Clinical trial innovation hampered by change fatigue and skills gaps, survey finds

Diane Lacroix, Vice President of Clinical Data Management, eClinical Solutions

A new survey from eClinical Solutions reveals that biopharma organizations are still grappling with the complex realities of data innovation, AI implementation, and outsourcing challenges in clinical trials.

Diane Lacroix, vice president of clinical data management, shared her insights with Discover Pharma, highlighting a growing maturity in how the industry approaches transformation – and where many are still falling short.

According to Lacroix, innovation inertia is affecting a majority of biopharma companies. “Nearly three in four industry leaders report their organizations are struggling to innovate their data strategies and processes,” she said, referencing the 2025 Industry Outlook Survey conducted by eClinical Solutions. While there isn’t a single reason for the stagnation, common themes emerged.

“Even when the right technology is in place, that’s only the beginning.”

“We see resistance to change, difficulty proving ROI, and resource constraints consistently come up,” Lacroix explained. Forty-five percent cited change resistance, while 50% reported budget restrictions that prevent innovation from becoming a priority.

“Even when the right technology is in place, that’s only the beginning,” she said. “The next step is ensuring you have the people and skills to realize the value of those tools.”

A lack of internal alignment further complicates transformation. “We’re seeing internal departments too siloed, teams without the necessary skills, and a mismatch between expectations and delivery when it comes to outsourcing partners,” Lacroix added.

When it comes to artificial intelligence, initial excitement appears to have cooled. “Last year, expectations were sky-high,” she noted. “But as organizations deepen their understanding, we’re seeing a more measured, practical approach emerge.” While 80% of respondents were exploring AI in 2024, that figure has now dropped to 56%, with 28% reporting they have no plans to adopt AI at all.

This isn’t necessarily a sign of failure, Lacroix emphasized. “The drop reflects a shift from curiosity to realism,” she said. “Most respondents now understand that integrating AI isn’t just a tech decision—it’s a process decision.” According to the survey, 98% of those using or considering AI reported facing challenges, with regulatory uncertainty, training requirements, and proving ROI among the top issues.

“There’s still no standard approach to using AI across organizations,” Lacroix said. “Everyone is figuring out the foundation – compliance, data quality, infrastructure – before truly operationalizing it.”

“Cycle time is the new currency in drug development.”

Despite the hurdles, she sees significant potential for AI in areas like real-time data monitoring, risk prediction, and predictive modeling for patient recruitment.

“Cycle time is the new currency in drug development,” Lacroix pointed out. “We’re not at the stage where there’s a single ‘right’ path, but there’s a growing awareness of the need for strategic AI adoption.”

She also called out less commonly used applications – such as audit-trail reviews, data redundancy management, and patient stratification – as areas ripe for further exploration.

As for the industry’s increasing reliance on outsourcing, the survey shows 75% of organizations are outsourcing some or all of their clinical data management. Yet many say their partners aren’t keeping up. “There’s a clear disconnect between the complexity of today’s clinical data and traditional outsourcing capabilities,” Lacroix said.

“Outsourcing partners need to level up—meeting timelines and delivering insights in real time is no longer optional.”

She cited data showing that many vendors fail to meet basic expectations, such as hitting timelines, delivering high-quality outputs, or assigning experienced personnel. Even more concerning, 80% of outsourcing respondents said their partners are falling short when it comes to real-time data insights – a top priority in the survey.

“Outsourcing partners need to level up,” she said. “Sponsors should be asking how their vendors are transforming data delivery across people, process, and technology. It’s not enough to offer support – they need to show how they’re actively adapting to today’s demands.”

For sponsors, she advised looking for outsourcing models that prioritize data, not just monitoring. “Ask how they handle emerging data types, how they measure success, and how they’re preparing for the future,” she said.

When it comes to modernizing clinical data strategies in risk-averse organizations, Lacroix recommended a phased, goal-oriented approach.

“Start by setting specific transformation goals and launch pilot projects that focus on high-impact areas,” she said. “Bring in multiple teams to foster collaboration and measure outcomes closely.”

She said that innovation shouldn’t be viewed as a single leap. “Scaling up doesn’t mean you jump in at the deep end,” she explained.

“It’s about learning from small wins, investing in training, and adjusting based on what works.”

Skills gaps remain a persistent problem. “T – he survey showed that not having enough people or the right skills is a major barrier,” Lacroix said.

“Continuous upskilling is essentialnot just to meet today’s needs, but to prepare for the increasing complexity of clinical data.”

“Innovation isn’t a single leap—it’s a series of small wins that build confidence and capability.”

Lacroix also advocated for risk-based approaches like RBQM (Risk-Based Quality Management), which she described as a “significant mindset shift.” Implementation challenges include change management, regulatory uncertainty, and infrastructure limitations – but a phased model can help.

“Begin with Critical to Quality (CTQ) factors and basic risk protocols,” she suggested.

“Then layer in oversight and centralized monitoring. This staged approach helps build confidence while laying the groundwork for broader RBQM adoption.”

Finally, she returned to the role of AI – not as a silver bullet, but as a strategic lever. “Organizations must go beyond incremental improvements,” Lacroix said.

“To harness AI’s full potential, you have to redesign workflows and embed the technology across the entire data lifecycle. The opportunity is real. With the right execution, AI can shorten cycle times and bring therapies to patients faster.”