Quick answer: A pipeline data subscription is only the first cost of pharma competitive intelligence. The business then pays again, in analyst hours and project fees, to match records, verify status, and recover missing details before synthesis can begin. AI-assisted reading and extraction can reduce that repeated work when it feeds maintained, checked records that analysts review.
In life sciences competitive intelligence, that second cost appears in internal hours and project fees as well as subscription spend. With a fixed budget, it can also mean questions that go unanswered.
A 2019 survey of 264 CI practitioners across industries reported that, on average, 29% of project time went to collecting information. Analysis accounted for 23%. Communicating results, planning, management, and evaluation took the rest.
The survey covered CI practitioners across industries, not pharma alone. Even so, it shows more project time going to collecting information than to analysis.
A subscription buys access to information. The business then pays analysts or commissioned researchers to make it usable. Before they can assess the competition, they may need to:
These checks catch the problems from earlier in this series: the programs a pipeline database can [miss], and the “active” labels that did not hold up in our [IBD pipeline comparison]. Synthesis depends on them.
An Alzheimer’s team that separates donanemab’s approved use from its Phase 3 program in preclinical disease needs that distinction for a landscape. A later competitor profile needs it again, and so does a conference briefing. If the correction stays in a spreadsheet or someone’s notes, the next assignment may have to establish it from scratch.
With a fixed budget, the team may narrow its coverage, delay an update, or leave a useful question unanswered.
The whitepaper compares the usual ways teams try to fix this, and what each one still leaves to the analyst.
When people must read and enter every document, editorial capacity limits how many sources a database covers and how often it reviews them. AI-assisted reading and extraction creates three opportunities to improve the underlying records:
Reading is only one part of the system. A fluent summary of an incomplete export keeps the export’s omissions, so the records still need matching, conflict handling, and review before they support a decision. For which CI workflows are ready for automation, see our post on automating competitive intelligence.
Ferma builds and maintains drug and program records from original sources, along with the Agentic Workflows that turn those records into competitive landscapes, competitor profiles, and briefings. Four components connect the source material to the finished deliverable: data, a knowledge graph, intelligence, and agents. One checked program record can then support several deliverables. Analysts still frame the question, judge the evidence, and own the conclusions. Learn more about how Ferma works.