If you have managed a clinical trial, you know the feeling: everything seems under control, and then suddenly it’s not. A wave of queries hits your inbox. Data review meetings stretch into three-hour marathons. Your monitoring team is drowning in spreadsheets. And somewhere, a critical protocol deviation sits unnoticed in a site’s source documents.

These are not isolated incidents. They’re predictable bottlenecks that slow down clinical trial workflows across the industry. The frustrating part? Most of these delays aren’t caused by complexity – they’re caused by volume. Too much data, too many manual checks, and not enough time to handle it all.

Agentic AI is emerging as one solution to these workflow challenges. The technology handles routine, repetitive tasks while clinical teams focus on decision-making and oversight. This article outlines five operational bottlenecks frequently encountered in clinical trials and the role agent-driven systems can play in reducing them.

1. Query Resolution Delays That Pile Up Week After Week

For many clinical data teams, the real struggle isn’t following protocols – it’s managing the constant flood of queries. Day after day, you’re buried in missing values, inconsistent dates, and data discrepancies. Each one demands manual review, slowing everything down. This data review burden impacts every stage of the trial lifecycle.

The numbers are sobering: addressing just one query can take up to 23 weeks, and one in five queries get resubmitted¹. For a trial with 200 subjects, you’re looking at 3,000 to 10,000 queries, with costs ranging from $28 to $71 per resolution². Research shows that 51% of queries are linked to anomalies in dates, signatures, or data legibility, while 43% relate to absent source data¹.

Now imagine if an intelligent assistant could flag inconsistent data the moment it appears—before it ever lands in your inbox. Multi-agent AI systems are making this possible by detecting and reporting anomalies in real time, keeping data review continuous and drastically reducing backlogs.

Instead of reacting to errors weeks later, teams can now stay proactive—reviewing cleaner data, making faster decisions, and maintaining oversight without the endless cycle of manual corrections.

2. Manual Data Review That Consumes Your Best Resources

Clinical data teams have long dealt with slow, manual processes checking data line by line, reconciling mismatches across disconnected systems. This data review burden not only delays trials but also raises the risk of errors and exhausts valuable team resources.

The typical late-stage protocol now collects 3.6 million data points—three times the number collected a decade ago³. Your senior medical reviewer shouldn’t be spending hours scanning through normal lab values to find the one result that actually matters. These challenges show that manual data management just doesn’t scale.

Think of Agentic AI as a team of smart digital assistants working together to support clinical trial operations. Each one can understand goals, make decisions, and carry out tasks on its own—like a virtual team member.

Agentic AI frameworks can continuously assess evolving data scenarios, producing composite “quality scores” and surfacing exceptions automatically.

MaxisAI’s Verticalized Agentic AI Platform for Clinical Trials brings these capabilities together into a unified workspace for automated data cleaning, mapping, and validation—allowing experts to spend more time applying medical judgment rather than chasing data inconsistencies.

3. Monitoring Efficiency That Scales Poorly

Anyone who has managed site monitoring knows how demanding it can be. The travel, the data checks, the constant coordination—it’s essential work, but often exhausting and reactive. Since 2020, the number of registered clinical trials has surged by more than 30%⁴ yet monitoring practices haven’t evolved at the same pace. Too often, issues are still discovered only after they’ve already caused delays or data inconsistencies.

Today, advanced oversight platforms are helping trial leaders move from reactive to proactive monitoring—offering real-time visibility into enrollment, protocol compliance, and data quality across sites.

For example, Maxis AI’s DTect AI illustrates how agentic AI can support continuous oversight—calculating site-level “Quality” and “Risk” scores, and enabling smarter, data-driven allocation of monitoring resources. The goal isn’t to replace human insight but to amplify it, so monitors can focus on meaningful interactions and early intervention where it matters most.

4. Protocol Deviation Management That Happens Too Late

Imagine spotting a major protocol deviation months after it happened—maybe during a monitoring visit or a regulatory inspection. Frustrating, right? It’s not about a lack of diligence; the real challenge is catching deviations early when visibility across systems is limited.

Research indicates that 70% of respondents believe unplanned mid-study adjustments are the most significant reason for trial delays⁵. By the time a deviation is identified and assessed, the pattern may have been repeated at multiple sites.

Integrated oversight and anomaly-detection tools are helping close that gap—reducing the time between deviation and corrective action. Site Copilot, for example, demonstrates how real-time data integration and intelligent alerts can flag unusual patterns early, enabling teams to act before issues escalate. It’s a move from documenting deviations late to preventing them early.

5. Cross-Functional Communication Gaps That Delay Decisions

It’s a familiar operational scenario: the biostatistics team spots a troubling trend, but by the time the clinical operations lead receives the flagged issue, the data management team has already moved on to another priority and the site team remains unaware. Delays mount simply because critical information lives in disconnected systems, schedules don’t align, and stakeholders lack shared visibility.

Research shows that silos between Data Management, Biostatistics, and other teams continue to slow decision-making across functions⁶.

Unified analytics platforms help bridge these gaps. SMART Optimizer, for instance, uses causal AI and predictive analytics to unify trial insights—identifying root causes of anomalies, running what-if scenarios, and supporting real-time decision-making to improve study outcomes.

Making the Shift: From Manual to Intelligent Workflows

The beauty of addressing these bottlenecks with Agentic AI is that you don’t have to fix everything at once. Think about your team for a second—what’s the process that causes the most headaches? Is it query resolution, endless data review meetings, or site monitoring inefficiency? Start there. Try implementing agents in that area, see how it works, tweak as needed, and then expand. Technology shines when it’s solving real, specific problems you actually face every day.

One of the biggest changes Agentic AI brings is how it reshapes the role of clinical data teams. Earlier, teams spent most of their time fixing problems after they happened. With agent-driven trial operations, many issues are caught early, so teams can focus more on oversight than cleanup. This fundamental shift in workflow helps teams work faster, smarter, and more closely together while significantly reducing the data review burden that has long plagued clinical operations.

Your team’s expertise remains irreplaceable. Clinical judgment, patient safety decisions, regulatory strategy—these require human intelligence and experience. But the repetitive, time-consuming work that keeps your experts from exercising that judgment? That’s exactly what Agentic AI is designed to eliminate.

Key takeaways:

• Query resolution accelerates when AI detects issues in real-time rather than weeks later
• Data review shifts from exhaustive manual checks to exception-based workflows, dramatically reducing data review burden
• Continuous monitoring replaces periodic site visits with proactive risk detection
• Protocol deviations are prevented through early intervention instead of late documentation
• Agent-driven trial operations dissolve cross-functional silos when teams work from a unified data foundation

The question isn’t whether your trials have operational bottlenecks. They do—everyone’s does. The question is whether you’re ready to remove them.

Want to see how agent-driven workflows can make your next trial smoother? Let’s start the conversation – just drop a “Hi” at connect@maxisit.com

References:

1. (2025). Query management strategies for clinical trial data. https://medrio.com/blog/query-management-in-clinical-trials/
2. Law, S. (n.d.). Clinical data quality & query resolution costs. LinkedIn. https://www.linkedin.com/pulse/clinical-data-quality-query-resolution-costs-steven-law
3. Applied Clinical Trials. (2021). Characterizing clinical data management challenges and their impact. https://www.appliedclinicaltrialsonline.com/view/characterizing-clinical-data-management-challenges-and-their-impact
4. National Center for Biotechnology Information. (2024). Resource management and capacity planning for clinical trial sites. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC12279055/
5. Clinical Research News Online. (2022, July 1). Clinical data management: What are the key challenges and how to navigate them? https://www.clinicalresearchnewsonline.com/news/2022/07/01/clinical-data-management-what-are-the-key-challenges-and-how-to-navigate-them-
6. Kasturi, J., Brown, A. P., Brown, P., Madhavan, S., Prabakar, L., & Wally, J. L. (2014). Interconnectivity of disparate nonclinical data silos for drug discovery and development. Therapeutic Innovation & Regulatory Science, 48(4), 498–506. https://doi.org/10.1177/2168479014531421