Janice MacLennan, founder and CEO, Nmblr, answers the question: can AI bring drugs to market faster?
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The pharmaceutical industry is under growing pressure to accelerate drug development. Global prescription drug sales are projected to exceed $1.9 trillion by 2027, driven by rising demand and intense competition. Speed-to-market increasingly determines who secures patents, sets treatment standards, and builds early trust with prescribers and patients.
Challenges, however, are mounting. Aging populations and chronic disease prevalence are driving demand, while investors expect faster development cycles and quicker returns. At the same time, molecular complexity, stricter regulatory oversight, and requirements for robust clinical evidence, real-world data, and post-market surveillance continue to slow approvals. Adaptive trial designs, decentralised studies, and innovative platforms like mRNA show promise, but operational hurdles remain.
Artificial Intelligence (AI) is emerging as a critical tool for navigating these pressures to get drugs to market faster. And while AI undoubtedly has the potential to play a transformative role, it must be approached with caution and utilised in a supportive capacity. People remain key to competing in today’s drug development landscape and it’s vital that teams move as one to make timely decisions and develop confident, powerful strategies that ensure drugs are delivered successfully, and to a receptive market.
Approach with caution
AI is reshaping pharmaceutical R&D and operations - from target discovery and preclinical studies to clinical trials, manufacturing, and post-market surveillance. Machine learning can optimise drug design, predict molecular interactions, streamline clinical protocols, and improve manufacturing quality by detecting inconsistencies in batches or processes. AI also aids regulatory compliance by tracking production, monitoring adherence to standards, and simplifying documentation for approvals. These applications barely scratch the surface of AI’s potential to accelerate development and enhance efficiency.
Yet risks remain. Bias in training data can lead to inequitable outcomes. Over-reliance on AI can erode human critical thinking and accountability. And AI’s ‘black box’ nature can complicate compliance with transparency requirements; making it difficult to understand how decisions are made.
AI also demands large volumes of sensitive patient and operational data, raising privacy and security concerns. Systems require ongoing validation, monitoring, and updates, alongside significant investments in technology, infrastructure, and specialised talent. Workforce challenges - skills gaps and potential role automation - can generate internal resistance, affecting adoption and organisational alignment.
A shift in perspective
To optimise use of AI and mitigate risk, its perception must shift from one of decision maker to collaborative partner. This means recognising AI’s ability to stimulate creativity and augment human judgement, through provision of data-driven insights, whilst acknowledging that it lacks contextual and ethical reasoning.
When we appreciate that AI requires human oversight to deliver value, we in turn acknowledge that it is people who can make the biggest difference in expediting time-to-market. AI can support but final decisions must involve clinicians, researchers, and commercial teams to ensure safety, efficacy, and market relevance. For example, AI-assisted platforms in drug discovery or precision medicine can identify promising compounds, but clinician input is essential to interpret biological context, refine trial protocols, and align therapies with patient needs.
Harnessing AI’s supportive value to accelerate innovation depends on teams working seamlessly together. The challenge that remains then is how to enable people working across functions, geographies, and time zones to come together to make timely decisions and develop confident, powerful strategies that ensure drugs are delivered not just quickly but successfully, and to a receptive market.
Beyond AI
Pharma companies must pair AI’s efficiency with human expertise to meet ambitious timelines and deliver patient-centric therapies to market. AI alone cannot overcome slow decision-making, miscommunication, siloed workflows, or weak strategy - critical factors that can delay launches and waste investment. Harnessing bright ideas, bringing together people’s experiences no matter their job function, making quick and effective decisions – all these steps cannot be forgotten. To enable this, company leaders must start with their people by providing pathways that enable participation from the best thinkers to offer diverse insights that drive smarter decisions.
Leaders must empower teams - local, global, or virtual - to collaborate seamlessly, ensuring high-stakes decisions are made collectively and efficiently. Platforms that support unified teamwork can eliminate friction, maintain alignment, and increase accountability by connecting insights, goals, and actions across the organisation. By fostering shared purpose and coordinated action, these tools enable pharma teams to make more confident, informed, and strategic decisions, accelerating the path from innovation to patient impact.
Cultivating a successful co-existence
Can AI bring drugs to market faster? Not on its own. AI is not a silver bullet, but it can add value as a collaborative tool that amplifies human judgment. The true bottleneck in bringing new therapies to patients faster lies in people, and the way they communicate and operate as part of a team.
To empower cross-functional, multi-location teams, barriers—geographical and departmental—must be removed. Platforms that enable collaboration allow teams to uncover insights, build shared visions, overcome obstacles, define confident strategies, and align actions across functions and markets, enhancing agility and efficiency along the way.
The future of pharma innovation lies in augmented intelligence, where AI enhances human potential without replacing it and where teams are supported to bring new drugs to market in the right shape, at the right time, and delivered to a receptive market.