-specific data models and ready-to-use business rules, processes and scalable regulatory compliance structures. It’s also reflected in prebuilt AI agents that integrate into these workflows and can be used out-of-the-box or triggered via user actions.

When organizations build custom systems, they must recreate this domain knowledge from scratch. Even with AI assistance, that process often requires years of iteration, testing and refinement.

Purpose-built platforms that offer these capabilities out of the gate can function as a combined system of record and workflow engine, enabling organizations to quickly execute on this knowledge, then use agentic AI to build custom, organization-specific capabilities.

2. Increased speed to value and lower total cost of ownership

With preconfigured infrastructure and integrations, purpose-built platforms are designed for rapid deployment. Additionally, by distributing development costs across many customers, platforms deliver capabilities that are prohibitively expensive or risky for an organization to successfully build independently.

That’s because custom-built solutions typically require organizations to construct multiple foundational layers before receiving any business value. This includes building a data foundation, integration pipelines, KPIs, governance workflows and security and regulatory frameworks.

Further, custom platforms require significant upfront investment to establish engineering teams, cloud infrastructure, data management frameworks, security and compliance controls, as well as continuously funding maintenance and upgrades.

These expenses accumulate over time, not just in operating costs, but also in talent flux.

Instead of laying out large capital investments and risking attrition, organizations can adopt a consumption-based approach with a purpose-built platform and choose services aligned with budgets and internal resources, then scale use and AI-driven customization as needed. This model offers reduced upfront costs, predictable operating expenses, faster realization of value and lower operational risk.

3. Continuous innovation for rapid scalability

Unlike custom platforms that require coordination among multiple internal teams stretched across other priorities, purpose-built platforms are maintained by dedicated product teams focused on consistently improving the platform. These teams monitor industry trends, stay up to date with emerging technologies and regulatory changes as they occur and implement best practices across customers.

As a result, platforms evolve constantly, delivering new capabilities, prebuilt integrations, agentic accelerators and other improvements to all users autonomously. They also incorporate configurable frameworks that allow organizations to adapt to regional requirements, such as GDPR and country-specific data privacy regulations.

Many custom-built solutions struggle with this as they are designed for a single region or use case. As life sciences organizations grow, their systems must scale across multiple business units, global geographies, diverse regulatory environments and increasing data volumes, requiring major architectural changes.

Over time, internally developed platforms can stagnate as resources shift toward other initiatives and solutions. This can create compliance risks, duplicate or outdated tech stacks, operational inefficiencies and increased maintenance costs. Many organizations also fail to retire systems that no longer provide business value, resulting in duplication of tools. This results in a growing gap between the organization’s capabilities and pace of technological change, which can create a negative impact on their bottom line.

Platforms mitigate these challenges by providing standardized architecture designed to operate on a global scale with innovation embedded directly into the product life cycle. With this capability, life sciences organizations can confidently build custom agents on top of these systems without having to train them from scratch on regional or data privacy requirements.

4. Product-driven over people-driven

Ultimately, the buy-versus-build debate reflects two fundamentally different operating models, both of which have evolved with the maturation of agentic AI.

In the platform, product-driven model, organizations adopt standardized technology built around industry best practices for a wider variety of organizations, rather than individual, in-house engineering projects. With agentic AI, these platforms have delivered further advancements in accelerated deployment, continuous upgrades, domain expertise, global scalability and lower operational overhead.

In a custom-built, people model, development relies heavily on internal teams to design, build and maintain technology systems. This approach offers flexibility but comes with significant dependencies on specialized skills and ongoing resource investment. While agentic AI has enabled these teams to accelerate development, organizations that choose to build their own solutions still face higher operational complexity, slower innovation cycles and increased maintenance burden.

Across industries, organizations are increasingly shifting toward platform-driven operating models they can enhance with custom AI builds to keep pace with technological change and reduce dependency on internal engineering teams.

5. A hybrid approach for operational efficiency

Agentic AI will undoubtedly transform how enterprises operate, but it won’t be replacing platforms. Instead it will be enhancing them with autonomous or semiautonomous agents that can interpret goals, plan actions and execute across systems.

Agentic AI works on top of these platforms to automate and optimize decision processes and orchestrate complex business workflows and systems critical for regulatory compliance (HIPAA, GxP, SOX), providing data integrity and audit trails.

Without a structured foundation, custom-built AI agents often struggle with data inconsistency, governance issues, lack of domain context and operational reliability.

Hybrid solutions enable enterprises to reap the benefit of “best-of-both-worlds” and accelerate the path to true innovation for building unique differentiation as well as automating complex workflows.

The most effective architecture will combine both elements where enterprise-grade platforms provide the backbone and agentic AI provides the intelligence and automation layer. Together, they create a more powerful ecosystem than either approach alone. Rather than rebuilding these capabilities from scratch, life sciences organizations can leverage them as a foundation for innovation.

Building on the right foundation

In the age of AI, the strategic goal is no longer simply buy versus build. Instead, the real opportunity lies in choosing the right platform foundation and using AI to extend its capabilities. Organizations that adopt this approach will be better positioned to move faster, innovate more effectively and focus their resources on what truly differentiates them.

In the end, AI may change how software is created—but it does not change the value of expertise. And expertise, when embedded into purpose-built platforms, becomes one of the most powerful accelerators of enterprise transformation.