Life-science AI workflow advisory

• Workflow diagnosisThe actual task, its information sources, hand-offs, decisions and rework, and where expert judgment cannot be delegated.
• Use-case prioritizationCandidate uses compared on value, risk, data availability and the review burden they create.
• Human-review designWho verifies what, against which sources, and how errors are caught and recorded.
• Pilot specificationAcceptance criteria, a representative sample and a measured baseline to compare against.
• Adoption planningGovernance, training and the handling of confidential inputs.

An AI demonstration usually shows the easy part. The hard work remains: verifying sources, handling exceptions, protecting confidential inputs and keeping an accountable expert in the loop.

For a specialist team the question is seldom which model to use. It is which task, evaluated how, and reviewed by whom. A bounded pilot measured against a real baseline is the quickest route to a reliable answer.

• Repeated manual workin literature, evidence or document review.
• A scatter of AI experimentswith no clear priority or owner.
• A need for a controlled pilotbefore wider rollout.
• Vendor claimsthat need to be tested against how your team works.

1. Observe the workflow as it is done today.
2. Choose one bounded use case with a clear owner.
3. Design the pilot and the review: acceptance criteria, sample and reviewers.
4. Evaluate against the baseline and decide whether to stop, adjust or extend.

• A current-state workflow and opportunity map
• A prioritized AI use-case assessment
• A pilot specification and human-review design
• An evaluation and implementation roadmap

• StructureA bounded advisory sprint, followed where appropriate by a separately scoped prototype or implementation with technical contributors.
• What we need from youA representative workflow, authorized sample materials, the current review process and a named business owner.
• Roles and limitsChoosing a tool does not establish security, compliance or suitability for regulated use. Engineering, data handling, validation and maintenance requirements have to be defined for the actual use. DrugDevX is developing its own AI-assisted capabilities, and we say so where it is relevant.