1. Life sciences IT priorities, as with all things in life, are not a single category. Suppose a small biotech CIO compares notes with a global pharma IT leader. Both can leave thinking the other is missing the point.

However, they usually are not missing the point. Instead, they are optimizing for different constraints, because they face different life sciences IT challenges.

• A small biotech racing from Phase 2/3 into first launch is constrained by time, staffing, and cash burn. It also needs “just enough” compliance to be inspection-ready.
• A large pharma (Pfizer/Merck/AstraZeneca scale) is constrained by global standardization, pervasive GxP governance, and integration load. “Good enough” in one business unit may still be unacceptable enterprise-wide.

Below is a practical benchmarking lens for the next 12–24 months. It includes two ranked “Top 5” lists of capability priorities (not tool shopping lists). They are anchored in the functions that drive outcomes: R&D, clinical operations, and regulatory affairs. Manufacturing/quality and commercial are secondary here. Still, they get more urgent as launch approaches.

The shared reality: regulators care about outcomes, not your org chart

At both scales, the non-negotiables are essentially the same:

• Data integrity and participant protection (clinical)
• Traceability, controls, and reliable records (GxP)
• Submission quality and timeliness (regulatory)

Regulators operationalize this through inspections and submission standards. For example, FDA’s Bioresearch Monitoring (BIMO) program inspects clinical research. The goal is to assure data integrity and human subject protections. BIMO runs 1000+ inspections annually across multiple compliance programs.
On the submission side, FDA’s eCTD format is the standard for many application types and amendments.

Where life sciences IT priorities diverge is how you get there.

Top 5 life sciences IT capability priorities — Small biotech

Consider a late-stage biotech with less than 200 employees, racing from Phase 2 or 3 into their first commercial launch. The CIO of this small biotech wins by enabling speed-to-decision and inspection/submission readiness. And the CIO must do this with lean teams, heavy outsourcing, and many partner dependencies.

Top 5 (Small Biotech)

1) Inspection-ready clinical documentation and trial oversight

This is the biotech equivalent of keeping the plane in the air while building it.

Clinical operations is where timelines can be won. It is also where they can be quietly lost. Missing documentation, inconsistent processes, or weak oversight can stay hidden for a long time. Then they show up during due diligence, key milestones, or an inspection.

Regulatory expectations keep moving toward risk-based quality management and “quality by design.” This is not just box-checking. ICH E6(R3) modernizes GCP with flexible, risk-based approaches and clearer responsibilities. This is useful for biotech teams. It supports “right-sized rigor,” not bureaucracy for its own sake: ICH E6(R3) Good Clinical Practice (GCP).

A practical implication is that the capability is not “buy an eTMF.” It is:

• documented oversight processes that work with a CRO model
• audit trails and role clarity
• fast retrieval of “show me” evidence (study, site, vendor)

The FDA’s BIMO framing is helpful here. It focuses on monitoring the conduct and reporting of regulated research. It also focuses on the reliability of data submitted in applications.

2) Regulatory submission readiness (and “truth” in dossiers)

Many late-stage biotechs learn this too late. “We have the documents” is not the same as “we can assemble a coherent submission.” It also is not the same as “we can version-control it.”

FDA’s eCTD expectations are explicit about standardized electronic structure.
The capability is less about publishing mechanics. It is more about:

• controlled authoring and versioning
• linkage between clinical, nonclinical, and (soon) CMC narratives
• a “single source of truth” for regulatory commitments and health authority interactions

This is where a RIM + document management backbone becomes a force multiplier. It reduces last-minute reconciliations. It also reduces “which version is real?” meetings.

3) Decision-grade development data foundation

In late-stage biotech, interpret “data foundation” narrowly and pragmatically. It is not an enterprise data lake program. It is the minimum set of capabilities that makes Phase 2/3 and pre-launch decisions defensible:

• reconciliation across clinical ops signals, clinical data, and safety signals
• consistent master data (studies, sites, investigators, compounds)
• integrations that reduce manual spreadsheets and re-keying

This is also where the biotech operating model creates hidden complexity. Trials are outsourced, and clinical trial vendors are numerous. Data arrives from many places. It also arrives with different semantics and quality.

4) Right-sized GxP controls + validation you can sustain

Small biotech IT leaders often feel squeezed between two forces:

• “We need to move fast. Don’t slow us down.”
• “We need to be inspection-ready. Don’t take risks.”

A workable middle is risk-based validation and evidence. It must be proportional and repeatable. FDA’s guidance on Computer Software Assurance (CSA) (finalized in September 2025) targets device production/QS software. Even so, it signals FDA’s preference for risk-based assurance. It also signals a desire to avoid unnecessary documentation burden.

Similarly, FDA’s Part 11 guidance clarifies scope. It emphasizes controls around access, integrity, and reliability. These apply to electronic records used in regulated activities.

The biotech twist is the program’s size. It must be small enough that it does not collapse under its own weight. This matters even more with frequent SaaS releases.

5) Security + identity + partner access that doesn’t slow the business

Late-stage biotech rarely builds everything internally. That increases third-party dependency. Alnylam’s filings, for example, discuss how greater use of cloud technologies increases third-party operational risks. They also note that a cloud provider failure could disrupt operations and expose information.

So the capability focus becomes:

• identity as the control plane (SSO/MFA, least privilege, fast offboarding)
• clean external collaboration patterns (partners, CROs, auditors)
• logging and monitoring that is adequate for GxP and security, not just “IT best effort”

As companies approach commercialization, cybersecurity governance often gets formalized. It can also move to the board level in filings. Alnylam describes board oversight and cybersecurity governance disclosures, for example.

Top 5 life sciences IT capability priorities — Large pharma (global scale)

At large pharma scale, the constraint is rarely speed. The question is not “can we stand up a system quickly?” The question is whether you can run a consistent, validated, secure operating model. It must work across geographies, portfolios, and decades of systems. It also must handle constant partner and acquisition integration.

Top 5 (Large Pharma)

1) Global process standardization with enforceable governance

Large pharma cannot rely on “smart teams doing the right thing.” The organization is too large, too distributed, and too regulated.

AstraZeneca’s disclosures highlight that IT systems enable critical business functions. They also note rising dependence on partner and vendor stability. Data integrity remains central, too. On top of that, companies must comply with data security and privacy laws. That combination drives governance and standardization to the top.

2) Enterprise data + integration foundation (with lineage)

At scale, “data foundation” is not a nice-to-have. It also prevents a permanent tax from one-off integrations. Moreover, it reduces inconsistent definitions across teams and regions.

This is where large pharma patterns show up. Digital and AI platforms are often framed as enterprise productivity levers. Pfizer, for example, describes using digital and AI across the company. It also cites concrete impacts. These include major reductions in computational time and throughput improvements via digital operations approaches in Pfizer’s 2023 annual report.

3) Pervasive GxP governance & validation rigor (end-to-end)

Part 11 and modernized GCP expectations do not disappear at scale. They expand.

Large pharma must show consistent control over electronic records and signatures. It needs documented decisions on where Part 11 applies. It also must document how records are relied upon, consistent with FDA’s Part 11 guidance.
Global adoption timelines for ICH E6(R3) also matter. They reshape expectations across regions. The EMA shows E6(R3) becoming effective in the EU (July 2025) and provides the structured evolution of the guideline.

4) Cyber resilience and identity across a massive ecosystem

Big pharma filings increasingly treat cybersecurity as enterprise risk. They also describe formal governance structures. Pfizer’s 10-K discusses extensive reliance on sophisticated IT systems. This includes cloud services. It also highlights the need to protect large amounts of confidential information. That includes IP and personal information.
Merck’s 10-K disclosures make similar points. They describe measures to protect IT systems and data. They also describe a dedicated CISO-led program in Merck’s 10-K disclosures.

5) Integration factory for partnering and M&A (and divestitures)

Large pharma is in a constant state of integration. That includes new assets, partners, and geographies. It creates an invisible backlog of identity, data, process, and validation work.

Public disclosures often show the impact of acquisitions on operations and financials. Pfizer’s 10-K references the Seagen acquisition in cash flow context, for example. It underscores how significant business development can be.
Large pharma also invests in AI and R&D through deals. AstraZeneca’s announced acquisition of Modella AI (Jan 13, 2026) is a current example of capability-driven expansion.

Why life sciences IT priorities diverge (and why both sides are “right”)

In conclusion, life sciences IT priorities diverge based on company scale. In turn, life sciences CIO’s choice of life sciences software partners will diverge.

1) Speed-to-decision vs. global repeatability

Late-stage biotech needs decisions fast because the runway is finite. Large pharma needs decisions that are repeatable, auditable, and scalable. Those decisions must also satisfy many regulators.

2) Configurable SaaS + managed services vs. platform ownership

Small biotech often wins by composing best-fit SaaS and using managed services. Large pharma often needs deeper platform ownership. At minimum, it needs platform governance to avoid fragmentation.

3) Targeted compliance controls vs. pervasive GxP governance

Biotech can apply GxP rigor to the critical path first. That usually means clinical, regulatory, and quality. It can expand controls as launch nears. Large pharma often has GxP across a wider footprint. Governance becomes a continuous system.

4) Partnering and M&A turn into IT’s hidden backlog

Biotech sees this through alliances. Examples include shared trials, data exchange, and co-development. It also shows up with pre-launch commercial partners. Large pharma sees a constant flow of acquisitions and integrations. Divestitures and vendor ecosystems add to the load.