The ideal scenario involves a collaborative approach where the industry works closely with regulators to co-create policies that balance innovation with compliance, ensuring that AI and automation drive positive health outcomes without compromising ethical standards, says Sudipta Ghosh, Partner, PwC India.  

What regulatory challenges do you see in the adoption of AI and automation in the pharma industry?

Regulatory challenges in AI and automation within the pharma industry primarily revolve around compliance, data security, transparency, and ethical considerations. The industry is heavily regulated, with agencies like the FDA (U.S.), EMA (Europe), and others requiring strict adherence to standards for patient safety, data integrity, and validation processes. Integrating AI and automation into this framework poses several challenges:

·  Data privacy and security: Given that AI systems rely on vast datasets, often containing sensitive patient information, ensuring data privacy and compliance with regulations like GDPR (Europe) or HIPAA (U.S.) is crucial. AI algorithms need to handle this data securely while adhering to data anonymisation and encryption protocols.

·  Validation and explainability: Regulatory bodies demand transparency in the decision-making process of AI algorithms, which requires the technology to be explainable and interpretable. Black-box AI models are often unsuitable for pharma because their decision-making processes can’t easily be justified. There is a need for robust validation methodologies to ensure these systems' accuracy and reliability.

·  Ethical AI and bias: AI algorithms must be designed to avoid bias, ensuring that they do not make decisions that could adversely affect specific patient groups. Regulatory guidelines are now pushing for frameworks that minimise bias in AI models, especially in clinical trials and patient treatment recommendations.

·  Compliance with evolving standards: AI technology evolves rapidly, outpacing the development of regulatory standards. This gap creates uncertainty for pharmaceutical companies on how to deploy AI innovations while staying compliant. Constant engagement with regulators to shape policies that accommodate these technological advances is critical.

What role do you think government funding should play in advancing AI and automation in pharmaceuticals?

Government funding plays a pivotal role in catalysing AI and automation within the pharmaceutical sector. Investments in this area can lead to breakthroughs in drug discovery, manufacturing efficiencies, and healthcare accessibility. Key areas where government funding could have the most impact include:

·  Research and Development (R&D): Government-backed grants and funding initiatives can support collaborative R&D projects between academia, AI tech firms, and pharma companies. This would accelerate innovations in AI-driven drug discovery and personalised medicine, reducing the time and cost involved in bringing new drugs to market.

·  Infrastructure development: The adoption of AI and automation requires significant investment in digital infrastructure, data centers, and high-performance computing. Government funding in this area can lower the entry barriers for smaller companies and startups in the pharmaceutical ecosystem.

·  Training and upskilling: To address the growing need for AI and data science talent in pharma, government-sponsored training programs and AI research centers can play a vital role. This would not only prepare the workforce for future challenges but also ensure the industry is equipped with professionals who understand both pharma and AI technologies.

·  Public-Private Partnerships (PPP): Governments should encourage PPPs where tech companies and pharmaceutical firms collaborate on AI and automation projects. These collaborations can de-risk investments for private players while ensuring public health interests are safeguarded.

What are the potential implications of AI and automation on quality assurance, drug pricing, and accessibility?

The implementation of AI and automation in the pharmaceutical sector has far-reaching implications for quality assurance, drug pricing, and accessibility:

·  Quality Assurance (QA): AI-powered predictive analytics can significantly enhance QA by predicting potential defects in drug manufacturing processes and ensuring consistency in production. Machine learning algorithms can analyse large datasets to detect anomalies in real time, reducing the risk of human error and ensuring that only high-quality products reach the market.

·  Drug pricing: Automation in production processes can lower manufacturing costs by increasing efficiency and reducing waste. AI algorithms can optimise supply chains, manage inventories, and predict demand, all of which contribute to cost reductions. These savings can be passed on to consumers, potentially lowering drug prices and making essential medications more affordable.

·  Accessibility: AI can play a transformative role in increasing the accessibility of drugs, especially in developing countries. By optimising distribution networks and predicting the demand for specific drugs in various regions, AI systems can ensure that medications reach underserved areas more effectively. Additionally, AI can aid in developing personalised treatment plans, improving patient outcomes and healthcare delivery in remote regions.

How do you view the balance between industry self-regulation and government oversight?

Balancing industry self-regulation with government oversight is crucial in driving AI adoption in pharma without stifling innovation. Here's a nuanced perspective:

·  Self-regulation: The pharmaceutical industry should proactively adopt ethical AI principles and best practices to guide its AI and automation initiatives. Creating industry-wide standards for AI model development, validation, data handling, and risk management can foster trust among stakeholders and accelerate regulatory approval processes. This proactive approach also positions companies as responsible innovators, leading the way in the ethical use of AI.

·  Government oversight: While self-regulation is essential, there must be a robust framework of government oversight to ensure patient safety, data integrity, and fair market practices. Regulatory bodies should focus on creating flexible, outcome-based guidelines that encourage innovation while setting clear boundaries to prevent misuse. Governments could also set up regulatory sandboxes for pharma companies to test AI solutions in controlled environments, allowing for faster iterations and refinement of their technologies.

The ideal scenario involves a collaborative approach where the industry works closely with regulators to co-create policies that balance innovation with compliance, ensuring that AI and automation drive positive health outcomes without compromising ethical standards.

What strategies do you recommend for addressing workforce displacement?

Workforce displacement is a significant concern as automation and AI technologies become more pervasive in the pharma industry. The key to addressing this challenge lies in a combination of proactive workforce development and creating new job roles:

·  Reskilling and upskilling programs: Companies should invest in comprehensive training programs to help their workforce transition into roles that require more analytical and technical skills. Reskilling initiatives should focus on areas such as data science, AI model management, and digital operations in pharmaceutical settings. Government incentives for reskilling programs can further enhance these efforts.

·  Human-AI collaboration: Rather than replacing jobs, the focus should be on augmenting human capabilities with AI tools. For example, automating routine tasks in clinical trials or drug manufacturing allows professionals to focus on strategic decision-making, complex problem-solving, and innovation.

·  Creating new job roles: The rise of AI in the pharma industry will lead to new job roles that didn’t exist before, such as AI ethics officers, data governance specialists, and automation strategists. Companies should work with educational institutions to align curricula with industry needs, ensuring a steady supply of talent for these emerging roles.