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.
Where does a competitive intelligence analyst’s time go?
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.
Why do pharma CI teams pay twice for pipeline data?
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:
- Match drug names and development codes, so records that describe the same asset connect
- Verify status and sources, to establish which programs remain relevant and what supports each claim
- Recover decision details, such as patient groups, treatment settings, and rights missing from the search
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.
Why do the same checks keep coming back?
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.
Where can AI competitive intelligence help?
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:
- Reach more original sources
- Keep the patient and program distinctions that change the answer
- Revisit sources for changes
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.
How does Ferma approach it?
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.
What’s inside the whitepaper
- What adding a data source, monitoring, commissioned research, an internal build, or provider upgrades can solve, and what each leaves behind
- How each of the three AI opportunities works, and where review is still required
- How Ferma’s four components work together, using the donanemab example from start to finish
- A test that compares your current process with Ferma on coverage, evidence, cost and effort, and later work
Frequently asked questions