Vistatec and Global RWC hosted a webinar on the value of experts for AI Life Sciences content. Karen Tkaczyk, Ph.D., Vistatec’s Director of Sales for Life Sciences, moderated the session. Panelists included Bruno Herrmann, a Global Consultant in Content Strategy and Product Management, Jeff Mocny, VP of Regulatory Strategy at Abzena, and Rachael Human, Marketing Manager at Global RWC.
As AI reshapes content workflows, the group asked where human judgment still creates the most value.
Five Myths About AI and Value Creation
Bruno Herrmann opened the session by challenging five common assumptions about AI Life Sciences content production. He built his career leading the first in-house content and language team at the largest CRO in the world. He began that work during the COVID years.
His first myth is that AI will replace all human jobs. Herrmann argued that automation and augmentation have always existed in balance, long before AI entered the picture. What changes now is where humans add value. He described the human edge as accountability, nuance, and judgment, qualities that remain central even as automation expands.
The second myth treats quality as the ultimate goal. It is the beginning of a longer journey towards trust, not the destination. Hermann compared it to the difference between a car in a showroom and a car on the road. Content becomes valuable once it works for the people who use it, not simply once it meets a checklist.
Three additional myths rounded out this framing:
- Value does not happen only at the point of delivery. It starts with every contributor across the supply chain, including translators, medical writers, and data scientists.
- Value creation does not belong to a small group of specialists. Engineers, knowledge managers, language experts, and domain experts increasingly work together to produce results.
- AI should not simply get plugged into existing workflows to fix them. Herrmann prefers the term “infusion” to “deployment,” using a tea-bag analogy. Some workflows need a light touch, and others need AI infused more deeply.
Regulatory and Industry Perspectives
Jeff Mocny brought a regulatory lens to the discussion. He described AI as a tool that supports human judgment rather than replaces it.
Regulators already require humans in the loop, and Mocny agreed this requirement makes sense. For him, value and quality connect directly to accuracy. Regulatory work depends on correctly presenting what actually happened.
Mocny’s team is evaluating AI tools for preparing regulatory filing materials, including IND, IMPD, and BLA submissions. He pointed out that guidance documents like ICH M4Q continue to evolve. That raises a real question for any organization adopting AI. How will these tools adapt as requirements change?
Rachael Human described how Global built its RegWriter AI tool with Moderna. The tool uses a graph neural network rather than a large language model. That choice keeps subject matter experts at the center of the writing process, instead of automating first drafts.
She explained that Global defines accuracy as traceability. Every data point needs a clear link back to its original source, something large language models often struggle to guarantee. Global plans to publish a white paper on accuracy standards for AI tools in the coming weeks.
Balancing Speed With Trust
Karen Tkaczyk asked the panelists a direct question. Was saving time and money the real goal behind AI adoption, or did other outcomes matter more? Mocny answered that his organization needs both speed and accuracy, and that these goals sometimes pull in different directions. Cost effectiveness matters, but so does traceability for clients who depend on regulatory precision.
Audience members asked whether the industry should push AI adoption faster or slower. They also asked whether content development and translation get held to different standards. Herrmann suggested that many organizations rush toward big AI investments before confirming basic readiness. He recommended starting with smaller wins and building AI awareness before scaling further.
On the standards question, Herrmann noted that some writers still view translation as a mechanical step rather than expert communication.
Closing Thoughts
Each panelist offered closing remarks. Rachael Human emphasized that accuracy has to stay central as the industry adopts new tools. Bruno Herrmann repeated his core message, arguing that speed without accuracy and trustworthiness creates no real value.
Jeff Mocny described himself as a cautious adopter. He prefers to watch new approaches prove themselves before relying on them fully.
Karen Tkaczyk closed the session with a clear statement. Human expertise is becoming more valuable, not less, as AI expands across the Life Sciences industry. The real question is how teams choose to respond, not whether change will happen.
Vistatec helps Life Sciences organizations pair AI capability with human expertise with services including AI Consulting, AI Governance, and Quality Evaluations of AI. Teams exploring where to start can also explore the AI Gap Analysis service. Contact us to speak with an expert today.
