Executive Summary

In the rapidly evolving biotechnology sector, Chief Data Officers (CDOs) face a critical decision: build AI capabilities in-house or buy (or partner for) commercial AI solutions. This choice fundamentally affects time-to-value, costs, risk, and strategic focus. Our analysis finds that hybrid approaches are increasingly favored: start by “buying” to achieve quick wins and validate use cases, then build custom systems where proprietary competitive advantage truly requires it (^[1]) (^[2]). Key factors include:

• Time and Speed: Off-the-shelf AI tools can be deployed in weeks or months, whereas building internally often takes years (^[3]) (^[4]). Early adoption of proven solutions generates momentum and avoids “pilot purgatory” (^[5]) (^[6]).
• Costs: Building involves substantial up-front and ongoing expenses — e.g., specialist salaries ($200k–$500k per expert (^[7])), GPU infrastructure ($0.2–2M/yr (^[7])), data engineering (20–40% of budget (^[7])), compliance overhead, etc. Buying carries license/subscription fees, but externalizing many development costs. For instance, one analysis estimates in-house AI projects as requiring $500K–2M up front and 30–40% of that annually, versus ~$50K–200K and 10–20% for commercial platforms (Table 1) (^[8]).
• Integration and Data Challenges: Biotech data are highly heterogeneous and siloed. Reports note that data often reside in “isolated, non-interoperable silos” across varied formats (^[9]). Building requires integrating such data under one architecture, a non-trivial engineering effort. Even top AI platforms admit “no universal solution” can address all needs due to cost and integration complexity (^[10]). Vendors must offer robust connectors and APIs to existing systems (ERP, LIMS, eTMF, etc.) (^[11]).
• Expertise and Talent: Developing biotech AI internally demands rare ML expertise. Industry experts warn that “developing AI solutions internally requires rare, highly specialized talent” (^[12]); hiring and retaining such talent is expensive and competitive. Commercial AI vendors already employ dedicated experts, effectively outsourcing much of this burden (^[12]).
• Regulatory and Security: Biotech applications are heavily regulated (FDA, GxP, etc.). Leading vendors often have built-in compliance and audit support (^[13]) (^[14]), including documentation for validation. Still, any solution (build or buy) must satisfy stringent data integrity, encryption, and traceability standards. For example, nearly 40% of healthcare leaders cite regulatory compliance as a key barrier to AI adoption (^[15]).
• Strategic Focus: CDOs should align AI decisions with strategic priorities. General-purpose or low-differentiation functions (e.g. administrative data extraction, routine analytics) can often be bought, whereas core R&D innovation (e.g. novel molecular design models) might justify custom builds (^[16]) (^[2]). Notably, Recursion Pharmaceuticals – an AI-driven biotech – pursues both: it has built a powerful in-house platform while raising ~$450M through partnerships, leveraging vendors for capabilities beyond its internal scope (^[17]).
• Outcome Potential: The stakes are high. Biotech firms are pouring resources into AI (one report found 74% of biopharma respondents have deployed AI in R&D (^[18])), and the potential returns are profound. For example, a PwC Strategy& analysis projects that broad AI adoption could double pharma operating profit by 2030, unlocking an additional ~$254 billion globally (^[19]). Realizing this requires making informed build-vs-buy choices.

This report provides an in-depth framework for CDOs to evaluate vendor options, calculate build costs, and manage integration complexity. We present cost breakdowns, vendor-evaluation criteria, case studies, and strategic recommendations. Each claim is supported by data, industry analysis, and expert insight.

Introduction and Background

The biotechnology and pharmaceutical industries are data-intensive and highly regulated sectors undergoing digital transformation. The pressures are immense: R&D productivity has stagnated even as R&D spending soared (e.g. from ~$194B in 2019 to $301B in 2023 (^[20])); personalized medicine and genomics generate diverse data streams; and competition demands speed-to-market. Meanwhile, AI capabilities have advanced rapidly. In 2023, generative AI investment in pharma grew from ~$160M in 2022 to an estimated $2.25B (^[21]), reflecting the belief that AI can revolutionize discovery, development, and operations. This rise is not mere hype – studies show AI can markedly improve outcomes (e.g., 70% of analyzed clinical trial recruitment improvements cited gains, or patient screening accuracy jumps (^[22])).

In this context, Chief Data Officers (CDOs) and IT leaders in biotech face a classic dilemma made urgent by AI: to build custom AI solutions internally, or to buy (or partner with) external AI technologies. This mirrors historical debates in pharma – for example, whether to construct dedicated manufacturing facilities or outsource to Contract Manufacturing Organizations (^[23]). </current_article_content>But AI brings unique factors: it is a fast-moving field, dependent on curated data and specialized skill sets, with regulatory implications and rapidly changing platforms.

Experience in other sectors offers guidance. Tech companies often do both – using third-party AI services for non-core tasks while developing proprietary innovations internally (^[24]). Financial services similarly distinguishes “core” versus “context” technologies, buying the latter and focusing in-house development on proprietary differentiators (^[25]). In biotech, this principle translates to concentrating internal efforts on unique scientific insights or analytics (e.g. novel drug-target predictors) and leveraging external solutions for commodity needs (e.g. text mining of literature, administrative workflow automation).

However, biotech’s data complexity and compliance requirements mean AI adoption is not plug-and-play. Integrating a new AI tool may entail connecting to electronic lab notebooks, LIMS (Laboratory Info Mgmt Systems), EHRs, imaging pipelines, and manufacturing QA systems – all under FDA/EMA oversight. Thus, understanding vendor capabilities, total cost of ownership (TCO), and system integration burdens is critical. CDOs must evaluate cross-cutting factors: alignment with strategic goals, cost and ROI, technical fit, regulatory adherence, and organizational readiness.

This report explores these dimensions in detail. It draws on industry analyses, case studies, and research literature to provide a framework for build-vs-buy decisions in biotech AI. We examine vendor evaluation criteria, break down build costs and hidden overheads, illustrate integration challenges with examples, and highlight future trends. Throughout, we emphasize evidence-based insights, citing empirical data and expert commentary.

Next, we discuss key factors in the build-vs-buy choice, followed by dedicated sections on vendor selection, cost analysis, integration complexity, and practical case examples. We conclude with implications and recommendations for biotech CDOs seeking to maximize the value of AI.

Build vs. Buy: Key Considerations

When an organization considers an AI solution, the decision is not simply “build vs buy” in isolation but a strategic choice impacting nearly every aspect of operations. Important dimensions include:

• Time-to-Value: How quickly must the AI deliver impact? Buying off-the-shelf solutions offers “speed to value” – organizations can deploy and show ROI rapidly (^[5]). Building custom solutions typically takes much longer; large-scale AI projects can “take years” to implement and may be obsolete by launch (^[3]). In early phases, quick wins are crucial to maintain support. Industry panels advise, “Buy first. Deliver something visible, fast, and safe. Then decide if it’s worth building” (^[5]). Empirical evidence supports this: teams achieving “quick AI wins” in year one are twice as likely to succeed long-term compared to teams that invest heavily in custom platforms from the start (^[26]).

• Scope of Customization: Assess how unique your requirements are. If the AI task involves highly specialized biotech data (e.g. custom imaging modalities, proprietary omics data, specialized phenotypic assays), out-of-box models may need substantial tuning or custom features (^[27]) (^[28]). In contrast, generic data tasks (text mining, standard lab analytics, well-known ML tasks) can often be addressed with less customization, favoring bought solutions. Pre-trained models and platforms excel at “fast deployment” with minimal setup (^[27]), but lack the flexibility to incorporate proprietary models or hypotheses. If domain nuances are critical, an internal build or combined approach may be better (^[28]) (^[2]).

• Technology Maturity and Integration: Off-the-shelf AI tools are increasingly sophisticated and API-driven. The distinction between “build” and “buy” has blurred into composing systems of bought and custom components (^[29]). A modern AI architecture might mix cloud APIs (e.g. NLP or vision APIs), open-source models, and internal data pipelines. Gartner (2024) reports ~65% of enterprises now use hybrid AI architectures (^[1]). The question becomes: where to leverage ready-made blocks, and where to innovate.

• Strategic Control and IP: Building in-house confers full control over models and data, potentially protecting intellectual property and trade secrets. Conversely, buying sometimes entails vendor lock-in or sharing limited data. Vendors typically govern the model and update roadmaps, while in-house teams can tailor future development. If data privacy or IP is paramount, a build or private deployment may be preferred. However, vendors in regulated domains often offer strict data governance (private cloud instances, on-premise options) to alleviate these concerns (^[30]).

• Resource and Talent Availability: Skilled AI personnel are in high demand. Hiring a team of experienced data scientists, ML engineers, and MLOps specialists can be expensive and competitive. Industry experts note that “developing AI solutions internally requires rare, highly specialized talent” (^[12]), costing far more than simply licensing a solution. Vendors effectively package specialized talent as part of their offering. Thus, staffing readiness is a key factor. If the necessary expertise is lacking, buying or partnering may be a pragmatic first step.

• Financial Impact (TCO and ROI): The choice is fundamentally a capital allocation decision. CFOs emphasize evaluating Total Cost of Ownership (TCO) over a multi-year horizon, including direct costs (salaries, hardware, software) and hidden costs (maintenance, upgrades, compliance, change management) (^[31]) (^[7]). Both models require investment, but with different profiles. Table 1 (below) contrasts typical costs and risks by approach. In general, building demands higher up-front and maintenance investment, while buying shifts costs to subscription and vendor management. The ultimate metric is risk-adjusted ROI: which path yields the most net benefit after accounting for opportunity cost and uncertainties (^[32]) (^[33]).

Table 1. Comparison of build vs buy (and an intermediate partnership) from a cost and risk standpoint (^[8]). Custom development incurs higher upfront and running costs but offers maximum control; commercial solutions are lower-cost and faster but with vendor dependency.

• Scalability and Maintenance: AI systems require continuous upkeep (model retraining, monitoring, scalability). A vendor’s SaaS solution may automatically handle upgrades and scaling, whereas an in-house system requires building a full DevOps/MLOps stack (^[34]) (^[35]). As Sheikh Sharjeel notes, hidden costs like CI/CD pipelines, orchestration, security, and governance can dominate the TCO after 9–12 months (^[34]) (^[33]).

• Organizational Fit and Change Management: Built solutions need organizational adoption (new workflows, training) that can slow ROI. Commercial tools often come with user support and pre-built user interfaces, reducing training overhead (^[36]). For example, a ready AI platform may integrate into existing collaboration tools (Slack, Teams) and include adoption analytics, whereas a custom build requires building these components (^[36]).

In summary, the build-vs-buy decision is nuanced in biotech. It should align with strategic priorities: owns core competency vs. outsources commodity tasks. As one expert advises, “keep your focus internal on what provides true competitive differentiation while leveraging external solutions for broader applications” (^[16]). The following sections elaborate on these factors – starting with vendor evaluation criteria, then dissecting build costs and integration complexities, and illustrating with case examples.

Vendor Evaluation: Criteria and Checklist

For biotechnology organizations choosing to buy or partner for AI, rigorous vendor evaluation is essential. The market is flooded with AI vendors, but not all meet the stringent needs of pharma/biotech. Key criteria include:

• Domain Expertise and Industry Fit: The vendor must deeply understand biotech and pharmaceutical contexts. A generic AI provider may not account for GxP compliance, clinical terminology, or lab workflows. Evaluate whether the vendor has relevant case studies or pilot projects in life sciences (^[37]). Ideally, their team should include pharma/biotech experts who can “speak your language” (^[37]). For instance, if evaluating an AI clinical-trial tool, the vendor should be able to discuss concepts like study protocols, enrollment, or sample blinding fluently. Scenario-based demos or Q&As (e.g. “How does your system handle audit trails in manufacturing data?”) can test their familiarity (^[37]). In short, choose vendors with proven success in biotech: a vendor “that speaks your language” and aligns with your domain is far more likely to deliver value (^[37]).

• Regulatory Compliance and Validation Support: Any AI touching regulated processes must support compliance (FDA21 CFR Part 11, EMA GxP, etc.). Vendors should provide built-in features like secure audit trails, data integrity, user authentication, e-signatures, and explain how their system fits into validated environments (^[14]). Ask for documentation: sample validation plans, test scripts, and development lifecycle records. Good vendors will supply a “validation package” and even help with Computer System Validation (CSV) by providing pre-written protocols (^[14]). Crucially, inquire how model updates are handled: if the AI model changes, how are customers notified and systems re-validated? A quality vendor will articulate how they maintain algorithmic stability (e.g. showing bias tests, accuracy benchmarks) and will offer clear processes for version control (^[14]). If a vendor hesitates at compliance details (“GxP?”), it is a red flag. In short, the vendor should be a partner in compliance, not a burden.

• Data Security and Privacy: Biotech data can be highly sensitive (patient data, proprietary research). Rigorously evaluate a vendor’s security model: Is their solution cloud-based or on-premises? If cloud, is it multi-tenant or dedicated? Ensure they offer end-to-end encryption (data at rest/in transit) and strong access controls (SSO, role-based permissions) (^[30]). Ask explicitly about data handling: Will your data ever be used to train generic models for unrelated customers? Many vendors may aggregate anonymized usage data to improve models, which may be unacceptable for confidential R&D data. Ensure they comply with relevant privacy laws: are they willing to sign a HIPAA Business Associate Agreement (for PHI) or GDPR data processing addendums? Check for security certifications (ISO 27001, SOC 2 Type II) as evidence of rigorous practices (^[30]). Finally, consider data sovereignty: can they guarantee that data stays within required jurisdictions? Any sign of lax security is grounds to eliminate a vendor.

• Technical Architecture and Integration: The AI tool must integrate into your existing IT landscape. Ask whether the vendor provides open APIs or connectors for your core systems (ERP like SAP, popular LIMS such as LabWare or Benchling, clinical systems like Veeva Vault for eTMF, etc.) (^[11]). Good integration capability is vital to avoid creating new silos. If a tool is a standalone web app with no hooks, it will impede workflows and user adoption. Vendors should support relevant data standards (HL7/FHIR for clinical data, ISO IDMP for product data, etc.) where applicable, ensuring interoperability.

Evaluate scalability: can the solution handle your data volumes, concurrent users, and data types? For example, if the vendor has only processed small R&D datasets but your organization has petabytes of genomic or imaging data, you need assurance they can scale (perhaps via cloud-native or distributed processing) (^[11]). Performance is critical – test the system with representative loads. Even a great algorithm is unusable if it takes hours to process what users expect in minutes. Many vendors offer sandbox trials; use these to stress-test integration and latency. Also ask about customization: can your data science team extend the models? Some platforms allow custom model uploads or plugin architectures; others are black-box SaaS. Decide based on whether you plan to fine-tune or deploy your own models in the tool.

• Functional Performance and Accuracy: Beyond architecture, the tool must work effectively on your use case. Request performance metrics and benchmarks in contexts akin to your needs. For predictive models, ask about accuracy, precision/recall, or area-under-curve statistics on validation data. If a vendor claims 95% accuracy (e.g. in extracting safety signals), find out how that metric was measured and, if possible, conduct a pilot on your data. Ideally, vendors will support proof-of-concept trials so you can evaluate performance in situ. Also, consider how they monitor performance over time: do they provide dashboards showing model drift or error rates? A strong vendor will advocate for continuous monitoring and periodic retraining. Importantly, clarify error handling: if the AI is uncertain on an input, does it defer to humans or confidently mis-predict? Projects in pharma need cautious “human-in-the-loop” overrides to avoid costly mistakes. Define Service Level Agreements (SLAs) not just for uptime, but for AI performance (e.g. false positive/negative rates).

• Support and Service: Gauge the vendor’s commitment. Evaluate SLAs for availability and support. Does the vendor train your staff and hand-hold through deployment? How fast will they fix issues or roll out custom feature requests? The quality of ongoing support is often as important as initial functionality. References and reviews from other biotech clients can be invaluable here.

In sum, vendor evaluation must be comprehensive. Table 2 (below) summarizes typical criteria and considerations:

Table 2. Key criteria for evaluating AI vendors in biotechnology. Vendors must address not only functional fit but also domain alignment and regulatory needs (^[37]) (^[11]).

Using this checklist helps ensure you select solutions that align with biotech’s stringent requirements. Combined with cost and strategic analysis (discussed next), it forms a complete decision framework for CDOs.

Build Costs and Total Cost of Ownership

Building AI capabilities in-house carries substantial costs that extend well beyond initial development. CDOs must consider Total Cost of Ownership (TCO), including one-time and ongoing expenses. Key cost components include:

• Personnel and Expertise: The largest single cost is usually talent. Experienced data scientists, ML engineers, MLOps engineers, and domain experts command high salaries. Estimates suggest total compensation for such specialists can be $200,000–500,000 per year each, accounting for base salary, benefits, and equity (^[7]). A capable in-house AI team might require multiple such roles (data scientists, ML engineers, DevOps), quickly putting salary costs in the low seven figures annually. Recruiting and retention require further overhead. By contrast, buying shifts this cost effectively into the vendor’s own staffing.

• Infrastructure (Compute and Cloud): Training and running biotech AI models demands heavy compute. High-end GPUs or HPC clusters are required, especially for deep learning on large datasets (e.g. protein structures, imaging, genomics). Infrastructure can range from <$200K/year for modest GPU clusters to $2M+ annually for enterprise-scale, high-availability compute (^[7]). Cloud computing adds usage costs: for example, renting an NVIDIA H100 GPU ranges from $0.58 to $8.54 per hour (~$5K–$75K on-demand per year) (^[38]). Training a large language model is especially pricey; estimates suggest training GPT-3 cost on the order of $4–12 million (^[39]). While biotech companies seldom train models of GPT-3’s scale from scratch, these figures illustrate the potential scale of compute investment. On-going inference also accrues cost: one study noted large models sometimes spend 10–20× more on inference than on training (^[40]).

• Data Engineering and Integration: Raw biotech data is complex. Engineering pipelines to ingest, clean, normalize, and integrate multi-modal data (genomic, imaging, sensor, historical) is costly. Reports estimate data engineering can consume 25–40% of total AI spend (^[7]). This includes building ETL processes, data warehouses or lakes, metadata management, and ensuring data quality. Debugging pipelines and creating gold-standard labels (e.g. manually annotating microscopy images or curating EHR data) adds labor. The “integration complexity” premium is often cited as a 2–3× multiplier on implementation effort (^[41]), since connecting heterogeneous systems and adhering to standards (CDISC, HL7, ISO IDMP) requires custom connectors and validation.

• Software Development and Platform: The “hidden” devOps stack must be built or licensed. For a custom AI platform, one must develop or acquire components like orchestration layers, data storage (vector databases, knowledge graphs), APIs, event queuing, CI/CD pipelines, monitoring/observability, secrets management, and infrastructure-as-code for automation (^[34]). Pre-built AI orchestration platforms (if building) can mitigate this, but costs remain. Alternatively, buying a platform shifts many of these costs to the vendor. In CFO lingo, one counts these under Platform & Infrastructure costs (^[34]).

• Model Development and Maintenance: Designing, training, and iterating ML models is labor-intensive. Initial development includes selecting algorithms, feature engineering, and hyperparameter tuning. After deployment, models degrade (drift) and must be retrained or replaced, incurring ongoing effort estimated at 15–30% of initial development cost per year (^[42]). Adapting to new data or migrating to upgraded model architectures also adds to the horizon cost.

• Compliance and Quality Assurance: In biotech, compliance is not a one-time check but an ongoing process. Implementing data governance, audit trails, validation tests, and documentation can be substantial. One analysis notes regulatory/compliance aspects can amount to an effective “up to ~7% revenue penalty risk” if neglected (^[43]). Investing in GxP-ready data pipelines, traceability, and audit-ready models is required, and any gaps can cause costly delays (e.g. rework after an FDA audit).

• Change Management and Training: Rolling out new AI tools requires training users and possibly developing new UIs or embeds (e.g. integrating into Slack or lab portals). These are non-negligible costs. A CFO-focused framework categorizes this under Change Management & Adoption costs (^[36]). Build solutions often lack pre-built integrations, so every adoption path (mobile alerts, dashboards) must be custom. Buying a solution may reduce this overhead (if it comes with standard integration) but still requires change management.

• Opportunity Cost and Absorbed Risk: Perhaps less tangible is the cost of capital and risk. In one CFO’s terms, any AI dollar carries a “risk premium” (model errors, security lapses, failed projects) (^[44]). Over-engineering a build that doesn’t pan out can mean sunk costs. Conversely, vendor solutions carry vendor risk (lock-in, discontinuation) which also has cost.

Summarizing these components, analysts often break down AI TCO into buckets as shown in Table 3. This highlights that infrastructure and talent dominate initial spend, with continuous overhead for maintenance and compliance.

Table 3. Major cost components of building AI systems in-house (^[7]). Note how infrastructure, data engineering, and specialist salaries form the bulk of investment, with significant ongoing overhead for maintenance and compliance.

To put this in perspective, even a modest internal AI project quickly reaches into the millions of dollars:

• Hiring a small AI team (2–3 data scientists + 1 ML engineer + 1 data engineer) costs easily $1M/year in salaries/overheads alone.
• Provisioning high-end GPUs for training (or using equivalent cloud compute) can add hundreds of thousands more annually.
• Initial platform development (assembling pipelines, APIs, dashboards) can cost $500K+ if done right.
• Each retraining cycle or major update adds incremental cost.

In contrast, a comparable commercial solution might require a few hundred thousand dollars upfront and a smaller subscription fee, while leaving much of the engineering and model upkeep to the vendor. This is reflected in industry analyses: one report suggests custom development TCO is much higher than using vendor tools (Table 1 above) (^[8]).

Cost-Benefit Analysis: The decision ultimately hinges on whether the added value of a custom build outweighs these costs. For differentiating AI capabilities – those that directly affect drug discovery success or form part of a patentable process – building may be justified despite the expense. For ordinary analytics (reporting, anomaly detection, literature review) – it usually is not. CFOs emphasize calculating risk-adjusted ROI: for example, if a build delays time-to-market by a year, the lost revenue may exceed any future OPEX savings. TCO modeling for 2-3 year horizons, including risk premiums (e.g. cost of possible delays and compliance fines), is recommended (^[31]) (^[44]).

In practice, many organizations find a blended approach yields the best ROI: they prove value quickly with bought solutions, then selectively invest in key custom modules that provide competitive differentiation. As one biotech advisory notes: “The most practical approach lies somewhere in between” – start with pre-trained tools for speed and ease of integration, then gradually build custom components for your proprietary data and expertise (^[2]). This phased strategy balances short-term cost and long-term value.

Integration Complexity in Biotech

One of the most daunting aspects of adopting AI in biotech is integration: bringing AI tools into the existing ecosystem of lab and IT systems, data repositories, and workflows. Unlike consumer sectors, biotech environments suffer from:

• Heterogeneous Data Sources: Biotech R&D involves myriad data types: genomic sequences, proteomic profiles, microscopy images, clinical trial records, chemical structures, etc. These data often reside in disparate systems (sequencing labs, animal studies, clinical trial databases, manufacturing logs). One analysis notes that data are “often scattered in isolated, non-interoperable silos” with a mixture of structured and unstructured formats (^[9]). For example, high-throughput sequencing data might sit in a bioinformatics server, lab instrument metadata in a LIMS, and literature insights in a document repository. Unifying such data to feed an AI model requires creating robust ETL pipelines and semantic mappings (ontology alignment, unit conversions, etc.). Poor data quality (missing values, inconsistent standards) further complicates this integration.

• Evolving Platforms and Standards: The tech stack is constantly changing. No single solution fits all needs: a “universal” platform is impractical given the pace of AI and biotech innovation (^[10]). As one expert observes, even if a perfect solution were built, “not every platform could simultaneously implement it due to cost and integration complexity. A steady state would never be reached due to ongoing modernization cycles” (^[10]). This means integration is an ongoing process: every time a new data type or regulatory requirement appears, the pipelines must be updated.

• Regulatory Interoperability: For regulated activities, systems must maintain audit trails and data lineage end-to-end. Custom integration must not break validation. For example, if an AI model uses lab results, the pipeline must ensure those results remain traceable to the original raw data. Integrating a vendor tool often involves certifying the interface too, adding another layer of validation.

• Legacy Systems: Many biotech companies have legacy software (older LIMS, ERP, on-prem databases) not designed with modern APIs. AI tools often expect cloud-native JSON/REST interfaces; bridging these to legacy platforms can require middleware. For instance, connecting an AI-driven image analysis pipeline to an FDA-compliant QMS might entail exporting data via spreadsheets, with all the risk of errors. Each integration point is a potential bottleneck.

• Scale and Performance: Some AI use cases (e.g. training on millions of microscopy images) require data to be moved and processed at scale. Traditional WAN links or local networks may be insufficient, necessitating data replication or co-location of compute. Integration here involves not just software, but also network and hardware planning.

Given these factors, CDOs often find that data integration and governance are as critical as the AI models themselves. In a 2025 industry survey, nearly half of healthcare respondents identified data quality and integration as a major barrier to AI adoption (^[15]). Indeed, 74% of biopharma respondents had AI in R&D, but that success hinges on robust back-end integration.

A telling summary from a federated-learning study emphasizes the challenge: “Data is often scattered across siloed systems, and security and privacy concerns complicate its effective use”, but federated approaches allow collaboration without moving raw data (^[45]) (^[46]). However, federation itself requires integration at the level of model parameters, which is complex. The baseline message is that integration can multiply costs: one analysis estimates it as a “2–3× implementation premium” (^[43]).

Integration Strategy: To manage complexity, organizations should:

• Adopt Flexible Platforms: Prefer AI solutions with open standards and modular architecture. Ensure any chosen vendor or framework has hooks for custom integration.
• Incremental Deployment: Begin with pilot integrations on a single data domain to validate the approach before scaling to others.
• Data Governance: Invest in master data management, ontologies, and quality controls upfront. Clean, well-documented data simplifies integration.
• Cloud and Hybrid models: Many biotech firms use hybrid architectures (training in the cloud, inference on-prem, or vice-versa) to balance scale with control (^[47]). Integration must plan for data movement in either direction.
• MLOps Pipelines: Adopt modern MLOps practices (continuous integration of models, automated deployments) to handle the iterative integration of new models and data.

In practice, successful AI integration often means re-architecting parts of the IT stack. For example, a company might establish a central data lake in the cloud, aggregating lab and clinical data, which becomes the input point for AI projects. Alternatively, containers and microservices can encapsulate AI models to plug into existing apps. But each path comes with its own complexity.

Integration vs. Vendor Selection: Note that the choice of build vs buy also affects integration. Buying a best-of-breed AI tool may reduce development effort, but still requires integration work. The vendor’s ability to integrate (as discussed in the evaluation section) is a key selection criterion (^[11]). On the build side, having internal knowledge of the systems can theoretically simplify integration (an in-house team can write custom connectors), but only if the team has the capacity to manage it.

Given these challenges, integration complexity is often a tie-breaker in the build-vs-buy decision. If a vendor’s solution seamlessly plugs into your ecosystem (e.g. it is delivered as a native module of your existing LIMS or runs in the same cloud environment), the “buy” option gains appeal. Conversely, if integration effort negates the time gain from buying, building or partnering may make more sense.

Case Studies and Real-World Examples

Examining how leading biotech and pharma companies have approached build vs buy decisions provides practical insights. Here are a few illustrative examples:

• Bristol Myers Squibb (BMS): BMS’s Chief Digital & Technology Officer Greg Meyers publicly emphasizes re-building internal technical capabilities. After a prior era of outsourcing, BMS “put the ‘T’ back in IT,” doubling their tech headcount to reduce dependency on third parties (^[48]). This reflects a strategic choice to build in-house: Meyers notes they wanted to be “creators” rather than “service brokers” (^[48]). BMS has indeed invested heavily in internal AI and data platforms (for example, an internal “AI factory” program). Yet BMS also actively partners with AI vendors. For instance, in 2022 BMS contracted with PathAI, a specialized pathology AI startup, for translational research in oncology, immunology, and fibrosis (^[49]). It also partnered with ConcertAI to enhance real-world evidence capabilities (^[50]). This exemplifies a hybrid model: BMS builds broad AI competencies internally but buys niche solutions for specific problems.

• Recursion Pharmaceuticals: Recursion is a biotech founded on the premise of AI-driven discovery. Its executives report that the company always pursued two parallel strategies: building a comprehensive internal platform and engaging partners for areas outside their core scope (^[17]). The CFO noted Recursion has raised ~$450M through partnerships while also “advancing a really high-value internal pipeline” (^[17]). Technically, Recursion’s platform ingests multi-omic and high-content imaging data at scale; they even disclosed achieving FDA IND clearance on a novel target that “would have been undiscoverable without the AI technology” they developed (^[51]). This underscores how an AI-first biotech can justify heavy internal investment. Recursion’s story also shows that building an in-house system enabled insights (“end-to-end system” evolution (^[52])) that are hard to replicate externally. Yet even Recursion collaborates with large pharma, offering its platform in partnerships where big companies lacked AI capability.

• Large Pharma Partnerships: Several big pharmas illustrate the “buy” side through strategic alliances. For example, GlaxoSmithKline (GSK) has multiple AI collaborations: originally it invested ~$43M with AI drug-design firm Exscientia; more recently (2025) GSK expanded deals to include next-gen platforms (news emerged of up to £43M collaboration for multiple targets (^[53])). GSK’s approach suggests leveraging external AI expertise for drug discovery acceleration rather than building all machinery internally. Similarly, Roche has partnered for AI-powered pathology: in Feb 2024 Roche entered an exclusive collaboration with PathAI to co-develop AI-assisted companion diagnostics for cancer (^[54]). In another example, Novartis has collaborated with Microsoft and NVIDIA to develop AI drug discovery capabilities (using NVIDIA’s computing and Azure’s AI), while also pursuing its in-house digital drug design projects. These cases highlight that large companies often buy or partner for specialized AI, particularly in early experiment or pilot stages.

• CDMOs and Service Providers: Contract Development and Manufacturing Organizations (CDMOs) in biotech are also adopting AI to offer services. For instance, some large CDMOs acquire process simulation or quality control AI tools rather than build them, as integration into many clients’ supply chains is eased by buying established solutions. This trend further pushes innovator companies to consider outsourcing even their analytics needs.

• Academic and Biotech Startups: Academic labs and small biotech firms often lack the resources to build full AI stacks. They may use cloud-based AI tools or collaborate with computational service providers. For example, a university pharma lab might use cloud-based genomic analysis tools (from Illumina, DNAnexus, etc.) and open-source models rather than building custom pipelines. These cases typically favor “buy” (or open-source) due to resource constraints, only building custom models as regionally supported initiatives (like intramural computing cores) allow.

Lessons Learned: Across these examples, patterns emerge:

• Hybrid Strategy: Both BMS and Recursion illustrate that even companies committed to building use external solutions for niche needs. Conversely, those focused on buying (like GSK, Roche for certain projects) still invest in in-house data platforms. This suggests that framing the decision as a strict choice is less useful than planning a portfolio of build and buy.

• Scale Matters: Larger organizations tend to have more in-house capacity (more staff, budget) and thus more inclination to build. Smaller players lean towards buying or partnering out of necessity. However, even giants acknowledge vendor value; smaller firms often grow by becoming the vendor.

• Project Type: Early exploratory work (e.g. evaluating if AI can predict protein targets) is often done with existing tools or contract research. Once a project is proven, companies may then build dedicated solutions, especially if the scale justifies it.

• Risk Management: Many companies treat initial vendor engagements as low-risk experiments. For instance, IBM’s Watson for Drug Discovery (a commercial platform) was used by several pharma as a proving ground; when expectations didn’t pan out, some projects were ended, and lessons learned informed internal strategy.

• Speed vs. Ownership: In fast-moving crises (e.g. COVID-19 vaccine development), pharma did build enormous internal capacity but also heavily licensed and partnered (see Moderna’s urgency deals, though not AI-specific). This shows that speed sometimes trumps ownership concerns.

These cases underline the importance of evaluating organizational context. A custom-built mega-LLM for drug design may be justified if it becomes a company’s competitive edge (as with Recursion’s RBM39 example (^[51])), but most routine tasks are better served by proven external solutions.

Implications and Future Directions

Looking ahead, the implications of the “build vs buy” decision extend beyond immediate project ROI. CDOs must consider:

• Long-Term Evolution: Technology evolves quickly. Today’s cutting-edge AI (e.g. large language models, graph neural networks) may be commodity tomorrow. Thus, organizations should build infrastructure and skills that can adapt. For example, if you buy a third-party model today, ensure you have the ability to later replace it with an improved model, possibly open-source, to avoid perpetual vendor lock-in. Conversely, if building, consider using modular architectures so components can be swapped when better algorithms emerge.

• Regulatory Landscape: Regulatory bodies are catching up with AI. New guidelines (e.g. FDA’s proposed AI/ML framework, EU AI Act) will impose rules on model validation, transparency, and risk management. Vendors may evolve to incorporate these natively, making compliance easier. In-house builds will need to align engineering processes with these emerging regulations, potentially increasing integration complexity.

• Data Strategy: The central asset for biotech AI is data. CDOs must ensure robust data governance. Future trends like federated learning (see below) and synthetic data generation could help overcome privacy/multi-party data sharing issues (^[55]) (^[56]). Investing in high-quality annotated datasets now will pay dividends when scaling AI later.

• AI Talent Development: Regardless of build or buy, organizations must develop some internal ML competency. Training life science researchers to partner with data scientists, or hiring “unicorn” staff who understand biology and AI, is critical. This ensures better communication with vendors/partners and that custom models align with scientific needs. As one Wheels CIO summarized for commercial shipping, “Our unicorns are people who understand the business well enough… and have the technical chops to get it done” (^[57]). The same applies in biotech.

• Collaborative Ecosystem: Many future AI platforms are ecosystem-driven. GenAI co-pilots specifically for pharma are beginning to appear (e.g. “AI co-pilots” trained on biomedical literature). CDOs should watch moves like Owkin’s “K Pro” platform or NVIDIA’s efforts – these may become new third-party options. Partnerships with cloud providers or consortia may also offer new ways to build (e.g. shared cloud infrastructure for genomics AI).

• Return on Investment: Strategic monitoring of ROI will be a continuous task. As one report notes, the most successful adopters embed AI in an enterprise roadmap aligned with business goals (^[58]). CDOs should maintain metrics (cost savings, research throughput, time-to-insight) and be prepared to pivot: if an in-house solution underperforms a commercial alternative, it may be wise to revise the strategy.

• Innovation Adoption Curve: The biotech sector is still relatively early in AI adoption. While many companies have pilot projects, only a few have fully integrated AI into core workflows. In coming years, as tools mature, the balance may shift. We may see standardization of certain AI-as-a-service offerings in biotech, reducing the need for custom builds for common tasks. Conversely, differentiation may come from proprietary data and models, keeping build relevant for unique R&D edges.

• Ethical and Societal Factors: With AI come concerns over explainability, bias, and impact on jobs. Biotech CDOs must consider whether buying black-box models meets ethical standards or if in-house builds (with full transparency) might be preferable. Public trust in biotech is sensitive; errors from AI in a clinical context can have outsized repercussions. This may influence decisions on how much control to retain.

• Case Law and Liability: As AI-driven decisions proliferate (e.g. AI suggesting therapy targets or manufacturing changes), questions of liability emerge. If a bought AI tool leads to a wrong decision, who is accountable? Legal frameworks are unsettled. In-house solutions might allow a company to take direct responsibility (and adjust policies accordingly), whereas vendor tools might complicate accountability. These considerations might push CDOs toward more in-house checks for critical processes, even if the initial model is bought.

• Innovation vs Maintenance: Over time, maintenance of AI systems will become significant. If buying, firms must plan for “vendor risk”: what if a vendor discontinues a product or goes out of business? Plans to migrate or replace must be in place. If building, firms face “technical debt”: frameworks evolve and code needs refactoring. In either case, a long-view life-cycle management strategy is needed.

Conclusion

The “build vs buy” decision in AI is not a one-time choice but a strategic framework guiding how a biotech organization commits resources and shapes its data strategy. Our analysis shows that neither extreme is universally right. Instead, CDOs should align the decision with corporate strategy:

• For generic, non-differentiating AI capabilities, buying (or partnering) is usually best: it delivers speed, leverages external expertise, and often comes packaged with compliance and support. A quick deployment can validate use cases and accrue early ROI (^[5]) (^[6]).

• For core, high-value AI capabilities that underpin proprietary science, investing in building can pay off: it offers complete control, deep customization, and the possibility of unique IP. This is justified only if the expected benefits (e.g. faster drug discovery on a critical pipeline) significantly outweigh the high costs and longer timelines (^[16]) (^[2]).

• Integration and Data Readiness are common to both paths: successful AI requires integrated, high-quality data and robust pipelines. CDOs must set realistic expectations: even bought solutions need integration work, and built solutions still rely on external models or datasets (open-source or commercial) in many cases.

• A staged, hybrid approach is wise: carve out specific, immediate projects for vendor solutions to prove value, and simultaneously develop an internal roadmap for longer-term, strategic builds. Use vendor partnerships as learning opportunities, bringing valuable lessons into your own teams.

• Measure Everything: Use clear metrics (time saved, cost avoided, success rates) and feedback loops to judge each project’s build/buy balance. Enterprise-level oversight is crucial: as observed in surveys, organizations with an enterprise AI roadmap outperform those with isolated pilots (^[58]).

Finally, building an AI capability is itself an iterative journey. Success stories show that leaders continuously adapt: for example, BMS reversed years of outsourcing by doubling tech staff (^[48]), while Recursion leveraged both internal innovation and external funding (^[17]). As AI technology advances (e.g. more capable biological LLMs, federated platforms), the trade-offs will evolve. CDOs must keep abreast of both industry solutions and internal needs.

In conclusion, biotechnology firms stand at a pivotal crossroads. Those that accurately assess costs, rigorously vet vendors, and thoughtfully integrate AI will be positioned to harness its transformative power. By designing a balanced AI strategy – mixing purchase and development in the right proportions – CDOs can accelerate innovation while controlling risk. This report provides a thorough framework and evidence-based insights to guide that journey.

Sources: All claims in this report are supported by industry studies, expert analyses, and use cases, as cited above (^[12]) (^[15]) (^[48]) (^[31]) (^[1]) (^[7]) (^[37]) (^[19]) (URLs in references). The reader is encouraged to consult the cited literature for further detail.