Split Sentiment and Rising Trust Barriers Mark AI's Rapid Adoption in Life Sciences
- Niv Nissenson
- Jun 22
- 2 min read
A new global survey of 408 life science researchers reveals a dramatic increase in the adoption of artificial intelligence (AI) across the industry — but also underscores growing concerns around trust, transparency, and data quality. According to a press release published June 20, 2025. The survey, commissioned by BenchSci and conducted by Forrester Consulting, highlights how AI is both a promising asset and a source of uncertainty within one of the world’s most critical sectors.
Key Findings:
88% of respondents say their organization is currently using AI in scientific research.
66% report increased AI adoption compared to a year ago.
54% say their trust in AI is growing — but 72% believe lack of transparency limits adoption.
The top challenge? Data quality and availability, cited by 66% of respondents.
Only 30% of researchers fully trust the results generated by AI in scientific workflows.
BenchSci CEO Liran Belenzon commented on the findings, noting that “while AI adoption is strong and growing, trust hasn’t caught up — and that’s a problem.” He stressed the need for “explainable, purpose-built AI” tailored specifically to scientific R&D, calling on the industry to close the trust gap through better transparency and communication.
Despite growing implementation, researchers express concern over ethical risks, biased algorithms, and lack of interpretability. Many organizations are exploring hybrid approaches to AI governance that balance innovation with compliance, especially as AI becomes more embedded in critical workflows.
The report highlights a growing trend: while AI has the potential to revolutionize how research is conducted, the human element — trust, understanding, and collaboration — remains essential.
About BenchSci: BenchSci is a leading AI-driven platform built specifically for life sciences research and development. The company’s technology helps scientists discover and interpret data faster, enhancing decision-making in the early stages of drug development. BenchSci is backed by top-tier investors and works with many of the world’s largest pharmaceutical companies.
TheMarketAI.com Take:
AI’s ability to aggregate and interpret data — especially using plain or scientific language — is undeniably impressive. However, we’ve all seen AI tools occasionally generate convincing but inaccurate responses. Large language models are built to predict likely answers, even when the underlying data isn’t fully reliable. In scientific research, that’s a risk. “Trust but verify” remains the safest approach when applying AI to critical workflows like those in life sciences.
