“Start With the Hard Problems”: A Novel Approach to Agentic Automation

• CSL Behring deployed AI agent to replace manual searches through 120-page medical rulebooks
• Tool expanded from 20 pilot users to 2,000 across 330 centers in under a month
• Company deliberately chose hardest automation problem first, avoiding low-hanging fruit approach

When most companies dip their toes into AI automation, they start with the digital equivalent of organizing their sock drawer. CSL Behring decided to jump straight into the deep end by automating one of their most complex processes: determining whether plasma donors are medically suitable. This counterintuitive approach of tackling the hardest problem first paid off spectacularly, proving that sometimes the best strategy is to ignore conventional wisdom about starting small.

The plasma industry operates under crushing regulatory oversight where a single misspelled medical term could derail an entire screening process. Before the AI agent, staff had to manually hunt through dense medical documentation, hoping their spelling was perfect and their memory intact. Now they simply ask a question and get consistent, accurate answers in under four seconds. The transformation from paper-shuffling to intelligent assistance represents exactly the kind of productivity leap that makes executives salivate and workers actually happy.

What makes this particularly noteworthy is how quickly adoption scaled once people saw the tool work. Initial skepticism about job displacement evaporated when staff realized the AI wasn't replacing their expertise but eliminating the tedious document archaeology that consumed their days. The rapid expansion from a tiny pilot to thousands of users across hundreds of locations suggests that when AI genuinely solves real problems rather than creating digital busywork, people embrace it enthusiastically. This case study offers a refreshing counternarrative to the usual AI implementation horror stories.