Workforce Development for Generative AI in Life Sciences (original) (raw)

Upskilling the Life Sciences Workforce for Generative AI Adoption

[Revised February 1, 2026]

Generative AI (e.g. ChatGPT, Google Gemini, Claude) is rapidly transforming pharma and biotech—and 2026 marks a pivotal year as the industry shifts from experimentation to enterprise-scale deployment. The AI in pharmaceutical market is expected to grow from 1.94billionin2025to1.94 billion in 2025 to 1.94billionin2025to16.49 billion by 2034, with a CAGR of 27%. McKinsey Global Institute estimates GenAI could unlock $60–110 billion in value annually for life sciences by accelerating discovery, trials, regulatory processes and marketing [1]. Of this total, $18–30 billion is attributed to commercial functions alone, and an additional $4–7 billion specifically to biopharmaceutical operations [2]. Leading companies now view GenAI as a strategic imperative. According to a KPMG poll of more than 100 life sciences CEOs in late 2025, 76% felt their organizations were moving at the right pace to handle the speed of AI developments. To move beyond pilots into enterprise-scale use, life-science organizations need a structured, step-by-step approach: align leadership and governance, identify high-impact use cases by function, build the right technology and data infrastructure, drive culture change with training and upskilling, and ensure ethical and compliant deployment. The following guide, drawn from recent industry reports and case studies, outlines this path in detail, with concrete examples and best practices.

1. Strategy and Governance: Align Leadership and Build Capacity

Successful GenAI adoption begins at the top. Executive sponsorship and a clear vision are essential. Establish a cross-functional GenAI Center of Excellence (CoE) or council that unifies experts from R&D, regulatory, IT, compliance and business operations under strong leadership [3] [4]. This central body sets strategy and standards while decentralized business units pilot innovations. For example, Indegene recommends a hybrid operating model: a centralized CoE drives innovation and sets policies (e.g. data governance, security, responsible AI guidelines), while domain teams embed approved AI tools into their workflows [3] [5]. Leadership must articulate measurable goals (e.g. “30% faster protocol drafting” or “40% reduction in document review time”) and hold stakeholders accountable [6].

Key governance pillars include:

• Strategic alignment: Balance “responsible AI” deployment with domain innovation. Ensure GenAI projects support business objectives (e.g. faster time-to-market, compliance) rather than operate in silos [3] [7].
• Executive sponsorship: Secure C-suite buy-in. Leaders should sponsor use cases, communicate the vision broadly, and support necessary investment in data, tools, and training [6] [8].
• Compliance oversight: Set up AI governance councils to review AI use cases for safety and regulatory compliance. Define policies on data privacy, intellectual property, and quality standards, in line with GDPR, HIPAA and new rules like the EU AI Act [7] [9]. For example, Indegene notes that as regulatory standards (GDPR, HIPAA, EU AI Act) gain prominence, robust security and Responsible AI frameworks become critical [7].

By designating accountability and governance structures early, organizations create the foundation to scale GenAI safely. Indegene emphasizes that talent development ("AI fluency") is a fourth pillar: investing in people and skills is as important as technology [10] [11].

The Agentic AI Shift (2026): Looking ahead, 2026 is being declared "the year of the agent" across the life sciences industry. Agentic AI—autonomous systems capable of reasoning, planning, and executing entire complex workflows—enables pharma companies to deploy a digital workforce of highly knowledgeable companions to support human workers in every function. According to PharmaVoice, "If 2025 was the year of embedding AI across pharmaceutical organizations, 2026 will be the year its role shifts from analysis to action." Leadership must prepare governance structures to accommodate these autonomous agents while maintaining human oversight.

2. Identify and Prioritize Use Cases Across Functions

Next, map GenAI use cases to each function’s highest-impact processes. This ensures focus on “low-hanging fruit” with clear ROI and avoids scattershot pilots. Common life-science functions and example use cases include:

• Research & Discovery: Literature review and knowledge synthesis. Scientists can use LLMs to scan and summarize vast literature, identify new target-disease links, and even generate hypotheses. For instance, AstraZeneca built an internal "AZ ChatGPT" research assistant that lets chemists query decades of proprietary data ("What do we know about Target X in oncology?") and get synthesized insights far faster than manual search [12]. Domain-specific models like BioGPT (achieving human parity on PubMedQA benchmarks) and newer alternatives such as Deep Intelligent Pharma are now outperforming early models by up to 18% in R&D automation tasks. Drug design: Generative models also aid molecular design by proposing novel compound structures based on learned chemistry patterns (e.g. GENTRL, ChemBERTa) [13]. In 2026, agentic AI agents autonomously screen millions of molecules, analyze structures, predict efficacy and toxicity, and design novel drug compounds—significantly shortening discovery timelines [14].
• Preclinical & Medical Affairs: Scientific writing and content generation. GenAI can draft sections of study reports or regulatory documents. For example, Indegene notes that medical-affairs teams at top pharma used GenAI to fact-validate promotional claims and create Standard Response Documents, cutting review time by ~60% [15]. Pharmacovigilance groups have used AI to produce first drafts of Periodic Safety Update Reports (PSURs), reducing submission time by over 20 days [16].
• Clinical Operations: Protocol development and patient selection. AI tools are optimizing trial protocols and design. In practice, GenAI platforms have cut protocol amendments by ~40% and boosted enrollment by ~25% [17]. Teams have asked ChatGPT-like models to draft informed-consent forms, patient recruitment letters, and monitoring plans. Eli Lilly, for instance, pilots ChatGPT to draft study protocols and informed-consent sections which experts then refine, greatly accelerating what was a manual task [18]. AI can also analyze patient databases to stratify cohorts or predict responses, improving trial outcomes [19]. Looking ahead to 2026: Multi-agent trial copilots are emerging—one major pharmaceutical company has adopted a supervisor agent that manages specialized agents focused on site activation, subject enrollment, and data management, potentially boosting productivity in data management by 60% while reducing database build timelines from months to under two weeks.
• Regulatory Affairs: Submission drafting and query response. Regulatory teams use GenAI to draft submission modules (e.g. Clinical Overview, CSR sections) and automate routine writing. A leading pharma tested a GenAI solution that searched past Health Authority (HA) queries and drafting patterns, enabling 80% faster response to new queries [20]. Merck’s “GPTeal” is an internal gateway to ChatGPT/LLaMA/Claude that lets reviewers safely generate content (e.g. draft responses to FDA queries) under IT oversight [21] [22]. Such tools help reduce repetitive paperwork, leaving experts to focus on strategic compliance checks.
• Medical Writing & Publishing: CSRs, publications, grant proposals. Generative AI can produce first drafts of lengthy documents. Merck reported scientists using ChatGPT (via GPTeal) to draft email updates, memos, and even clinical study report sections [22]. Because LLMs can hallucinate or “invent” data, human review is mandatory – but having a draft saves weeks of labor. An Applied Clinical Trials article notes GenAI’s potential to automate Clinical Study Reports (CSRs), speeding TTM for new therapies, provided data and document standards are “ready” for AI use [23] [24].
• Commercial & Marketing: Content creation and personalization. Marketing teams leverage GenAI for digital ads, emails, web copy, and sales collateral. Pfizer’s internal GPT “Charlie” generates draft promotional material and flags compliance issues in real time [25] [26]. Content reuse is tagged “green” for fast approval, while new claims get a “red” flag for human review – a built-in compliance guardrail. Indegene also cites companies creating dynamic, localized marketing videos with AI, cutting production costs by ~40% and doubling speed-to-market [27].
• Manufacturing & Supply Chain: Process documentation and troubleshooting. AI can draft SOPs or maintenance guidelines by summarizing best practices. For example, Moderna extended its GenAI tools beyond R&D: manufacturing teams use GPT assistants to troubleshoot process documents, and legal teams use them to summarize regulations [28]. By treating AI as a "virtual coworker," Moderna reports faster resolution of manufacturing questions and better knowledge sharing [29]. Pfizer's manufacturing success: Pfizer used AI/ML to optimize the manufacturing of Paxlovid by analyzing supply chain data and dynamically adjusting process parameters, which reduced the cycle time of a critical production step by 67% and enabled 20,000 extra doses per batch.
• Human Resources & Administration: Internal communications and training. Even non-scientific departments benefit. Novartis deployed a “NovaGPT” branded ChatGPT for HR, drafting policy documents, company announcements, and job descriptions [30] [31]. This cut writing time dramatically – often only a few AI-generated sentences are kept and polished, boosting efficiency while maintaining quality [31] [32]. Such pilot use cases build familiarity with AI before rolling it out to core functions.

In practice, each organization must tailor its use-case list. A McKinsey study found that beyond marketing and discovery, AI is maturing in supply-chain forecasting, pharmacovigilance, and medical affairs [8] [9]. Companies should inventory processes and prioritize those with high volume or cognitive burden (e.g. repetitive writing, complex data search) [33]. Use frameworks (like Indegene’s ROI matrix) to score use cases on business value, strategic fit and feasibility [10] [7].

With strategy and use cases defined, invest in the right technology stack. Key considerations include:

• Choice of models: Public cloud LLMs (ChatGPT, Gemini, Claude, LLaMA, etc.) versus on-prem or private models. Many life-science companies use ChatGPT Enterprise (HIPAA/GxP-compliant version) for broad needs, as Moderna did [29]. Others deploy guarded interfaces: Merck's GPTeal wraps ChatGPT, LLaMA and Claude in a secure portal so company data never leaks to external models [34]. Domain-specific models have evolved significantly: while Microsoft's BioGPT (trained on PubMed) remains a benchmark, 2025 analysis shows that domain-specific instruction tuning now enables fine-tuned 8B LLaMA-based models to outperform GPT-3.5 (now succeeded by GPT-5 mini) by 46.5% on chemistry tasks. AI-generated code: About 60% of code in recent pharma product releases was AI-generated or AI-assisted, with industry leaders projecting more than 95% of code will be AI-produced within five years—making governance of generated code as important as the code itself. Evaluate models for scientific language handling, citation ability, and privacy.
• Data and Knowledge Integration: Generative AI is most powerful when connected to your data. Use retrieval-augmented generation (RAG) or knowledge graphs to ground LLMs in internal databases (clinical results, medical literature, SOP libraries). AstraZeneca’s AZ-ChatGPT, for example, taps decades of experimental data to answer queries [12]. Ensure data is standardized (CDISC SDTM for trials, etc.) so AI can retrieve and cite it correctly. As one guideline notes, data readiness (standardized formats and tagged content) is a prerequisite for trustworthy AI outputs [24].
• Development Platforms: Provide an AI "sandbox" or workbench for teams to experiment. This may be Jupyter notebooks with LLM APIs, integrated tools (e.g. Semantic Scholar with AI plugins for literature search), or low-code platforms. Encourage developers to build GPT assistants (custom chatbots) for specific tasks. Moderna's creation of 750+ custom GPTs (e.g. a "Dose ID GPT" for trial dosing analytics) shows the potential of agile development on enterprise AI APIs. According to OpenAI's case study, within two months of adopting ChatGPT Enterprise, 40% of Moderna users were creating GPTs, with every user averaging 120 conversations per week. Their Contract Companion GPT delivers "90 to 95% time gain" on contract review, serving as a universal company-wide asset [29].
• Security and Compliance Controls: Work with IT and security to ensure encryption, logging and monitoring. Use enterprise-grade offerings (OpenAI Enterprise, Google Vertex AI, Azure OpenAI) which offer data controls. Implement prompt filters to block sensitive data. Audit AI use: for regulated documents, maintain versioning and “chain-of-custody” records of AI-generated content. Indegene emphasizes “strong data security protocols” as core to GenAI initiatives [35].

In summary, treat GenAI tools as you would any critical IT system: integrate them with existing workflows, validate outputs, and ensure there are human oversight steps. Adopt multi-modal capabilities (text, images, even protein folding) where relevant – e.g. Google's Med-Gemini for radiology or Google's Bio-Gemini for text may open new channels, but these too must be validated in the lab context. Preparing for agentic AI: As the industry moves toward autonomous agents in 2026, ensure your infrastructure supports multi-agent orchestration. McKinsey research shows that nearly 60% of research and early drug discovery workflows will require custom-built agents, which can free up 21–30% of capacity in wet labs, data analytics, and regulatory support. For most tasks, however, text-based LLMs tied to life-science data will continue to drive immediate benefit.

4. Culture Change, Training and Upskilling

Technology alone isn’t enough; the human factor is often the rate-limiter. Many pilot projects fail to stick because end-users lack trust, skills, or clarity on how to use AI [36] [37]. A Wipro analysis concludes the main challenges in GenAI adoption are “not model selection or infrastructure – they are human” [36]. To overcome this:

• Communicate Clearly: Frame GenAI as an augmentation of human work, not a replacement. Emphasize how it automates tedious tasks (e.g. first-draft writing, data sifting) so staff can focus on higher-value analysis and decisions. Involve users early: gather input from lab scientists, clinicians, and regulators on pain points and involve them in designing AI tools. Wipro advises “co-design solutions with the teams who will use them daily” [33]. When employees feel included, they are likelier to embrace new workflows.
• Rapid Training Programs: Launch company-wide AI literacy initiatives. Johnson & Johnson ran mandatory GenAI training: over 56,000 employees completed courses on ChatGPT and prompt engineering, and 14,000 participated in six-week bootcamps for 37,000 training hours [38]. Pfizer's AI Festival: In late October 2025, Pfizer held its second annual AI Festival Week, gathering colleagues globally (virtually and in-person) to reaffirm the company's commitment to AI innovation. Teams should learn both the potentials and pitfalls of AI: how to craft effective prompts, how to verify facts, and what constitutes sensitive data. Provide hands-on workshops and office hours with AI experts. Some orgs require AI certification for managers or role-based AI competence badges. The skills gap challenge: A FiercePharma analysis notes that "technological advancement has outstripped the ability of traditional training programs to keep up"—companies that establish "digital academies" and cross-disciplinary learning programs now will be better positioned for the agentic AI era.
• Governance and Guidelines: Publish internal guidelines on acceptable use. Lilly’s CIO succinctly told staff: “Use ChatGPT for work, but _never input anything you don’t want to get out_” [39]. Educate teams about compliance (e.g. “don’t feed PHI or proprietary research into public chatbots”). Like Merck’s example, ensure that any new AI tool or GPT assistant is approved and secured before use.
• Embed AI Tools in Daily Work: Provide easy access to approved AI assistants. Moderna's experience shows that broad adoption follows once tools are made available. After securing an enterprise ChatGPT instance, over 80% of Moderna employees began using it for daily tasks, turning AI into "extensions of our team" [40]. Moderna's legal team achieved 100% adoption of ChatGPT Enterprise, with their Chief Legal Officer noting it "lets us focus our time and attention on those matters that are truly driving an impact for patients" [41]. Celebrate early wins (e.g. "AI Sunday" productivity stories), and share metrics on time saved to motivate adoption.
• Continuous Feedback and Iteration: Maintain communication channels (Slack, Yammer, regular town halls) for users to report issues and suggest improvements. Indegene notes that “regular feedback loops” and performance reviews help AI initiatives adapt to emerging challenges [42]. Quickly address any misinformation or bias flagged by users.

In sum, treat upskilling as a core pillar of your GenAI strategy [10] [11]. By investing in people (training, AI champions, cross-functional councils) and framing AI as a team effort, organizations can achieve sustained adoption. The hybrid workforce future:PwC's 2026 pharma outlook emphasizes that companies must invest in creating the "hybrid workforce of the future," capturing the best of human potential and agentic AI. Job roles, performance metrics, and career paths will need to be redesigned around adaptability and outcomes. As one leader put it, building a "culture of AI" is just as important as the technology itself.

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5. Pilot, Measure ROI, and Scale Up

With use cases selected and teams trained, run proofs-of-concept (PoCs) to validate impact. Start small, then expand what works. Indegene observes that leading pharma are moving from isolated pilots into production. For example, pilots in medical writing, literature review and content generation have shown enough value that companies are now scaling these solutions [43].

Key steps:

• Define Success Metrics: Before each pilot, set clear KPIs. Metrics might include time saved (e.g. hours per report), error reduction (e.g. QA edits per document), or business outcomes (e.g. enrollment rates, submission speed). Indegene suggests tracking tangible outputs (reduced manual review time, higher compliance scores) to “showcase cost and time savings” [10] [7].
• Ensure Human-in-the-Loop: Initially use GenAI as a co-pilot. For instance, have writers draft with ChatGPT and then edit, or have scientists vet AI-generated hypotheses. The goal is to build trust: as accuracy is proven, you can gradually automate more. Merck’s policy is that all AI-drafted clinical reports are reviewed by experts before submission [44] – a model of cautious scaling.
• Iterate Quickly: If a pilot underdelivers, refine the prompt, expand data sources, or adjust the model. Common pitfalls like hallucinations or irrelevant output can often be solved with prompt tuning or adding context. For example, linking ChatGPT to internal documents (GPTeal, AZ ChatGPT) greatly improved result relevance [21] [45].
• Quantify the Benefit: Once a pilot yields positive results, quantify ROI and build the business case for rollout. Indegene’s ROI framework advises valuing AI by cost savings (e.g. FTEs), speed improvements, quality gains and risk mitigation [10] [7]. For example, reducing regulatory response time by 80% translates directly to faster approvals [20]. Compile case studies internally to demonstrate value to skeptics.

After success, scale up by extending the AI tool to other teams or sites. Moderna provides a textbook example: after the “mChat” pilot, they rolled ChatGPT Enterprise and ~750 personalized GPTs out company-wide, covering R&D, manufacturing, legal, and commercial [40] [28]. Similarly, Pfizer gradually expanded its “Charlie” marketing assistant across regions once it proved 5x faster content creation [25].

Throughout scaling, maintain governance: only certified/trained employees should have access, and audits should ensure compliance. Keep refining the CoE’s playbook with lessons learned. Indegene underscores that scaling requires a value-chain approach (not fragmented labs) and ongoing alignment of AI investments with workflows [35] [7].

6. Ethical, Security and Compliance Considerations

In biotech and pharma, rigorous ethics and compliance cannot be an afterthought. Key guidelines include:

• Data Privacy and Security: Never expose patient PHI or proprietary IP in public LLMs. Use secured enterprise AI platforms or on-premises models for sensitive tasks (as Merck’s GPTeal and Novartis’s internal “NovaGPT” do [34] [30]). Enforce strong encryption, access controls and no-logging features. Train staff on not including identifiable data in prompts. All AI usage must comply with HIPAA (US), GDPR (EU) and company data policies [7] [9].
• Regulatory Guidance: Stay abreast of evolving rules. FDA's January 2025 Draft Guidance: The FDA released its first comprehensive AI guidance for drug development—"Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products"—proposing a risk-based credibility assessment framework with a seven-step process for establishing AI model credibility. AI models where final determinations are made without human intervention are classified as higher risk. The comment deadline was extended to Q1 2026, with final guidance expected Q2 2026. Joint FDA-EMA Principles (January 2026): In a landmark collaboration, the FDA and EMA jointly published ten principles for good AI practice across all phases of medicine development, from early research through clinical trials to manufacturing and safety monitoring. EU AI Act Implementation: The EU AI Act entered into force August 2024, with prohibited practices effective February 2025, GPAI rules effective August 2025, and most high-risk AI provisions (including healthcare) effective August 2026. Medical device and IVD compliance has been extended to August 2028 via the Digital Omnibus proposal. Violations carry penalties up to €35 million or 7% of global revenue. In practice, this means keeping thorough records of AI model versions, training data, and validation outcomes – similar to software validation in GxP. Treat key GenAI tools as regulated systems: maintain SOPs for their use, and include AI outputs in audit trails.
• Accuracy and Reliability: LLMs can “hallucinate” – producing plausible but false statements. In scientific applications this risk is critical. A JMIR study found ChatGPT’s literature search retrieved only ~0.5% relevant studies versus 40% for Bing AI (with a human benchmark of 100%) [46]. Thus, always fact-check AI outputs: verify citations, cross-check facts, and have domain experts edit results. Use LLMs primarily for drafting and ideation, not final content without review. Techniques like prompt engineering to cite sources (or using retrieval) can mitigate hallucination.
• Bias and Fairness: AI models trained on past data may reflect historical biases. In patient stratification or discovery, ensure algorithms are evaluated for bias against any group. Incorporate diverse datasets where possible, and include ethicists/clinicians in reviews of AI-driven decisions.
• Accountability: Define who is responsible for AI-generated work. For example, even if an AI draft is used, the author of the final document is accountable for its content. Document the human-AI workflow: who prompted, who reviewed, and who approved. This accountability is crucial for regulatory scrutiny and legal compliance.

By proactively addressing these considerations, companies not only avoid pitfalls but can gain a competitive edge. As ZS Consulting notes, complying with the AI Act and similar regs "largely mirror" principles of responsible AI that life sciences companies should follow [47]. Industry outlook: The FDA has been actively preparing—in November 2025, they launched an AI Benchbook and internal courses to upskill staff, suggesting the agency aims to expedite future reviews [48]. In effect, early adopters who build "safe and reliable" AI pipelines will establish trust with regulators and patients alike.

7. Summary Table of Key Use Cases

The table below summarizes representative GenAI use cases by function, with industry examples:

This non-exhaustive table illustrates that virtually every life-science function can leverage generative AI in some capacity. Companies should customize this mapping to their specific processes and systems.

8. Change Management and Continuous Learning

Finally, recognize that GenAI adoption is an ongoing journey—and 2026 marks an inflection point. The technology is rapidly evolving from informational to agentic, so embed a culture of continuous learning. Encourage R&D/IT teams to pilot emerging tools (e.g. AI code generators for bioinformatics [52] [53]) and share findings. Industry events to watch: The Bio-IT World Expo (May 2026) will address critical gaps in AI implementation, including integrating AI across R&D functions from target identification through clinical development. The SCOPE Summit (February 2026) focuses specifically on AI in clinical research. Maintain a pulse on regulatory and public sentiment: while some early uncertainties remain about AI in regulated settings [54], the joint FDA-EMA principles published in January 2026 signal growing regulatory acceptance and clarity. Engage with external communities (academic, conferences, alliances) to keep skills sharp.

In summary, the path to upskilling and adopting GenAI in life sciences involves leadership alignment, cross-functional governance, targeted use cases, and rigorous training and ethics practices. By following a structured roadmap—starting from vision to pilot to scale—organizations can safely harness generative AI's power to accelerate innovation and productivity across R&D, clinical, regulatory, and commercial operations. The 2026 imperative: As McKinsey's late-2024 survey found, while all surveyed companies have experimented with gen AI, only 5% have realized it as a competitive differentiator generating consistent financial value. The companies that benefit most from 2026's shift to agentic AI will be those with confident leaders, long-term visions, and robust change-management plans. As one industry report concludes, "the time to move from experimentation to enterprise-scale adoption has arrived" [55], provided companies invest in both technology and people to make AI an enduring part of their workflows.

Sources: Industry whitepapers and case studies from Indegene [56], McKinsey reports [1] [57] [58], expert blogs (Wipro) [59], IntuitionLabs analysis of pharma AI case studies [60], OpenAI case studies [41], regulatory guidance (FDA [61], EMA ema.europa.eu), and industry news (PharmaVoice [62], Pharmaphorum [14], Pharmaceutical Technology [63], Takeda [51], ZS Consulting [47], PwC [64], Medable [50]). These sources provide real-world examples and best practices for deploying generative AI in regulated life-science settings.