An Interview With...
Kim Boericke
CEO
Veristat
Female CEOs remain significantly underrepresented in In the U.S. life sciences industry, with recent data from 2023-2025 showing figures around only 8-12% for public companies, though some reports cite even lower figures for startups. Overall there has been a marked decline in leadership roles despite women making up nearly half the overall workforce in some areas. More specifically, about 70% of employees at CROs are female with this dropping sharply to less than 25% at the senior leadership level.
Veristat’s new CEO, Kim McLean Boericke, is one step closer to changing the tide in representing the small fraction of companies led by women and is now the only female with the highest job among the top-8 clinical research organizations (CROs). Boericke is ready for the job with a new mission for the mid-sized CRO. She aims to double-down on its focus on data and analytics and increase investment in strategic uses of artificial intelligence (AI) and automation to accelerate and improve trial execution.
How has Veristat’s mission evolved in recent years to meet the changing needs of sponsors and the broader clinical research landscape?
Veristat has evolved in direct alignment with our clients. Most notably, in the last six years, we have made great strides in the depth and breadth of global regulatory services. We offer biotech organizations comprehensive or advanced capabilities that they may not have in-house while bringing larger pharmaceutical companies’ expert regulatory strategy designed to drive products to approvals.
Our regulatory services couple with the foundation of our core business, which is focused on data – insights, analytics, and biostatistical consulting. Additionally, these capabilities inform our medical writers to round-out a diverse team that drafts approvable regulatory submissions. We support sponsors through the full development lifecycle for even the most complex trials – from protocol design through trial execution and submissions.
With AI increasingly influencing clinical trial design and management, what role does Veristat see for this technology in improving efficiency and data quality?
We use AI throughout our daily work to deliver high-quality data faster and better insights that accelerate trial execution. AI is key to making that happen at every stage – from early targeting to designing the right protocol for the patient population and expediting data collection and analysis with faster outputs to guide earlier and better decision-making at each phase of the drug development life cycle.
Over time, AI will allow teams to spend more time on the “thinking” part of their job, and less on the manual, “hands and feet” part of their job. Of course, we still have serious challenges with patient enrollment and retention where AI can help, but we still need clinicians that are engaged, a proper value proposition that motivate patients to commit to participation, and strategies to retain patients for the duration of the study.
If we can shorten patient enrollment with better study design and site selection, which continues to be the biggest trial delays, then we will be able to measure the impact AI has on the clinical trial. AI will also start to improve efficiency and speed in data collection, cleaning, and analysis leading to greater speed and efficiencies in the full drug development lifecycle.
What are some of the biggest operational or ethical challenges CROs face when integrating AI tools into trial planning and execution?
Today’s biggest ethical challenge with AI in trials facing CROs is protecting data privacy and sponsor confidentiality.
AI’s tentacles extend endlessly to collect both public and semi-private data, so companies need to establish the right rules and firewalls to protect data and prevent confidential information from one sponsor’s trial to inform another sponsor’s trial via an AI agent. CROs have decades worth of cross-company data. When AI is introduced, CROs must have ways to carefully parse that information out so that we’re only leveraging what should be used to inform sponsors.
Operationally, one of the biggest challenges with AI is ensuring consistency. When one person asks AI a question, the way that person asks the question could be slightly different than the way another person does….which means, there will be different responses to the same basic question. In operations, however, everyone needs to receive the same answer every time. It cannot deviate. Since we are in a highly regulated industry, we need to follow the process the right way without exception– but this is sometimes difficult with AI.
How do you balance the use of AI-driven automation with the need for scientific and regulatory rigor in clinical decision-making?
AI should be used as AI-enabled workflows, rather than fully autonomous agentic workflows so that we do not remove humans from the process. AI provides people with data and tools so they can work more efficiently and effectively but we still need a human in the loop.
In what ways are Veristat leveraging data analytics or predictive modeling to support patient recruitment, trial monitoring, or risk-based management?
Before we even get to patient recruitment, Veristat leverages analytics to determine protocol feasibility. Is there a matching patient population? Because sponsors are trying to standardize the data being collected for their trials, they are also creating protocols that demand a very standardized patient pool, which often does not exist or does not have enough qualified patients to enable a powered analysis. The human body is not standard issue. We help sponsors right from jump street by leveraging analytics tools like AI to determine whether the protocol-specified patient population is possible, and if not, we use analytics to change the criteria to make the protocol more feasible.
Next, we get into AI for patient enrollment and site identification. We use AI technology to analyze both company data (i.e., where we have run studies before; were they successful?) and public data to identify sites in areas with the right patient population for this trial.
AI is also critical for data cleaning and data monitoring. For instance, we can standardize how we look at risk-based monitoring and see where the risks are in the data, highs and lows, outliers, missing data, and site performance. This enables a more targeted visit for the monitor and greater focus on patient recruitment, training, and other more value-added tasks. This same data can be used by medical monitors to see the “full” patient, rather than just a series of pooled data points across site visits. AI can flag early safety signals or any risks on the study.
From one set of AI tools layered on top of EDC environments, you now have standardized the way teams work across multiple areas of the organization, increasing efficiency and effectiveness. When everybody’s looking at the same data, no one repeats work, or re-queries, or throws something over the fence to the next group to act upon.
How is the emergence of AI reshaping the skill sets CROs need in their workforce—both in data science and traditional clinical operations?
Many sponsors say that they are not even talking to someone for a new job opportunity if they don’t understand how to use AI. It will be a prerequisite for the next generation to be technically savvy and know how to formulate the right questions to leverage AI in their day-to-day jobs.
And this will change the profile of key team members. For instance, historically we viewed clinical trial monitors as clinically focused – nurses, medical doctors, PhD graduates with biology or chemistry degrees. Now, AI can be used to inform clinical research associates (CRAs) on medical information so we may no longer need a PhD or registered nurse but someone who is more technically proficient.
On the other hand, data professionals will need to have a greater medical understanding so that they can ask AI the right questions, in the right way. In fact, the data cleaning process will become more automated, essentially, changing the role of the data manager. In the near future, the role of CRA and data manager may merge into one. This concept was explored about 20 years ago when EDCs hit the market, but it never materialized. When we see these roles converge a bit more, expect other roles to emerge – such as an AI Manager.
Looking ahead, how do you see partnerships between sponsors, CROs, and technology firms evolving as AI becomes more deeply embedded in the clinical trial ecosystem?
Technology has been layered on top of other systems and workflows and not incorporated within them for years. For example, electronic patient-reported outcomes (ePROs) are typically layered on top of existing EDC systems, so the cost ends up building, rather than decreasing. The cost never seems to normalize out it is usually additive.
Sites today typically work with eight to 12 different vendors for each study they are participating it at their hospital or clinic. Sites must log in, input their data for that vendor into that environment, and then must do it again into another environment and so on. The burden is extensive for both the site and now the patient too. These additive burdens end up costing more as sites need to add additional specialized resources and administrative costs.
We need to work together to integrate these systems or create and use a standardized box of tools. It helps sites, CROs, and sponsors because then we can aggregate all the data together and make faster decisions. The impact of integrated systems will not only save time but decrease cost and trial burden.
Unfortunately, we currently have too many choices and technology continues to evolve. And each sponsor has its own favorite technology stack that allows them to run their own trials in addition to outsourcing them to CROs. As such, CROs wind up with multiple technology stacks to maximize the potential sponsor customers.
If we can figure out a way to leverage AI to help standardize some technology, we could radically reduce trial complexity and costs, speed trials, and enable sites to spend more time focused on treating patients rather than data entry for different environments. In this ideal, technology is a win-win solution for every stakeholder in clinical research.