# David Pagan

David Pagan works on AI for manufacturing operations, with a focus on applying the technology in regulated life sciences and other complex industrial settings. His posts discuss practical issues such as site-specific processes, software reliability, quality systems, and turning technical advances into scalable commercial products.

- Profile: https://b2binfluencer.marketing/creators/david-pagan/
- LinkedIn: https://www.linkedin.com/in/jdavidpagan
- LinkedIn followers: 2,348
- Topics: artificial-intelligence
- Based on 11 dated posts, latest 2026-09-14

## Summary

David Pagan is most useful for grounded talks or advice on moving AI and quantum into regulated manufacturing operations.

- Best for: B2B life sciences technology firms planning a keynote or scoped advisory on regulated manufacturing AI.
- Not for: Consumer health or wellness brands seeking high-reach sponsored video series with defined rates and audience measurement.
- Check first: Ask for a talk recording or reference, and confirm fee, availability, terms, and Murmuratto role.

### Takeaways

- AI deployments must fit each plant, not the company org chart.
- Quick AI prototypes can become a manufacturing single point of failure.
- Scientific breakthroughs do not create value unless commercial execution follows.

## Main ideas

- **AI deployments have to fit the individual plant, not the company org chart**: Pagan's central claim at the AI in Manufacturing event is that manufacturing is not one homogeneous environment. Even within a single company, sites differ in processes, equipment, systems, risks and regulatory requirements, so AI deployments have to account for those differences at the site level. They warn that when companies ignore that context, the consequences can reach the supply chain: shortages of goods or supplies, or worse, of drugs and therapies. They see the slow pace of AI adoption in manufacturing as potentially a good sign: the complexity demands care before scale.
- **Quick AI prototypes can become a manufacturing single point of failure**: Pagan argues that AI has made it remarkably easy to prototype internal applications, but that convenience carries operational risk. In their view, business-critical software cannot depend on the executive who built it, one IT person who maintains it or undocumented grassroots knowledge. They draw on a familiar manufacturing idea: plants already understand single points of failure in physical operations. The question is how to apply that thinking to the software running alongside them. Software reliability, they suggest, becomes manufacturing reliability.
- **The next step after data-driven manufacturing is knowledge-driven manufacturing**: Pagan describes sites connecting ERP, MES, QMS, PLC and SCADA data to make data-driven decisions, and calls that useful. The next step, they argue, is knowledge-driven decision-making: combining operational data with the SOPs, policies, procedures and other information that define how manufacturing is supposed to work. They acknowledge this is partly a question of wording, but say the concept holds on its own. They also urge sites not to wait until all their data is perfectly structured before starting AI initiatives, and argue that AI in manufacturing operations is not a strategy by itself.
- **Scientific breakthroughs do not create value unless commercial execution follows**: Reporting from The Economist's Commercialising Quantum, Pagan says AI and quantum can reinforce each other in life sciences, with use cases a recurring theme of the day. Across drug discovery, materials science and battery development, they saw a consistent opportunity: better modelling and understanding of molecular interactions that are difficult, expensive or time-consuming to explore through traditional approaches. AI already accelerates parts of that work, and quantum computing is increasingly discussed as an additional layer. But Pagan says most of the conversation focused on scientific and technological breakthroughs, while the path from discovery to commercial reality still runs through manufacturing operations. Speed to market, financial viability, regulatory compliance and scalability determine whether innovation creates value or remains an interesting result.
- **AI tools for regulated plants should be built by people who have run them**: Pagan's argument is about who builds operations AI and how their background shapes the product. They describe Murmuratto, the company they work with, as a multinational with three offices, including one in San Juan, Puerto Rico. Its team includes bio experts with decades of regulated manufacturing experience at global biopharma and biotech sites — people who understand quality systems, regulatory requirements and production pressures. Pagan says that expertise shapes governance, deployment strategy and system architecture: the team designs around the jobs to be done, the workplace environment and how decisions actually get made. The stated design priority is the lowest barrier to entry and highest usability for people on the plant floor.
- **Conference field notes are how Pagan works out a position**: Across the posts, Pagan turns event attendance into structured arguments rather than diary entries. They attend an industry gathering, identify the theme that kept resurfacing, develop it through numbered observations or a point of view, and end with a question for readers. The events range from manufacturing AI to quantum commercialisation to regional tech and biopharma gatherings. This gives readers a view of how Pagan forms and states positions publicly, anchored to something specific said or shown in the room rather than general commentary.

## Work to read

- [AI in manufacturing: the adoption gap, software reliability and knowledge-driven operations](https://www.linkedin.com/posts/jdavidpagan_last-week-i-attended-aimst-ai-in-manufacturing-activity-7497616211782926336-Bz7N)
- [AI and quantum in chemistry and life sciences: execution decides what wins](https://www.linkedin.com/posts/jdavidpagan_uk-i-think-ai-and-quantum-potentialize-activity-7472991703155654656-Hy6d)
- [Why regulated-manufacturing experience shapes how AI tools get built](https://www.linkedin.com/posts/jdavidpagan_this-week-san-diego-flew-west-to-join-activity-7475189682625089537-KXtR)
- [A short note on switching from manufacturing to tech at ConvergeSouth](https://www.linkedin.com/posts/jdavidpagan_convergesouth-activity-7500205444439728128-yRdf)

## Brand relationships

- **Murmuratto** (disclosed_partnership)
- **Invest Puerto Rico** (mention)
- **The Economist** (mention)
- **ConvergeSouth** (mention)

## Partnerships

Pagan's posts cover AI in manufacturing, quantum commercialization in life sciences, and their company's work, but don't describe a partnership, sponsorship, or paid-collaboration format. They include a thank-you to the Flywheel team after ConvergeSouth and note Pagan's participation in an Invest Puerto Rico delegation at BIO 2026 on behalf of Murmuratto. Neither post presents a buyer-facing opportunity; gratitude toward an event organiser doesn't establish a commercial relationship. For a campaign, ask Pagan or Murmuratto directly about availability, formats, and pricing. A first enquiry through Pagan's LinkedIn profile can specify the proposed engagement, such as a talk, advisory role, or sponsored placement.

## Alternatives

- Dan Lewis: Choose Dan Lewis over David Pagan when the same life-sciences technology company wants the keynote and advisory session anchored in the pharma industry's current deal, regulatory, and manufacturing-lifecycle context. Dan Lewis's posts summarise recent industry moves such as Bristol Myers Squibb's $15.2B licensing deal with Jiangsu Hengrui across 13 early-stage oncology, hematology, and immunology programmes; Takeda's plan to cut 4,500 jobs by March 2027; Eli Lilly's Mounjaro becoming the top-selling drug in Q1 2026; and the EU's provisional Critical Medicines Act deal. The posts also connect digital manufacturing and automation to project delivery, CQV, quality, and operational readiness, and argue that complex manufacturing challenges are rarely solved by optimising one discipline in isolation. This is a better fit when the audience needs a current market and lifecycle-integration briefing, or when the buyer wants a speaker who argues that engineering, quality, automation, digital systems, and operations should align around the same objectives.
- Lavine Hemlani: Choose Lavine Hemlani when the same company wants the keynote and advisory session to address where the money and national strategy for life-sciences manufacturing are moving, rather than plant-level AI execution. The posts describe the UAE as a coordinated biotech build-out: $2.5 trillion sitting in seven UAE institutions against only $2 billion of venture capital raised in the country in 2025; non-oil activity at 77.3% of a $517 billion GDP; Mubadala deploying $17.4 billion in the first nine months of 2025; the UAE making less than 5% of its own medicine today and aiming to become a place where medicine is invented; Mubadala holding more than $3 billion in biotech with drug discovery as its largest investment sector; a crown-prince-level Longevity Authority; a national manufacturing drive; and a population genomics programme. This is a better fit when the buyer wants an investment, sovereign-capital, and ecosystem-construction lens on biopharma manufacturing, or when the audience is deciding where to locate or partner rather than how to deploy AI on a regulated site.
- Lane Cooper: Choose Lane Cooper when the same company wants the keynote and advisory session framed around enterprise AI accountability, governance, and cyber resilience rather than regulated manufacturing execution. The posts argue that boards have moved from AI enthusiasm to demanding measurable financial and competitive outcomes; cite an August 2026 Gartner research note on evidence of business outcomes and an ISG study of 1,200 enterprise AI use cases in which 31% of prioritised initiatives reached production and only about one in four met growth-return expectations; and describe CIOs being asked to drive growth and profitability while controlling AI spend, reducing legacy technology costs, and maintaining governance. The second post extends the buyer task into risk: AI increases the speed, scale, and complexity of data, identities, applications, and decisions, so leaders need visibility into where information originates, how it moves, and whether its integrity can be demonstrated across preparedness, readiness, visibility, response, recovery, governance, and survivability. This is a better fit when the buyer's audience is enterprise technology and security leadership and the desired outcome is a board-level case for AI accountability and resilience.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/