The Workforce is Ready, but Not Yet Systematically Trained
Across industries, AI adoption is moving faster than most transformation programs. McKinsey’s 2025 workplace report notes that 92 percent of companies plan to increase AI investment over the next three years, but only 1 percent of leaders consider their companies mature in AI deployment. The same report highlights an important leadership gap: employees are often more ready to use AI than leaders believe, and nearly half of employees want more formal AI training as a way to increase adoption.
Feedback gathered across multiple AI workshops conducted with pharmaceutical professionals also reflects similar observations. In a recent AI-in-pharma training cohort, workshop feedback from AI-in-pharma training programs suggests that pharmaceutical professionals are increasingly receptive to structured AI education when it is linked to real GMP workflows rather than presented as a generic technology topic. Participants particularly valued practical sessions on investigation writing, prompt engineering, audit readiness, corrective and preventive actions (CAPA) support, and standard operating procedures (SOP)/document review. This indicates that AI skilling is most useful when employees can clearly connect the technology to day-to-day quality system activities.
From a GMP perspective, the value of such training is not simply improved productivity. More importantly, it helps employees understand the boundaries of appropriate AI use: how to frame questions, how to verify AI-generated outputs against approved source documents, how to avoid unsupported conclusions, how to protect confidential or regulated information, and how to ensure that final decisions remain with qualified personnel. In this sense, AI upskilling acts as a control mechanism. It reduces the risk of casual or uncontrolled AI use and supports a more consistent, scientifically grounded, and review-ready approach to documentation and decision support.
These responses are significant because they come from the operational context where AI will either succeed or fail: deviation handling, CAPA development, SOP interpretation, training management, audit readiness, shop-floor support, and quality decision-making. The enthusiasm is real, but enthusiasm alone is not a control strategy. Pharmaceutical companies need structured AI skilling that teaches employees when to use AI, how to challenge its outputs, how to protect data, and how to keep the qualified human responsible for the final decision.
Why AI Skilling is Different in GMP Environments
In many industries, AI training can focus mainly on productivity: writing faster, summarizing better, preparing presentations, or automating routine analysis. In pharmaceutical manufacturing, those benefits matter, but they are not enough. A GMP user must understand the boundary between assistance and decision-making. AI may help draft a deviation narrative, but it cannot own the investigation. It may suggest potential root causes, but it cannot replace scientific evaluation. It may compare an SOP against a regulation, but it cannot approve procedural adequacy. It may generate a CAPA proposal, but quality leadership must ensure the CAPA is proportionate, effective, and aligned with the actual root cause.
This distinction has already become visible in regulatory communications. In a 2026 US Food and Drug Administration (US FDA) warning letter, the agency criticized inappropriate AI use in pharmaceutical manufacturing where AI-created documents and recommendations were not adequately reviewed by authorized human quality personnel before use. The lesson is not that AI should be avoided. The lesson is that AI must be governed, reviewed, and understood by the people using it.
A useful analogy is the introduction of electronic quality management systems. Companies did not simply install an electronic quality management system and assume compliance would improve. They trained users on workflows, roles, data integrity, audit trails, deviation routing, and procedural expectations. AI requires the same seriousness, but with an added challenge: AI outputs may sound fluent even when they are incomplete, unsupported, or wrong. Therefore, AI literacy must include critical review skills, not only tool usage.
Regulators are Signaling Responsible Adoption, Not Resistance
Recent regulatory signals suggest that responsible AI adoption is becoming part of the future operating model. In May 2026, the US FDA announced expanded internal AI capabilities, including Elsa 4.0 and HALO, a consolidated data platform intended to support workflows across US FDA centers. The US FDA described features such as custom agents, document generation, quantitative data analysis and visualization, secure search, voice-to-text dictation, optical character recognition, and optimized search across large document repositories. Importantly, the US FDA also stated that human subject matter experts are involved at every stage so that inputs, analytical processes, and outputs are verified.
International regulators have also emphasized common principles for AI across the drug product life cycle. The January 2026 “Guiding Principles of Good AI Practice in Drug Development” calls for AI to be human-centric by design, risk-based, aligned with applicable standards including GxP, supported by clear context of use, governed by multidisciplinary expertise, and managed through data governance, performance assessment, life-cycle management, and clear communication of limitations.
For industry, this is a strong signal. Regulators are not standing outside the AI transformation. They are participating in it, while emphasizing oversight, traceability, validation, and human accountability. Pharmaceutical manufacturers should therefore prepare their workforce to meet this standard.
What Pharmaceutical Employees Must Be Trained to Do
An effective AI skilling program should be role-based and practical. A generic two-hour introduction to ChatGPT is not sufficient for a GMP organization. Employees need examples from the work they actually perform.
First, users need AI literacy. They should understand what generative AI is, how large language models produce responses, why hallucinations occur, and why a confident answer is not the same as a verified answer. Second, they need prompt engineering for pharmaceutical operations. A good prompt is not a trick; it is a structured way to provide context, constraints, source material, output format, and review criteria. Third, they need GxP judgment. Users must know which tasks are appropriate for AI assistance, which require documented review, and which should not be delegated to AI. Fourth, they need data protection habits, including what information can be entered into an approved tool, what must be anonymized, and what should never be uploaded to an unapproved environment.
Fifth, teams need training in output verification. AI-generated investigation drafts should be checked against source records, batch documents, laboratory data, equipment logs, environmental monitoring trends, and approved procedures. SOP gap assessments should cite the relevant clauses and distinguish between a true gap, a wording improvement, and an implementation issue. CAPA suggestions should be challenged for root-cause linkage, proportionality, owner feasibility, timelines, and effectiveness measures.
Finally, leaders need to be trained differently from end users. Site heads, quality heads, digital leaders, and functional managers must learn how to select use cases, define context of use, set governance expectations, measure benefits, manage risk, and prevent uncontrolled shadow AI use. Leadership capability is essential because AI adoption is not a software rollout; it is a change in the way knowledge work is performed.
A practical skilling framework for pharma manufacturing
A structured AI skilling roadmap can be organized into five levels.
Level 1 is awareness: what AI can and cannot do in pharmaceutical manufacturing. Level 2 is safe use: approved tools, data security, anonymization, and human review. Level 3 is functional application: using AI for investigations, CAPA drafting, SOP review, audit readiness, training content, and quality risk management support. Level 4 is governance: context of use, validation expectations, documentation, performance monitoring, and life-cycle management. Level 5 is transformation: building internal champions who can identify use cases, measure value, and support responsible scale-up.
The best programs combine classroom learning, hands-on exercises, pharma-specific case studies, and post-training adoption support. For example, participants can be asked to transform a poor deviation narrative into a structured investigation, compare an SOP against a regulation, draft a CAPA effectiveness check, or create an audit checklist from a procedure. These exercises teach the skill that matters most: not accepting AI output, but reviewing and improving it as a qualified professional.
What to measure
AI training should not be evaluated only by attendance. Companies should measure whether the training changes behavior and improves quality system performance. Relevant metrics include user confidence, prompt quality, reduction in drafting time, reduction in review cycles, quality of investigation narratives, CAPA acceptance rate, audit readiness cycle time, SOP review throughput, and reduction in avoidable documentation rework. Over time, organizations can track whether trained teams identify better use cases and use approved tools more consistently than untrained teams.
The operational business case is compelling. AI can reduce time spent on first drafts, improve consistency of documentation, and help teams navigate large volumes of procedures and data. However, the compliance case is even more important. Training reduces the likelihood that employees will use public tools inappropriately, rely on unsupported AI outputs, or misunderstand the boundaries of human accountability. In this sense, AI skilling is both a productivity enabler and a risk control.
Conclusion
Pharmaceutical manufacturing has always depended on trained people. Facilities, equipment, systems, and procedures matter, but they only work when employees understand how to use them correctly. AI is no different. In fact, because AI affects knowledge work, documentation, analysis, and decision support, the need for workforce capability is even more critical.
The next phase of AI in pharma will not be won by companies that buy the most tools. It will be won by companies that build the most capable, responsible, and confident users. Regulators are adopting AI with human oversight. Employees are ready to learn. The technology is already powerful enough to affect daily GMP work. The missing link is structured skilling.
For pharmaceutical manufacturers, the question is no longer, “Should we train people on AI?” The question is, “How quickly can we build AI capability while preserving GMP discipline, data integrity, and patient safety?” Companies that answer this well will not only improve productivity; they will build a more inspection-ready, scientifically rigorous, and future-ready quality culture., , , , , ,
