Post Event Recap • Life Sciences
Agentic AI in Life Sciences.
Game Changer or just Hype?
You were registered but couldn’t attend. Here are the key insights from the event, summarized for leaders in Life Sciences.
75 — 85%
of workflows will be supported by AI.
95%
of job roles will fundamentally change.
25 — 40%
less personnel needed in Commercial and Finance.
6 — 8%
productivity increase in companies.
The Scale of Change
What’s at stake in Pharma and Life Sciences industry
According to McKinsey, nearly all workflows in pharma will be AI-supported in the future.
The question is not whether. The question is: how, and under which governance.
Key Message from the Event
“If your business is based on language and processes, you are already within the impact radius. The question is not whether AI will be used, but under which governance it remains controllable and traceable.”
Key Insights from the Event
The real hurdles are not technical
The biggest obstacles have nothing to do with the AI technology itself.
They lie in implementation, ownership, and traceability.
Hurdle 01
No clearly defined AI use case
Teams reach for AI tools without defining the actual business problem. AI for the sake of AI creates complexity, not value. First the pain point, then the technology.
Hurdle 02
No implementation plan
Good intentions don’t deliver results. Without a structured path from idea to pilot to scale, most AI initiatives fail after the first proof of concept.
Hurdle 03
IT or business? Unclear ownership.
If responsibility is not clearly defined, AI becomes a coordination conflict. Instead of efficiency, shadow processes, delays, and shifting responsibility emerge.
Hurdle 04
Fragmented data and tools
AI requires access to structured processes, consistent data, and clear ownership. In many organizations, these foundations are not yet in place.
LinkedIn Poll • Vote Now
Which hurdle is biggest in your organization?
Vote and see what other Life Sciences leaders say. The results will be shown live.
The Honest Answer
When does it work? When doesn’t?
Agentic AI delivers results when processes, ownership, and data are clear.
Not before.
Game Changer — when…
✅ Speed with control
Content variants are created faster with full traceability of source, version, and approval status.
✅ Faster MLR approvals
AI reduces friction before review: structuring, consistency checks, referencing. Fewer revision cycles.
✅ Scaling without headcount
Marketing and Sales reach more segments and channels without hiring proportionally more staff.
Still Hype — when…
❌ No clean data foundation
Unclear approvals mean: no one takes ownership and everything is manually checked. The time savings disappear.
❌ Ownership not defined
Without clear responsibility for risk, approval, and operations, coordination chaos replaces efficiency.
❌ No audit trail
In regulated environments, the rule is: no traceability, no approval. Without proof, nothing is usable.
The Factorial Approach
The AI Impact Framework
Structured. Pragmatic. Built for regulated industries. Five steps from business goal to scalable AI in operations.
01
Ground & Discover
Clarify business goals and pain points. Identify use cases with the highest ROI potential.
02
Design &
Decide
Specify use cases and evaluate costs, benefits, and risks for an informed go/no-go decision.
03
Build &
Prove
Implement a prioritized use case as a Minimum Viable AI and validate it with real KPIs in a pilot.
04
Scale &
Govern
Integrate the solution into operations, scale step by step, and establish pragmatic governance.
05
Vision &
Align
Derive an AI strategy and roadmap aligned with the long-term company strategy.
Lessons Learned • From the Panel
Three things everyone took away
Transformation affects people
AI can only be successfully introduced if the people affected are involved from the start. Change management is not an add-on, it’s the foundation.
Define the business case first
Don’t introduce AI for AI’s sake. Solve a real, measurable business problem. Sustainable return comes from that, not from the tool itself.
AI is here to stay
The earlier organizations start, the better positioned they are. Those who operationalize AI now as a process and governance topic will define the new standard instead of reacting to it.
Let’s talk about what this means for your organization.
No pitch deck. No pressure. Just an open conversation about where AI can create real value in your context.
