The Bottleneck in Biotech Isn’t AI Technology — It’s Trust
Trust is often a major barrier to companies integrating AI tools.
In an effort to optimize operations and maximize efficiency, many biotechnology and pharmaceutical companies are looking to integrate AI into their R&D workflows.
AI models can help R&D scientists to navigate large volumes of study data, flagging inconsistencies or outliers. AI tools can also help to write regulatory documentation. However, despite these promising applications, many companies are hesitant to adopt AI tools beyond running small pilot programs. The reason for this hesitancy is not so much because of any inherent flaws in the AI models they might have developed, but often, it is a matter of trust.
In the pharma and drug discovery industries, inaccuracies can have dramatic consequences; and AI tools are only as good as the data that is input into them.
To learn more about how AI tools are being used by biotechnology and pharma companies, how these companies can better manage their data to prepare for AI systems and what the future has in store for the sector, Technology Networks spoke with Sunitha Venkat, vice president of data services and insights at Conexus Solutions. In her role, Venkat works directly with life sciences firms to implement AI-enabled regulatory workflows.
What are some practical examples of how AI might be used in biotechnology and pharmaceutical companies?
Sunitha Venkat (SV):
AI is becoming a foundational enabler across the life sciences value chain, from discovery to commercialization. In drug discovery, AI can rapidly analyze molecular, genomic and clinical datasets to identify novel targets, predict compound efficacy and optimize lead candidates. In clinical development, it supports trial design optimization, patient cohort identification and early detection of safety or efficacy signals.
Beyond R&D, AI is driving significant transformation in commercial operations and marketing. By unifying HCP 360° touchpoints across sales, medical and marketing channels, AI helps identify high-value engagement opportunities, tailor personalized content and streamline next-best-action recommendations for field teams. Predictive models can optimize call planning, territory alignment and promotional spend allocation, enabling greater efficiency and stronger customer experiences.
In regulatory operations, pharmacovigilance and supply chain forecasting, AI automates repetitive reviews, accelerates insight generation and improves decision quality.
AI offers tremendous opportunities, but success depends on thoughtful implementation. Organizations should start small, prioritize data quality and foster cross-disciplinary collaboration. Over time, AI will become a natural extension of scientific workflows – helping life sciences companies bring innovative therapies to patients more efficiently.
AB:
Many organizations recognize the potential of AI to transform their workflows, but are hesitant to implement it in practice. In your experience, what are the biggest reasons why an R&D firm might hesitate to use AI tools?
SV:
Hesitation stems from a mix of trust, skill and structure gaps.
Firstly, on trust, scientists and clinicians are cautious about using black-box models when data lineage or model rationale isn’t clear. In terms of expertise, AI adoption requires collaboration between computational teams, regulatory experts and therapeutic scientists, which many organizations are still building. Finally, there is integration complexity – AI needs to fit seamlessly within validated, compliant systems (e.g., GxP environments).
AB:
How does data quality impact the accuracy and reliability of AI tools? What can firms do to improve data quality and build trust in AI outputs?
SV:
The accuracy, fairness and reproducibility of AI outputs are directly dependent on the quality of input data. Poor-quality data not only skews outcomes but can also amplify biases in clinical or scientific decision-making.
Life science firms can strengthen AI reliability by embedding data governance and lineage tracking early in their data lifecycle. This may include:
- Implementing Master Data Management frameworks for consistent entity definitions (e.g., molecule, indication, investigator).
- Using data governance tools to trace AI outputs back to source systems and transformations.
- Employing data validation and anomaly detection pipelines in ingestion processes.
- Establishing data quality key performance indicators, such as accuracy, completeness, timeliness, conformity, etc.
AB:
For organizations just beginning their digital transformation, what can be done to ensure their data is AI-ready?
SV:
AI readiness in life sciences begins with a solid and scalable data foundation that spans R&D, clinical and commercial domains. Organizations should establish a clear data strategy – defining critical data assets, ownership and stewardship – and unify them in a governed data platform that integrates research, trial, real-world and customer data.
Equally important is fostering cross-functional alignment among scientific, medical, commercial and IT stakeholders. Establishing an AI Governance Council helps to institutionalize this collaboration by setting guardrails for data quality, ethical AI use, model transparency and compliance. The council can ensure that AI initiatives are not siloed, but strategically prioritized to drive measurable business and patient impact.
Ultimately, being AI-ready means combining trusted, well-curated data with accountable governance and informed teams. When organizations treat data as an enterprise asset and embed AI into everyday decision-making, they can accelerate discovery, optimize clinical operations and personalize commercial engagement with confidence.
AB:
Looking ahead, how do you see the relationship evolving between scientists, data specialists, regulatory professionals and AI tools?
SV:
The future of life sciences will be defined by deeper collaboration between scientists, data specialists, regulatory professionals, business operations and AI tools. AI will serve as an enabler, not a replacement – helping scientists accelerate discovery, data experts ensure model integrity and explainability, and regulatory teams streamline documentation and compliance reviews.
As AI becomes embedded across R&D, clinical and commercial operations, cross-functional collaboration will be the norm. Establishing an AI Governance Council will help align these groups under a shared framework by defining standards for data quality, ethical AI use and transparent model validation.
In this ecosystem, human expertise and AI intelligence will complement each other. Scientists will focus on interpretation and innovation, data teams on reliability and scalability and regulators on accountability and trust – turning AI into a true partner in advancing science and improving patient outcomes.
