For pharmaceutical companies, choosing an enterprise AI platform is no longer simply an IT procurement decision. It is increasingly a question of how much AI infrastructure, governance, model management, and operational risk an organization is prepared to own.
AI use across drug development is expanding, and the U.S. FDA says it has seen more than 500 submissions with AI components between 2016 and 2023, spanning areas including nonclinical, clinical, postmarketing, manufacturing, digital health technologies, and real-world data analytics. As adoption grows, CTOs and AI leaders face a practical choice: build an internal AI stack from the ground up or license an enterprise AI platform designed to provide much of that infrastructure.
There is no universal answer. Building can make sense when a company has highly specialised requirements, deep internal engineering capabilities, and a long enough time horizon to justify infrastructure investment. Buying can make more sense when the priority is getting AI into production without turning the IT organisation into an AI infrastructure company.
The important question is therefore not simply build or buy?
It is:
Which parts of the AI infrastructure should your organisation own, and which parts are better provided by a purpose-built platform?
1. Why This Decision Is Landing on Pharma Leaders' Desks Now
Many pharmaceutical companies have already moved beyond AI experimentation.
Individual teams may have built literature summarisation tools, internal copilots, knowledge assistants, clinical research workflows, or prototype applications using large language models. The challenge appears when several of these pilots need to move into enterprise deployment.
A prototype can work with a small dataset and a handful of users.
Production AI is different.
It needs:
Secure data access
Identity and permissions
Model orchestration
Data ingestion pipelines
Knowledge management
Monitoring
Evaluation
Auditability
Governance
Deployment controls
Ongoing maintenance
The FDA's current AI guidance reinforces why this matters in life sciences. Its 2025 draft guidance for AI used to support regulatory decision-making proposes a risk-based credibility assessment tied to the specific context of use. FDA and EMA's good AI practice principles also emphasise human-centric design, risk-based approaches, data governance, documentation, performance assessment, and lifecycle management.
For CTOs, this changes the economics of the decision.
The organization is not deciding whether it wants an LLM.
It is deciding whether it wants to operate the infrastructure around AI for years to come.
2. Reframing the Question: Who Owns the Infrastructure Risk?
Build-versus-buy discussions often focus on control.
Building appears to offer maximum control because the company owns the code, infrastructure, integrations, and deployment environment.
But control also creates responsibility.
If you build the stack internally, your organization may become responsible for maintaining:
Data pipelines
Model APIs
Retrieval systems
Knowledge stores
Evaluation frameworks
Security controls
Access management
Model upgrades
Monitoring
Governance workflows
Disaster recovery
Performance testing
The real question is therefore not whether you can build an AI system.
Most large pharmaceutical companies can.
The question is whether building the underlying infrastructure is strategically valuable enough to justify owning its full lifecycle.
NIST's AI Risk Management Framework provides a useful way to think about this problem. Its framework organizes AI risk management around Govern, Map, Measure, and Manage, rather than treating AI risk as a one-time technical assessment.
That lifecycle perspective is particularly relevant to pharma.
AI infrastructure is not finished when the first application goes live.
It continues to require governance, evaluation, maintenance, and change management.
3. The Hidden Costs of Building In-House
The initial engineering estimate for an internal AI platform can be misleading.
A team might estimate the cost of building an ingestion pipeline, connecting an LLM, creating a retrieval layer, and deploying an internal interface.
But these are only the starting components.
Data engineering becomes a permanent responsibility
Pharma organizations work with highly fragmented information.
Scientific publications, clinical trial data, regulatory documents, internal research, commercial intelligence, and proprietary knowledge may exist in completely different systems.
Connecting these sources requires ongoing engineering work.
The challenge is not simply ingesting data once. Sources change. Schemas change. APIs change. Permissions change. New data sources are added.
An internal team must continue maintaining those connections.
Model orchestration creates another layer
If the application depends on one model provider, the architecture can become closely tied to that provider's APIs, pricing, capabilities, and release cycle.
If the organization wants flexibility across multiple models, engineering teams must build the abstraction and orchestration layer themselves.
That means handling model routing, authentication, versioning, testing, fallback behaviour, and performance differences.
Validation does not end after deployment
Pharma organizations need to consider how AI systems perform as models, prompts, datasets, and workflows change.
The FDA's good AI practice principles specifically highlight risk-based performance assessment and lifecycle management.
That means the internal platform team may eventually become responsible for maintaining evaluation frameworks and documenting changes across the AI lifecycle.
Maintenance is the largest hidden commitment
An AI platform that works today may not work identically six months from now.
Models evolve.
Security requirements evolve.
Data sources evolve.
Enterprise policies evolve.
User expectations evolve.
The cost of building AI infrastructure therefore includes not only development but also operating the infrastructure as a long-term product.
4. The Hidden Costs of Buying the Wrong Platform
Buying is not automatically easier.
A poorly selected platform can introduce a different set of risks.
Vendor lock-in
A platform that only supports one model provider can create long-term dependency.
If the model's economics, performance, availability, or capabilities change, the organization may have limited options.
Limited deployment control
Some SaaS AI products require sensitive information to leave the organization's controlled environment.
That may not be acceptable for proprietary research, regulated information, confidential strategy, or other sensitive knowledge.
Rigid architecture
A platform may work well for today's use case but become difficult to adapt when the organization wants to connect new data sources or develop new AI applications.
Fragmented AI adoption
If every department purchases a different AI tool, the organization can end up with multiple knowledge stores, separate permissions, different models, and inconsistent governance practices.
The result is the opposite of what enterprise AI is supposed to achieve.
Instead of creating a shared intelligence layer, the organization creates another technology silo.
5. What Should You Evaluate Before Buying an Enterprise AI Platform?
A serious procurement process should look beyond the product demonstration.
CTOs and AI leaders should evaluate the underlying architecture.
Architecture transparency
Ask what sits underneath the interface.
Where is data stored?
How is knowledge structured?
How are models connected?
How are sources updated?
How are permissions enforced?
How are outputs traced back to evidence?
A polished interface is not enough for enterprise deployment.
Multi-model flexibility
An LLM-agnostic AI platform can reduce dependence on a single model provider.
This matters because the model landscape is changing rapidly.
A company may want to use different models for different workloads based on performance, cost, security, latency, or deployment requirements.
The underlying knowledge and application architecture should ideally remain stable even when the model changes.
Deployment control
For AI infrastructure for regulated industries, deployment architecture is a critical evaluation criterion.
Organizations should determine whether the platform supports environments such as:
Private cloud
VPC
On-premises infrastructure
Sovereign cloud
Isolated environments
The right architecture depends on the organization's security, data residency, regulatory, and operational requirements.
Knowledge architecture
Do not evaluate only the LLM.
Ask how the platform manages enterprise knowledge.
A pharmaceutical organization may need to connect documents, studies, products, experts, regulatory information, clinical evidence, and internal knowledge.
The knowledge layer can ultimately be more strategically important than the model itself.
Figure: What to evaluate before buying an enterprise AI platform.
6. Where KnolForge Fits Into the Build-vs-Buy Decision
For organisations considering whether to build an enterprise AI infrastructure stack internally or license one, KnolForge is an enterprise knowledge and AI memory platform designed to provide the knowledge layer behind AI applications. Instead of requiring teams to build and maintain every part of this layer themselves, KnolForge provides the infrastructure needed to connect enterprise knowledge with different AI models and applications.
This distinction is important because the knowledge layer sits between an organisation's data and the AI systems that use it. As AI models continue to evolve, enterprises may want to change the models they use without rebuilding the underlying knowledge infrastructure.
KnolForge is designed with this flexibility in mind. It can connect with multiple AI models, including GPT, Claude, Mistral, Llama, and proprietary models, allowing the underlying enterprise knowledge layer to remain independent of a single model provider.
For CTOs and technology leaders, this LLM-agnostic architecture can reduce the need to rebuild enterprise AI infrastructure whenever the organisation changes its preferred model or adds a new one.
KnolForge also supports deployment across VPC, on-premises, and sovereign-cloud environments, giving organisations greater control over where their knowledge infrastructure operates and how it is governed.
For a pharma organisation evaluating private AI deployment enterprise requirements, this can be particularly relevant. Rather than choosing between building the entire knowledge infrastructure internally and relying on a generic external AI service, organisations can use a dedicated enterprise knowledge layer while retaining control over their environment.
The result is a more flexible approach to the build-vs-buy decision: organisations can avoid building every foundational component from scratch while maintaining the deployment, model and knowledge controls required for enterprise AI use cases.
7. A Practical Build-vs-Buy Decision Framework
Rather than debating build versus buy at a high level, score the decision against the organization's actual circumstances.
1. Data sensitivity
Ask:
How sensitive is the data?
Does it contain proprietary research?
Are there regulatory or contractual restrictions?
Does the platform support the required deployment model?
Higher sensitivity increases the importance of deployment and data controls.
2. Timeline
Ask:
Do we need production AI in six months?
Is this a multi-year infrastructure programme?
How quickly must new use cases reach production?
If speed matters, building every foundational component internally can create unnecessary delays.
3. Internal AI talent
Consider whether the organization has enough people to maintain the complete stack.
Do you have specialists in:
AI engineering?
Data engineering?
Cloud infrastructure?
Security?
Model evaluation?
MLOps?
Knowledge engineering?
Having AI researchers is not necessarily the same as having an enterprise AI infrastructure team.
4. Regulatory exposure
Consider the consequences of AI failure.
An internal research assistant and an AI system supporting a regulated workflow may require very different governance.
The FDA's risk-based approach makes context of use particularly important when AI contributes to regulatory decision-making.
5. Strategic differentiation
Finally, ask the most important question:
Is the infrastructure itself a competitive advantage?
If the answer is no, building every infrastructure component internally may not be the best use of scarce engineering resources.
9. The Better Question May Be What You Should Build
The strongest strategy may not be purely build or purely buy.
Pharma companies can own the components that differentiate their AI strategy while licensing the infrastructure that does not.
For example, an organization might choose to own:
Proprietary datasets
Scientific workflows
Domain-specific applications
Internal governance policies
Unique models
Business logic
While using a platform for:
Knowledge infrastructure
Model orchestration
Data ingestion
Deployment infrastructure
Enterprise controls
AI application foundations
This approach can preserve strategic control without forcing internal teams to recreate every piece of AI infrastructure.
It also allows technology leadership to focus engineering resources on the areas where proprietary development actually creates differentiation.
Conclusion: Don't Build AI Infrastructure Just Because You Can
The build-versus-buy decision in pharma is ultimately a question of where engineering effort creates strategic value.
If your organization has a genuine need for proprietary infrastructure, the talent to operate it, and a clear reason to own the entire stack, building may be justified.
But if the objective is to deploy secure, governed, flexible AI across the enterprise, the economics deserve closer examination.
The cost of building is not the first version.
It is the years of data engineering, model orchestration, validation, security, upgrades, monitoring, governance, and maintenance that follow.
And the cost of buying is not the license.
It is the architectural limitations, vendor dependency, deployment restrictions, and data-control issues that can follow a poor platform decision.
That is why pharma technology leaders should consider a third path: buy the foundational AI infrastructure and build the differentiated intelligence on top of it.
A platform such as KnolForge is designed around that model: an enterprise knowledge layer that can operate within controlled environments, connect to different AI models, and provide the infrastructure for building AI-powered applications without forcing organizations to repeatedly rebuild the foundation.
The more important question for a CTO is therefore not “Can our team build this?”
It is:
“Is building this infrastructure where we want our scarce AI engineering resources to go?”
For many pharmaceutical organizations, that question can change the entire build-versus-buy discussion.
If your team is already evaluating AI infrastructure, deployment models, or enterprise knowledge architecture, Pienomial’s KnolForge provides a platform designed to support these requirements without forcing enterprises to build every capability from scratch. The next step is to assess which capabilities you genuinely need to own and which can be provided through a platform built for the enterprise environment.
