Clinical operations transformation in the perform-and-transform era
To deliver the speed, scale and reliability evolving pipelines require, clinical operations must overcome a formidable challenge: Sustain execution excellence today while fundamentally reimagining the way trials are run tomorrow.
Welcome to the perform-and-transform era of clinical operations, where accountability is expanding as pressures to do more with less intensify, and where AI is moving from a productivity aid to a core capability.
But first, here's what's driving the shift to a new era of clinical operations...
5 shifts that will define clinical development transformation in the perform-and-transform era
Five fundamental shifts in how clinical operations teams operate, make decisions and partner across the organization will define success in the perform-and-transform era. These shifts span accountability, operating models, talent and the role of digital and AI capabilities.
How well teams make these shifts will determine whether they can deliver consistent trial outcomes at portfolio scale.
Across all five shifts, AI is not a standalone capability but an enabling layer that allows new operating models and decision-making approaches to scale.
1. From executors to architects of trial delivery
Clinical operations must move from executing protocols as designed to shaping them earlier to improve feasibility, speed and reliability.
Current state → Future state
Overly complex and impractical clinical trial protocols are a top contributor to clinical development inefficiency, with direct impact on recruitment, retention and quality. Despite being on the hook to execute protocols, clinical operations teams currently have insufficient influence on protocol design.
In the perform-and-transform era, scientific ambition must be balanced with operational pragmatism. Clinical operations is uniquely positioned to bring this balance to protocol design by applying institutional knowledge, data and AI-enabled insights to assess feasibility trade-offs early, before execution risk is locked in. Within R&D’s pursuit of technical, regulatory and market success, clinical operations is best suited to define and own POpS—the probability of operational success, or the ability to consistently deliver trial data within speed, cost and quality constraints—across the clinical portfolio.
Why it matters
Used predictively, POpS provides a common lens—supported by data and AI-enabled signals—for assessing protocol risk and aligning feasibility, site strategy, patient engagement and analytics efforts around a single portfolio-level outcome.
Target state
Clinical operations has an equal seat at the table in early development strategy and protocol design, with clear accountability for improving portfolio-level POpS across clinical programs.
2. From study-by-study partnerships to sustained relationships
Clinical operations must expand from focusing on sites on a study-by-study basis in favor of building and maintaining relationships “above trial” and over time.
Current state → Future state
While many clinical operations organizations have begun to focus on site engagement within trials, long-term success will require understanding site needs and then partnering with sites for long-term mutual success. As competition among sponsors for sites intensifies, clinical operations must shift from relationships based on transactional delivery to ones based on sustainable, collaborative site partnerships. They can do this by simplifying processes, aligning incentives, improving the day-to-day site experience and, most importantly, understanding what sites need from the partnership.
Target state
Clinical operations has evolved site-facing roles into long-term partnership leads, with shared objectives, codesigned initiatives, streamlined startup and early operational input that improves delivery reliability for both sponsors and sites.
3. From role-based work to outcome-driven teams
Clinical operations must shift from narrowly defined, role-based work to teams organized around shared outcomes.
Current state → Future state
Clinical operations teams historically have relied on highly specialized roles that often operate in silos outside the core study team and are measured against role-specific objectives. Task orientation, standard operating procedures and detailed checklists have defined how teams work. This status quo reinforces cautious rule-following rather than proactive problem-solving. As trials grow more complex, this model limits flexibility and inhibits the ability to scale effectively.
In the perform-and-transform era, clinical operations is being asked to do more with less and to do it faster. Meeting this mandate requires greater agility and flexibility in how work is organized, necessitating a move away from continued role proliferation toward agile teams that can pivot as needs change.
Why it matters
Outcome-driven teams are better equipped to navigate complexity, reduce handoffs and respond to emerging challenges in real time. By organizing around outcomes rather than tasks, clinical operations can improve speed and coordination while empowering teams to contribute more directly to program- and portfolio-level goals.
Target state
Clinical operations is organized around outcomes rather than roles and supported by multidisciplinary professionals with a “major/minor” orientation. This means developing team members who combine deep expertise in one area with working knowledge of adjacent disciplines such as data management, biostatistics, patient safety and regulatory affairs. AI agents and chatbots will support these adjacent areas to accelerate learning and impact.
In this world, team members are empowered to solve problems beyond narrow role boundaries in service of program, study and transformation objectives.
4. From risk avoidance to risk-managed quality
Clinical operations must move from overly conservative, control-heavy approaches to quality toward proportionate, risk-based quality management focused on what is critical to outcomes.
Current state → Future state
Many clinical operations organizations have historically prioritized risk avoidance, often applying controls that go beyond even the strictest interpretations of regulatory requirements and guidance. This has led to rigid processes, a check-the-box mentality and a proliferation of controls that add burden without improving quality.
Data quality has historically been the primary lens through which risk is managed in clinical operations, making it the most visible—and most overcontrolled—expression of this broader risk-avoidance mindset. Take the continued use of 100% Source Data Verification (SDV) in some studies, despite empirical evidence that it does not meaningfully improve data quality. In contrast, evidence suggests that risk-based monitoring approaches can reduce error rates in critical data while lowering operational burden.
In the perform-and-transform era, quality must be managed through a risk-based lens that assesses study, process and vendor risks proportionally based on what’s truly critical to quality.
Why it matters
Risk-managed quality enables clinical operations to focus time, attention and resources where they have the greatest impact on patient safety and data integrity, reducing unnecessary burden while improving oversight of the risks that most threaten trial outcomes.
Target state
Clinical operations operates a portfolio-level, risk-based quality management approach that proportionally manages study, process and vendor risks based on what is critical to quality.
5. From AI-compelled to AI-embedded operations
Clinical operations must move from adopting digital and AI tools out of necessity to embedding them into core workflows that reshape how trials are designed, planned, executed and overseen.
Current state → Future state
The change is already happening with AI adoption as clinical operations professionals use digital and AI to support their work in ever-greater numbers. To fully realize the benefits, three things need to change.
First, processes will need to be updated to embed the use of AI agents and tools directly in workflows. Second, teams will need to continue to run proofs of concept in parallel to newly adopted workstreams as new AI and digital benefits are proven. Third, teams will need to adopt governance frameworks to measure the impact of AI initiatives, focus sponsor investments and ensure value realization.
Why it matters
When digital and AI capabilities remain layered onto existing processes, their impact is incremental and difficult to scale. Embedding AI into core workflows enables clinical operations to move beyond productivity gains toward more consistent, data-informed decisions across design, planning, execution and oversight.
Target state
Core clinical operations processes are reconstituted on a foundation of integrated digital and AI capabilities. Talent is cultivated or hired with digital-native skillsets, including the responsible use of AI in daily work. The impact of AI is measurable and transparent to clinical operations stakeholders and leadership, guiding continued investment and adoption.
Clinical operations transformation in the perform-and-transform era
Clinical operations teams today hold a dual, non-negotiable mandate: deliver operational excellence while also redesigning how trial delivery works in the future. Going forward, leaders will measure success not only by the execution of individual studies but also by how effectively teams shape delivery models, operating structures and decision-making at scale.
In this era, the primary constraint isn’t technological, it’s organizational. And misaligned roles, incentives and decision rights are largely responsible. Addressing these organizational misalignments requires rethinking how clinical operations organizations are designed.
Leaders should start with two principles:
One: Invert the traditional organization design sequence. Rather than starting with org charts and then retrofitting the talent, leaders should do the opposite: Define the capabilities clinical operations must deliver to make the five shifts the perform-and-transform era demands.
Two: Design the people and structure around desired capabilities. Align talent, skills and culture accordingly. Then, and only then, leaders can begin to define the structure, processes, governance and incentives needed to enable sustained performance and transformation.
Operationalizing perform-and-transform through capability-led design
ZS’s Organization rEvolution framework (Figure) provides a structured way to operationalize this shift. It starts by clarifying the capabilities required to deliver strategic ambition, then systematically aligns talent, culture, structure, governance and incentives around those capabilities. Rather than treating organization design as a one-time restructuring exercise, it treats it as a deliberate evolution anchored in performance outcomes and long-term transformation.
FIGURE: ZS’s capability-first framework for organizational evolution
In practice, this means designing explicitly for both present-day performance and future transformation. Here’s what that could look like in practice:
Perform: Most clinical operations roles are aligned with, and measured against, delivery outcomes for specific program or study goals—think timely database lock—and also support adjacent functions, such as regulatory submission readiness.
Transform: A smaller subset of roles is aligned with, and measured against, explicit transformation outcomes. Some of these roles should focus on enhancing the existing delivery engine, while some focus on reinventing the clinical trial operating model.
Organizations that design deliberately for both performance and transformation will be best positioned to deliver reliable clinical outcomes at portfolio scale.
ZS partners with clinical development leaders to define the critical capabilities required for the next era, diagnose misalignment across roles, decision rights and incentives, and design pragmatic roadmaps that evolve operating models without compromising near-term delivery. To explore where to start, connect with a ZS clinical development or R&D transformation expert.
