Benefits and risks of AI

What AI can help you do: More time for the work that matters

AI can boost research and clinical workflows when it is built and governed for verification. It can:

• Save time — faster discovery, screening and early synthesis

• Synthesize information — consolidate relevant evidence into more usable views for review

• Scale discovery — explore larger corpora and broader subject areas more quickly

• Support decision-making — strengthen evidence review when outputs are attributable and traceable to sources

• Improve outcomes through governance — enable continuous evaluation and updates as evidence changes

Where the risks come in — and things can go wrong

AI outputs can be compelling and useful, but they are only as reliable as the evidence foundation and the governance around the workflow.

Common risk points include:

• Overreliance on confident answers — fluent outputs may mask gaps in coverage or missing context

• Incomplete or biased retrieval — what the system surfaces can depend on governed access and content selection

• Weak traceability — without clear attribution and visibility into sources, verification becomes difficult

• Privacy, IP and neutrality concerns — poor controls can create compliance and trust issues

• Quality and safety risks — accuracy and completeness need continuous evaluation, not one-time testing

How Elsevier is different

Elsevier helps institutions realize AI value by grounding it in a trusted evidence foundation and responsible governance.

We focus on making AI outputs evidence-based, transparent and governable so users can verify results and experts stay responsible.

Key capabilities include:

• Trusted scientific, medical and health content — curated knowledge that reflects the scholarly and clinical record

• Attribution and citable sourcing — make answers attributable so users can trace back to original publications

• Human expertise in the loop — support expert review and judgment for interpretation and decisions

• Responsible AI governance — privacy, IP protection, neutrality considerations, and accountability that supports safe, governed use

Costs and ROI considerations

AI value depends on more than tool access. Institutions typically need to account for:

• Adoption and onboarding, including AI literacy and workflow change

• Governance setup, including privacy, IP, attribution and auditability needs

• Integration effort, such as aligning content access and user roles

• Validation and evaluation, including domain review steps and success metrics

• Ongoing monitoring, so performance and risk stay continuously assessed

Quality safeguards to look for

Benefits become real when safeguards are designed into the workflow. When you evaluate AI solutions, look for capabilities that support:

• Attribution and citations built into outputs — connect answers to original publications so users can verify evidence and continue research at the source

• Traceability to governed evidence — ground responses in curated scientific and medical knowledge that is continuously updated

• Transparency into sources and limitations — make it easier to understand what information was used and where confidence may vary

• Human oversight support — keep experts in the review loop for interpretation and decisions

• Ongoing evaluation for quality and risk — continuously assess accuracy, completeness and potential risk over time

• Privacy, IP protection and neutrality — support responsible handling of data and enforce governance expectations to reduce compliance and integrity risk