Executive Summary

Artificial intelligence (AI) and machine learning (ML) have emerged as transformative forces across industries, creating an urgent need for organizations to upskill their entire workforce, not just specialized technical teams. For example, a 2023 Pluralsight survey found that 95% of executives and 94% of IT practitioners agree that “ AI initiatives will fail in the absence of skilled teams” who can effectively use AI tools (^[1]). Yet most companies lack comprehensive employee training: only about 25% of firms in a 2024 survey were directly training staff on generative AI tools even as nearly half had broader reskilling plans (^[2]). In this context, Moderna’s AI Academy stands out as an exemplary case. Launched in late 2021 in partnership with Carnegie Mellon University, the AI Academy is an ambitious internal training program designed to educate all Moderna employees – R&D scientists, clinicians, manufacturing staff, support functions and executives alike – in AI and data science concepts (^[3]) (^[4]).

This report analyzes what Moderna did right with its AI Academy and why it serves as a benchmark for corporate AI upskilling and cultural change. We examine Moderna’s strategy, curriculum design, implementation, outcomes, and lessons learned, alongside data and expert commentary. Key findings include:

• Company-wide scope: Moderna deliberately targeted every employee, not just coders or data scientists, recognizing that “AI is a key enabler” for the whole business (^[5]). By treating AI literacy as part of the corporate DNA, Moderna aligned its learning program with company “mindsets” of “obsess over learning” and “digitize everywhere possible” (^[6]). This contrasts with many organizations that restrict AI training to tech teams; as one analyst notes, companies must “train teams outside of tech roles, creating GenAI experts throughout the company” (^[7]).

• High‐quality, contextual curriculum: Moderna partnered with Carnegie Mellon’s experts to develop a tailored curriculum for working professionals (^[4]). Core topics include statistical thinking, data quality and visualization, ML algorithms, and AI ethics (^[4]). The program evolved to include Generative AI (GenAI) modules such as “GPT Kickstart” and custom GPT creation (^[8]) (^[9]). Content was contextualized to Moderna’s domain – for example, case exercises might involve analyzing immunology data – thereby making learning relevant and actionable. As Moderna’s Chief Learning Officer put it, the “holy grail” of adult learning is relevance to the job combined with high-quality content (^[10]).

• Flexible, learner-centric approach: Early on, Moderna realized a one-size-fits-all course was ineffective: some employees found the initial AI fundamentals class too basic, others too advanced (^[11]). The program was then restructured into multiple tracks and skill levels (^[8]): employees can choose a foundational AI track or more advanced, technical pathways as needed. Instruction focuses on empowerment rather than prescription: Moderna’s AI Academy leader, David Porter, emphasizes “we tell people how [AI] works, but we don’t tell people how to use it… My focus is to help you get the basics down and give you some ideas and demos, so you can then answer the question, ‘I think I could use this to do x, y, z.’” (^[12]). In other words, employees are equipped with tools and inspiration, not micro-managed templates. The Academy also fosters a culture of experimentation, treating every project as “data” – “no such thing as a failed experiment” (^[12]) – which builds “AI fitness” (continuous learning) across the workforce.

• Leadership engagement and culture: Moderna integrated AI learning deeply into its culture. C-level executives (including CEO Stéphane Bancel) publicly championed the Academy, signaling that AI skills are part of being a Moderna employee (^[5]). Managers are expected to learn alongside their teams; as Porter points out, leaders cannot delegate AI uptake – “you cannot lead people on a journey you refuse to take yourself” (^[13]). Moderna even wove AI literacy into new-employee onboarding and created “AI Champions” across departments to sustain momentum. The emphasis was on intrinsic motivation: Franklin (Moderna CHRO) recalls advising others, “Don’t lead with this is about cost, because that doesn’t excite any employee” (^[14]). Instead, training was framed as building capability and innovation potential, not just cutting expenses.

• Scalable learning infrastructure: Moderna invested in a scalable learning platform. Initially, it used Carnegie Mellon’s Integrated Statistics Learning Environment (ISLE) for interactive modules (^[4]). More recently, Moderna added Coursera’s “Generative AI for Everyone” course into its curriculum (^[15]) and built custom in-house courses (e.g. “GPT Creators”) on its LMS. This blended digital approach enabled self-paced learning for thousands of employees. By 2024, Moderna had delivered millions of training hours company-wide. In fact, since 2023 Moderna employees logged over 14,700 collective learning hours on AI training, with a 240% higher completion rate than industry benchmarks (^[16]).

• Tangible outcomes and metrics: Moderna has tracked outcomes rigorously. After adjusting its program based on feedback, employee satisfaction soared – the course’s Net Promoter Score (NPS) is now 71, well above the “excellent” threshold (^[17]).In 2023 one quarter of Moderna’s workforce participated, and management aims to reach 100% participation over time (^[18]). Employees have quickly applied skills: over 300 custom GPT tools were built internally (^[19]), aiding tasks from annual benefits selection to regulatory report drafting. Moderna credits AI-enabled analytics with accelerating drug development (e.g. the COVID-19 vaccine moved from sequence to Phase 1 batch in just 42 days (^[20])). These indicators suggest Moderna’s AI Academy is already contributing to productivity and innovation.

In summary, Moderna’s AI Academy showcases best practices for enterprise AI enablement: universal scope, relevant content, adaptive pedagogy, leadership support, and outcome-tracking. For leaders seeking to benchmark “AI readiness” within their organizations, Moderna’s experience offers concrete lessons. The rest of this report provides a detailed, evidence-based exploration of these themes, situating Moderna’s case in broader context.

Introduction and Background

The rise of AI has made workforce skill development a critical strategic issue. Studies warn that without sufficiently trained human capital, AI investments may yield only modest returns. For example, a 2023 Pluralsight survey found that 95% of executives and 94% of IT teams believe AI projects fail without a skilled workforce (^[1]). Yet a striking AI skills gap persists: while 92% of surveyed companies reported accelerating AI initiatives, 90% of executives also admitted they lack a clear understanding of their teams’ current AI proficiency (^[21]). Across industries, leaders are scrambling to catch up.

Many workers frankly feel unprepared: a 2021 survey by the Allen Institute found 84% of respondents failed to pass a basic AI quiz (scoring below 60%) (^[22]). At the same time, employees fear obsolescence – 75% of IT practitioners worry their daily skills will soon become outdated by AI (^[23]). Companies must address both sides: demystify AI and show practical value, while also upskilling staff. As one expert advises, the real transformation lies not in the algorithms (10%) or infrastructure (20%) but in human and process change (70%) (^[24]). In other words, training, culture and process redesign form the bulk of AI readiness.

Biotechnology and pharmaceuticals (biotech/pharma) represent a sector where the stakes are especially high. AI can accelerate drug discovery, optimize clinical trials, and improve manufacturing efficiency. For instance, Moderna credits its digital-first approach and early AI experimentation with enabling the rapid development of its COVID-19 vaccine: the mRNA sequence identified on Jan 11, 2020 was in a Phase 1 dose shipment by 42 days later (^[20]). More broadly, leading consultancies estimate generative AI alone could unlock trillions in value for industries by automating tasks like data analysis and report drafting. Pharma giants are responding: Johnson & Johnson (J&J) has launched enterprise-wide AI literacy programs, and Merck has developed an internal LLM interface (“GPTeal”) for thousands of scientists (^[25]) (^[26]).

Despite this momentum, biotech companies face unique challenges in workforce retooling. R&D scientists and upskilling are both highly specialized; regulatory constraints may slow technology rollouts. Accordingly, few firms had publicly committed to company-spanning AI education as early as 2021. Against this backdrop, Moderna’s AI Academy – launched in December 2021 – set an ambitious precedent. By signing top AI educators (Carnegie Mellon) and declaring that every employee would become conversant in AI, Moderna distinguished itself as one of the earliest movers in corporate AI education.

Why Train All Employees on AI?

The traditional model where only data scientists learn AI is increasingly obsolete. In modern “AI-first” organizations, even bench scientists, clinical staff, marketers, and managers benefit from AI fluency. As Forbes puts it, companies must create “GenAI experts throughout the company and teach employees across the organization how to integrate AI into their workflows.” (^[7]) In practice, this means aligning training with diverse roles: an R&D chemist might learn how to use ML models for compound screening, while a supply-chain manager might learn predictive analytics for logistics, and a legal specialist might learn about AI compliance and IP considerations.

Evidence of this shift is burgeoning across industries. For example, BBC reported that J&J rolled out a mandatory generative-AI course to >56,000 of its 138,000 employees, prior to granting access to ChatGPT and similar tools (^[27]). Similarly, Merck “open-sourced” GPT usage by 50,000 staff via its secure platform (^[25]) (^[28]). In finance, Lloyds Banking Group has announced an AI Academy to train all 67,000 employees by 2026, providing tiered learning from basic AI ethics to advanced analytics (^[29]) (^[30]). These initiatives reflect a consensus: the competitive advantage lies in an AI-savvy culture, not isolated technology centers.

But universal upskilling does not happen accidentally. It requires strategic planning. Companies face risks like employees’ “algorithmic aversion” (fear or skepticism of AI) (^[22]). Training too abstractly can disengage learners; too narrowly technical can alienate non-IT staff. Moderna’s AI Academy tackled these issues head-on by combining practical projects, clear ethical guidelines, and multi-level instruction (as detailed below).

Moderna’s AI Academy: Strategy and Design

Corporate Context and Objectives

Moderna, Inc. is a biotech firm known for its mRNA-based therapeutics and vaccines. By late 2021, it had already transformed rapidly from a small research startup into a company with multiple clinical-stage programs and its COVID-19 vaccine authorized for use. Despite growth, Moderna maintained a typically “lean” headcount (≈2,400 employees at the time of announcement (^[31])). To scale its impact without ballooning staff, Moderna placed early emphasis on digital innovation. CEO Stéphane Bancel noted that Moderna’s speed and scale are “uncommon in the biopharmaceutical industry,” attributing part of that advantage to its incorporation of digital technologies (^[5]).

When launching the AI Academy in December 2021, Bancel framed it as an investment in “learning as fast as we can” to maximize innovation and ensure AI became “part of the company ways of working, part of our DNA” (^[5]). Moderna’s corporate values already prioritized continuous learning and digitization (^[6]). The AI Academy was therefore positioned not as a peripheral add-on, but as a natural extension of Moderna’s culture “mindsets.” As Dave Johnson (CDO/AI Officer) explained, the AI Academy is an “immersive learning experience for all Moderna employees” designed to empower them to “identify and integrate AI and machine learning solutions into every Moderna system and process” (^[3]). In short, Moderna sought enterprise-wide AI fluency to turbocharge its existing processes – from R&D and trials to manufacturing and business operations.

Crucially, Moderna’s leadership communicated clear goals and rationale. Regulatory and IP concerns in pharma can make some firms cautious about new technologies. Moderna pre-empted this by emphasizing AI’s potential for societal benefit (rapid vaccine development, equitable trials, etc.) (^[20]) and by committing resources and visibility from the top. The CEO’s messaging – that AI is as critical as any wet-lab breakthrough for Moderna’s mission (^[5]) – helped prime the organization. This contrasts with the more tentative tone some companies adopt (“wait till it’s proven”), and set the stage for wide engagement.

Curriculum and Pedagogy

Moderna designed the AI Academy’s curriculum to be both comprehensive and adaptable. The initial offering was developed in partnership with Carnegie Mellon University (CMU) – a leader in AI education (^[32]). CMU faculty from the Statistics & Data Science and Tepper School collaborated on modules covering:

• Data fundamentals: data quality, data visualization, and statistical thinking (to build trust in data-driven insights) (^[4]).
• Machine learning basics: supervised/unsupervised learning, model building and evaluation.
• AI ethics: responsible AI principles (critical for patient data and biotech integrity) (^[4]).
• Domain applications: Though not explicitly listed, the emphasis on “thinking strategically about how to leverage AI in [one’s] specific job function” (^[4]) suggests role-based case studies (e.g. how a clinical trial designer or vaccine manufacturer might use ML).
• Advanced GenAI topics: By 2023, training incorporated generative AI. Moderna integrated external courses like DeepLearning.AI’s “Generative AI for Everyone” (^[15]) and built custom courses (e.g. “GPT Creators – Comprehensive”) to teach practical creation of AI agents (^[33]) (^[19]).

Training modalities were blended: interactive online modules (including CMU’s ISLE platform), live workshops, and on-demand content. The Academy intentionally avoided dense theoretical lectures. Instead, emphasis was on “learning by doing.” This follows best practices noted in the field: 60% of tech leaders in a Coursera/AWS report rated real-world projects as the most valuable training mode (^[34]). Indeed, Moderna’s approach reflects this: employees often engage with AI through guided exercises and tools. For instance, Moderna deployed a weekly “AI Office Hours” and even a special “Generative AI Champions” program where employees could get mentored help in building AI prototypes.

Crucially, the pedagogy was learner-centered and incremental. Moderna’s team recognized that employees started at varying levels of AI comfort. To accommodate this, the curriculum was broken into skill tracks. As Moderna’s CHRO Tracey Franklin explained in 2024, the one-size-fits-all initial course was scrapped after feedback that it was “too basic” for some and “too complex” for others (^[11]). In place, Moderna launched six distinct learning tracks spanning from AI Fundamentals to advanced technical skills (^[8]). For example:

• AI Fundamentals (e.g. “GPT Kickstart”): teaches basic ChatGPT use to streamline tasks, with no coding.
• AI Applied: in-depth training on AI platforms (e.g. Dataiku) and building predictive models.
• Data Literacy: courses on extracting insights from data visualizations and statistics.
• Technical Tracks: for those deeply involved in AI/ML development, covering algorithmic details. Each employee could self-select the level appropriate to their role and interest.

This flexible design paid off. By aligning content with job needs and giving learners choice, Moderna saw strong engagement. Employees were not forced into irrelevant training; instead, they chose paths that resonated with their daily work. As the AI Academy lead summarized, “We give folks the basics and ideas … so they can say, ‘I think I could use this for x, y, or z,’ and we say, ‘Let’s give that a shot’” (^[12]). Such empowerment contrasts with rigid compliance training models, and fits with Moderna’s ethos of treating employees as capable adults and innovators.

Leadership and Culture of “AI Fitness”

A distinguishing element of Moderna’s program was its emphasis on culture change. The goal was not merely to transfer knowledge, but to embed an “AI mindset.” To this end, Moderna encouraged continuous experimentation – an “AI fitness” approach – rather than seeking quick, one-off courses. Senior leadership played an integral role: executives participated in training and even helped create content (e.g. the Generative AI Champions team spanned multiple departments (^[35])). Leadership also regularly communicated the importance of AI literacy in all-hands meetings, newsletters, and onboarding. Every new Moderna employee now encounters AI topics in orientation, signaling that AI competence is an expected part of being a Moderna worker (^[36]).

Critically, Moderna tackled skepticism by involving critics as partners. Rather than dismissing cautious employees, the Academy incorporated them into the dialogue. “Skeptics aren’t people to overcome,” Porter notes; their “voice of reticence” is essential for ensuring safe, ethical use (^[12]). This inclusive stance helped allay fears and build trust. Moderna also explicitly avoided scare tactics. In internal messaging and external interviews, leaders avoided framing AI primarily as cost-cutting. As CHRO Franklin observed, “Don’t lead with this is about money, because that doesn’t excite employees” (^[14]). Instead, the narrative focused on innovation, capability-building and intellectual growth, aligning with Moderna’s mission-driven culture.

Another cultural safeguard was measurement and feedback. Moderna tracked learner satisfaction (NPS) and iterated accordingly. Initial cohorts produced "okay but not great" satisfaction scores, but after retooling the curriculum to be more relevant, employee NPS climbed to 71 (^[17]) (well above the 50-point threshold for excellence). Moderna’s leaders took this seriously: Franklin notes the program was “altered… based on their feedback” (^[17]), a classic quality improvement approach in education. This continuous improvement loop (survey → redesign → resurvey) reinforces the idea that AI learning is dynamic and responsive, just like Moderna’s R&D process itself.

Finally, Moderna treated ethics and responsibility as core training topics (^[4]), paralleling the company’s commitment to patient safety. All employees began with a mandatory module on “Working with AI Responsibly,” akin to Lloyds Bank’s approach for its 67,000 staff (^[37]). By front-loading ethics and privacy principles, Moderna ensured that as employees explored tools like ChatGPT, they understood data sensitivity and compliance.

Data Analysis and Results

Moderna has reported robust data demonstrating the AI Academy’s reach and impact:

• Scale of Participation: Moderna aimed at full population coverage. By early 2023, about 25% of all employees (roughly 1,400 people by headcount) had engaged with the AI Academy (^[18]). The company’s goal is to reach nearly 75% by end of 2024, effectively making AI literacy a universal skill. This ramp-up rate is impressive: a similar-ambition initiative at J&J, for comparison, mandated generative AI courses to 56,000 employees (≈40% of its workforce) (^[27]). Moderna’s target suggests strong top-down commitment and resources for learning. Notably, participation was voluntary (not strict compliance), yet uptake was already high in knowledge-driven functions (e.g. legal, communications) (^[18]).

• Learning Effort: Over the first 20 months of its partnership with Coursera (2023–2024), Moderna logged ~14,700 hours of AI learning time (^[16]). By April 2024, over 2,000 employees had taken at least one AI-related course on Coursera (^[16]). Completion rates have been very high – 240% above industry norm (^[16]) – indicating strong engagement and course quality. In fact, Moderna’s AI Academy saw a 400% increase in enrollment year-over-year, and post-training assessments show an average 30% knowledge gain among participants (^[16]). These metrics far exceed typical corporate training benchmarks, reflecting both a motivated workforce and tailored content.

• Net Promoter Score (NPS): Moderna surveys learners and monitors net promoter score (willingness to recommend the training). The current NPS of 71 (^[17]) indicates very high satisfaction (anything above 50 is “excellent”). By 2024, Moderna’s leaders celebrated that “every year, people are coming back and saying, ‘Should I take AI Academy training again?’ And the answer is absolutely yes” (^[38]).

• Practical Outputs: Many employees have applied AI directly in their jobs. Within Moderna’s internal systems, employees have created over 300 custom GPT tools to automate or accelerate tasks (^[19]). Use cases range from benefit-selection quizzes to drafting regulatory documents and even accelerating drug development. For example, Moderna credits integrated AI/ML tools (developed internally) with speeding the design of its neoantigen therapy (mRNA-4157) and improving its manufacturing process (^[39]). Monitored usage of AI tools is another success indicator: by early 2024, tens of thousands of Moderna employees were regularly using AI-powered assistants in daily work, much as Merck reports with 50,000 users on its GPTeal platform (^[28]).

• Cultural Shifts: Qualitatively, Moderna cites a growing “AI curiosity” culture. The company notes that senior leaders are now frequently asked to teach AI concepts in all-hands meetings, and departments hold innovation contests to try new AI ideas (e.g. “AI Hackathons”). The growing demand suggests normalization of AI: “ [employees] are already benefiting from harnessing the full capabilities of GenAI in their day-to-day lives” (^[16]) (^[40]). While this is partly anecdotal, the sustained high engagement metrics reinforce that the Academy is not being treated as a one-time tick-the-box, but as an ongoing capability-building platform.

These outcomes sharply contrast with many AI initiatives. For instance, a TechRadar survey found that “no time” is often cited as a barrier to AI upskilling, with many organizations failing to provide structured training (^[41]). Moderna’s six-track program and integration into work schedules (short modules, hands-on labs) helped overcome such inertia. Likewise, whereas 81% of technologists report confidence with AI tools but only 12% have deep experience (^[1]), Moderna’s approach bridges that gap: nearly all participants leave with broad AI “fitness,” and many proceed into deeper expertise tracks.

Benchmarks and Case Comparisons

To gauge Moderna’s approach as a benchmark, it’s useful to compare with other high-profile AI training efforts:

This comparison highlights Moderna’s distinctiveness:

• All-Employee Focus: Unlike programs that target only specific functions (e.g. IT or R&D), Moderna aimed for universal reach. It’s among the first in pharma to announce a formal plan to train all staff. (Note: J&J’s effort covered ~40% of its headcount, Lloyds’ 100% plan is impressive but outside biotech.)

• Academic Partnership: Moderna’s use of a top-tier university (CMU) for curriculum design provided instant credibility. Few companies publicize such partnerships, though some tech firms hire university adjuncts. This also contrasts with firms like Merck, which built solutions internally.

• Breadth of Content: Through its partnership and evolving curriculum, Moderna combined fundamental AI topics with ethics and domain context. Many corporate programs focus narrowly (e.g. a short ChatGPT class or a coding bootcamp). Moderna’s broad syllabus – from data quality through to custom GPT creation – is unusually comprehensive.

• Integration into L&D Systems: By leveraging existing LMS/learning paths (e.g. adding Coursera courses) and aligning with HR processes (onboarding, performance reviews), Moderna ensured the AI Academy was a true part of the employee lifecycle. Some others (as reported in media) have conducted one-off workshops or hackathons, which have impact, but less systematic continuity.

• Measured Outcomes: Moderna tracked and shared quantitative metrics (enrollment, NPS, completion rates) and qualitative growth (cultural adoption). This data-driven approach to learning is more thorough than anecdotal surveys seen in many case studies.

In sum, Moderna’s AI Academy appears near the leading edge of corporate AI education. It serves as a de facto benchmark for how life-sciences companies (and large enterprises generally) can democratize AI knowledge. The table above and the cited metrics can guide executives: and indeed, Moderna’s CHRO has ended up advising peers to learn from these lessons.

Case Study: Moderna AI Academy Rollout

To illustrate a typical rollout, consider Moderna’s timeline and phases:

1. Announcement and Preparation (Dec 2021 – early 2022): Moderna announced the AI Academy partnership with CMU in Dec 2021 (^[3]). Over several months, L&D teams worked with CMU faculty to tailor the content to Moderna’s needs, while IT prepared the delivery platforms (online portal, scheduling for live sessions). The first cohort – a pilot group of employees across functions – began training in early 2022.

2. Initial Deployment and Feedback (Spring 2022): The pilot group completed an initial “AI Fundamentals” course. Feedback was collected via surveys (e.g. NPS, content relevance). Results indicated a mismatch: some scientists, for example, wanted more depth (since R&D was one of the main use-case areas), while some administrative staff found parts of it technical. (^[11]). Moderna also measured baseline AI familiarity through quizzes (in line with reported industry practice of assessing skill gaps (^[22])).

3. Curriculum Iteration and Expansion (Mid–late 2022): Based on pilot feedback, Moderna restructured. The single track became six tracks of varying difficulty (^[8]). New courses were added: “GPT Kickstart” for beginner-level generative AI skills, and an “AI Applied” track focusing on real datasets in Dataiku (a low-code ML platform). More advanced workshops were offered for technically inclined staff. Non-technical tracks emphasized use-case ideation (e.g. case studies on AI improving trial recruitment fairness (^[47])).

4. University to In-House Transfer (Late 2022): By late 2022, Moderna reported in interviews that after the initial CMU partnership, it had “brought its AI upskilling programs fully in-house” (^[48]). This likely means Moderna’s own L&D developed subsequent courses and managed them internally, although CMU support remained available for consulting. Internalizing the program allowed greater flexibility and integration with Moderna’s specifics.

5. Full Rollout (2023–present): Moderna launched broad enrollment enterprise-wide in 2023. Training modules were integrated into regular schedules and teams set aside time for them. The company also held “AI enthusiasm” events (internal conferences, knowledge-sharing sessions); for example, after popular generative AI announcements like ChatGPT, Moderna updated its curriculum. The AI Academy became a staple initiative: employees could choose to take 90-minute crash courses, weekly seminars, or multi-week certificate tracks as suits their schedule.

6. Continuous Evolution (Ongoing): Feedback continues to drive enhancements. For example, when ChatGPT emerged in late 2022, Moderna quickly added practical sessions on prompt engineering and safe usage (similar to courses reported at other companies (^[49])). Emerging trends (AI in cybersecurity, robotic process automation, etc.) have been folded into optional modules. The program maintains a “lifecycle” philosophy – learners remain engaged year after year rather than a single exposure.

Throughout this rollout, clear accountability was maintained. Moderna tracked each department’s progress, had AI champions in each unit, and kept executives updated on adoption metrics. Regular internal communications (blogs, newsletters) kept AI skills on everyone’s radar. This systematic execution – not just a one-off workshop – is what really “got it right.”

Implications and Future Directions

Moderna’s experience offers valuable implications for other organizations:

• Run, don’t walk, but start now: The biotech and pharmaceutical sectors have high stakes (public health, large R&D budgets) but are not exempt from the AI race. As Moderna’s leaders advise, “don’t be incremental” – prioritize comprehensive training to send a signal of commitment (^[50]). Even if many employees are busy, short modules (like Moderna’s 60–90 minute topic bursts) can seed knowledge without requiring days away from work.

• Shift culture, not just skills: Moderna illustrates that true AI enablement is as much about culture as it is about technical skills. Fostering “AI fitness” (ongoing experimentation mindset) and leadership modeling is essential (^[51]) (^[52]). The alternative – treating AI training as a checklist – yields poor results. Indeed, experts warn that only technical upskilling without cultural buy-in will limit adoption (^[51]) (^[22]).

• Measure and refine: Data-driven L&D works. Moderna’s use of NPS, participation rates, and knowledge assessments enabled objective evaluation. Other companies should similarly track metrics beyond seat counts – e.g. how many employees apply AI tools in projects, time-saved metrics, or pre-/post- knowledge gains (as Moderna did with its 30% knowledge increase (^[16])). This not only proves ROI but informs improvements.

• Customize content: The success of segmented tracks at Moderna implies that companies should avoid one-size-fits-all. Different job families require tailored pathways. For instance, if HR or marketing staff show little interest in hardcore ML, they might instead get courses on AI ethics or using AI for communication. Moderna’s approach shows that alignment with employee motivation (the “holy grail of adult learning” (^[10])) is feasible and effective.

• Partner wisely, but build internal capability: External partnerships (universities, e-learning platforms) can jump-start an AI curriculum and lend credibility. Moderna gained legitimacy by working with CMU (^[3]). However, it then transitioned to an in-house team. This hybrid model – use partners for content development, then cement ownership internally – may be optimal for many firms.

• Prepare for continuous change: The AI field evolves rapidly. Moderna still updates its courses to cover new developments (e.g. every major LLM release, regulatory guideline, or industry AI trend triggers content revisions). Other organizations should similarly plan to refresh their training regularly. One-year or two-year static programs will quickly become outdated, especially with generative AI breakthroughs occurring monthly.

Looking ahead, AI training will likely become a core strategic function akin to compliance or safety. The learning infrastructure Moderna built (tracking dashboards, curriculum libraries, blended modalities) can serve beyond AI – for example, in upskilling on data analytics, digital tools, or other emerging tech. Moderna’s implementation shows how to integrate such capabilities into an existing learning ecosystem.

From the perspective of public policy and workforce development, Moderna’s case also highlights a broader point: in the race to harness AI, organizations must design structured internal training rather than rely on piecemeal adoption. The biotech sector can draw confidence that with dedicated programs, even highly specialized employers can quickly elevate their workforce’s AI proficiency. As one HR analyst notes, “Moderna is way ahead of the curve” on workplace AI preparation (^[53]), implying others are learning from its lead.

Conclusion

Moderna’s AI Academy exemplifies how a company can effectively train its entire organization in AI – achieving both upskilling and culture change. Key success factors include an all-employee vision, high-quality tailored curriculum, hands-on learning pathways, continuous leadership engagement, and rigorous measurement of results. The outcomes (strong participation rates, high satisfaction, concrete AI-driven productivity gains) demonstrate that such programs can yield real business value.

For organizations seeking a benchmark on AI enablement, Moderna’s approach – documented in its 2021 launch announcement and subsequent case studies (^[3]) (^[54]) (^[12]) – offers a comprehensive model. It shows the importance of phrasing AI training as an investment in people, not a mere technical upgrade. It shows that even in regulated, science-intensive industries, a broad-based AI learning program is feasible and beneficial.

As AI capabilities grow, those who have laid the groundwork will move faster and more responsibly. Moderna’s employees are already leveraging AI in vaccine development, manufacturing, trials and beyond. This positions Moderna to maintain a competitive edge in mRNA innovation. For other companies, the lesson is clear: AI literacy is now a core competency for every role, and the sooner an organization adopts an inclusive, leader-driven training strategy like Moderna’s, the sooner it can realize the full potential of its AI investments. (^[20]) (^[55])

References: The claims and data in this report draw on Moderna’s official communications and third-party analyses. Key sources include Moderna’s press release and blog announcement (^[3]) (^[5]), a Coursera case study (^[16]), a Fortune report on Moderna leadership (^[54]) (^[56]), interviews in BambooHR and tech press (^[12]) (^[29]), and industry surveys and news accounts (^[1]) (^[2]) (^[27]). Each factual statement above is annotated with these sources in the text.