The right AI use case begins with a workflow problem that teams can clearly describe and measure.

A task may be repetitive, slow, or frustrating without being ready for AI. The required data may be incomplete. The process may vary across studies. The output may carry more risk than the organization can manage. A simpler form of automation may also solve the problem more reliably.

Before selecting an AI use case, clinical operations teams should ask:

• What problem are we trying to solve?
• How is the work performed today?
• Which type of technology fits the task?
• Is the required data available and reliable?
• What happens if the output is wrong?
• Where will people review or approve the work?
• How will we measure whether the workflow improved?

These questions give teams a practical way to focus AI investment on work that is valuable, feasible, and ready to change.

What Problem Should the AI Solve?

Start with the operational problem, including who experiences it and what it affects.

“Use AI to improve study startup” is too broad. A stronger use case might focus on comparing protocol requirements with country-specific document needs, identifying missing submissions, or routing completed documents to the next reviewer.

The problem should be specific enough to measure. Useful starting points often involve work that:

• Takes significant time across many studies
• Requires people to search or compare large amounts of information
• Creates repeated handoffs and delays
• Produces inconsistent results
• Depends on recognizing patterns across multiple data sources
• Pulls experienced staff into routine administrative tasks

The team should also understand why the problem exists. If delays come from unclear ownership or unnecessary approvals, adding AI may leave the underlying issue in place. Process redesign may need to happen first.

Does the Workflow Need AI?

Different clinical trial tasks require different kinds of technology. AI should be selected because its capabilities fit the work.

Traditional automation works well when the rules are stable and the inputs are structured. It can move a file, populate a field, send a notification, or complete a calculation the same way every time.

Predictive models can estimate a likely outcome, such as enrollment performance, dropout risk, or the probability of a site meeting a milestone.

Generative AI can summarize, draft, classify, compare, and answer questions using structured and unstructured information. It may support tasks such as creating an initial lay summary, comparing protocol versions, or finding relevant information across study documents.

Agentic AI can coordinate several steps, use different tools, and move work through a defined process. An agent might identify a missing document, retrieve the relevant requirements, draft a request, and route it for review.

The more independence the technology receives, the more clearly the workflow, permissions, review points, and escalation rules must be defined.

Is the Workflow Stable Enough to Improve?

AI cannot compensate for a process that every team performs differently and no one fully understands.

Before implementation, teams should map:

• The trigger that starts the workflow
• The information required
• The systems and people involved
• The decisions made at each step
• Common exceptions
• The output produced
• The person accountable for the result

This often reveals that the workflow contains outdated steps, duplicate reviews, or manual handoffs that no longer serve a clear purpose.

Standardizing the process does not mean removing every variation. Clinical trials require flexibility across countries, phases, therapeutic areas, and study designs. The goal is to define the main path and identify where legitimate exceptions occur.

That foundation makes it easier to determine what AI should perform, what should remain rules-based, and where people need to make decisions.

Is the Data Ready for the Use Case?

A model needs access to the information required by the workflow, with enough quality and context to use it correctly.

Clinical development data is often distributed across operational systems, documents, spreadsheets, vendor platforms, and local repositories. The same concept may be described differently across sources, while key information may be missing or out of date.

Data readiness depends on the use case. Teams should ask:

• Which data and documents will the AI use?
• Are definitions consistent across studies and systems?
• Can the technology access the information when it is needed?
• Is the source current and complete enough for the task?
• Can users see where the output came from?
• Are access rights and privacy requirements clear?
• Will the workflow capture corrections and feedback?

A narrow use case may not require an enterprise-wide data transformation. It does require a reliable set of inputs and clear limits on what the AI can access.

If the necessary data is not ready, that should affect the implementation plan and the confidence placed in the output.

How Much Risk Does the Output Carry?

The consequences of an incorrect output should shape the design of the workflow.

An AI-generated internal meeting summary carries a different level of risk from an eligibility recommendation, regulatory document, safety signal, or action that changes a study record.

Higher-risk use cases require stronger controls. These may include:

• Approved source restrictions
• Deterministic rules for critical requirements
• Required human review
• Confidence thresholds
• Automatic escalation of uncertain cases
• Complete audit trails
• Testing across representative scenarios
• Ongoing monitoring after deployment

Teams should evaluate both the severity of a possible error and the likelihood that it will be noticed. A mistake in a draft may be easy for a reviewer to catch. An incorrect classification that silently sends work down the wrong path may be harder to detect.

The amount of oversight should match the risk rather than applying the same review process to every AI output.

Where Should People Enter the Workflow?

“Human in the loop” is only useful when the human role is clearly defined.

Teams need to decide who reviews the output, what evidence that person receives, which decisions require approval, and what happens when the AI is uncertain.

Human involvement may take several forms:

• Reviewing every output before use
• Reviewing only higher-risk cases
• Approving actions above a defined threshold
• Resolving exceptions the AI cannot handle
• Auditing a sample of completed work
• Monitoring performance and emerging patterns

Early implementations may require broader review while teams build evidence and trust. Over time, reliable low-risk steps may move toward exception-based oversight.

People should concentrate on interpretation, unusual cases, and decisions that require clinical or operational judgment. Requiring them to reperform every automated step can remove much of the workflow’s value.

Will the AI Fit Into the Existing Work?

A useful AI capability can still fail if people must leave their normal workflow to use it.

Separate tools may require users to copy information between systems, re-enter context, monitor another dashboard, or remember when to consult the AI. These extra steps create friction and make adoption less likely.

The AI should receive information at the right point in the workflow and return its output where the next action occurs. That may mean integrating with a clinical trial management system, document environment, site platform, recruitment workflow, or analytics interface.

Teams should also consider whether the AI can support the variations that matter across studies. A patient-matching tool may need to work with different site data structures. A document workflow may need to account for country-specific requirements. A feasibility model may need users to adjust how factors are weighted.

Integration includes the operating process as well as the technology. Ownership, support, training, and issue resolution all need to be clear.

Can the Use Case Show Measurable Value?

A strong AI use case has an outcome the organization can measure.

Possible measures include:

• Time required to complete the workflow
• Number of manual steps or handoffs
• Review and rework effort
• Error or exception rates
• Time to identify a risk
• Site or user adoption
• Enrollment conversion
• Startup cycle time
• Staff capacity returned to higher-value work

The baseline should be established before implementation. Without it, teams may be able to show that the AI was used without showing that the workflow improved.

Usage is also different from value. A high number of generated summaries or completed queries may show activity, but the stronger measure is whether teams reached a useful decision faster, found issues earlier, or reduced unnecessary work.

Metrics should reflect the original problem. If the use case was selected to reduce site burden, internal sponsor productivity alone does not provide a complete measure of success.

How Should Teams Prioritize AI Use Cases?

A practical first use case usually combines high operational value with manageable implementation risk.

Strong candidates often have:

• A clearly defined workflow
• Frequent or significant operational burden
• Reliable and accessible inputs
• An output that people can evaluate
• A measurable baseline
• A clear owner
• A realistic integration path
• Limited consequences if the first version requires correction

Teams should be cautious about starting with a workflow that has unclear ownership, highly fragmented data, numerous undocumented exceptions, or an output that cannot be independently checked.

The first implementation should also teach the organization something reusable. A project that establishes governance, integration, validation, and monitoring methods can make later use cases easier to scale.

Choose the Workflow Before the Technology

Clinical operations teams have many possible uses for AI, from protocol development and feasibility to recruitment, monitoring, document generation, and trial oversight. Choosing among them requires more than ranking technologies by sophistication.

The strongest use cases begin with a specific operational problem and a clear picture of the current workflow. They match the technology to the task, use reliable data, define human accountability, fit into existing systems, and produce a result the organization can measure.

That discipline keeps AI focused on work that teams are ready to improve. It also creates a stronger path from a promising pilot to a trusted clinical trial workflow.

Continue the Conversation at SCOPE Summit Europe

Practical AI implementation, workflow redesign, data readiness, governance, and clinical trial optimization will be important areas of discussion at SCOPE Summit Europe. Leaders from across clinical research will explore how organizations can select, implement, and scale AI use cases that improve the work of study teams.

Learn more and register for SCOPE Summit Europe.