AI in Life Sciences: Is your organisation ready?

AI can revolutionise life sciences, provided it is built on the right foundations. Faster drug discovery, personalised medicine, and predictive analytics – it’s all within reach. But there’s a catch. Too many companies rush in without the necessary groundwork.

AI is a broad term – there’s a big difference between using AI to summarise meeting notes and using it to fundamentally improve your business model. In life sciences, the applications of AI span the entire value chain. However, not all areas are equally primed for AI adoption. Balancing the tension between adhering to regulations and pushing for market dominance is key to unlocking real value and regulatory compliance.

Bridging vision and execution

While executive enthusiasm for AI is high, many organisations struggle to translate vision into execution. Leadership may be aligned on the strategic importance of AI, but without clear communication, accessible tools, and organisation-wide enablement, the gap between ambition and reality widens. Establishing cross-functional communication channels, investing in comprehensive training, and ensuring AI tools are usable across all levels of the organisation are essential to turning strategy into sustained impact.

Before jumping on the AI train, here are five organisational elements you need to get just right.

Laying the right foundations for AI

Data infrastructure & manufacturing realities

AI is only as good as the data feeding it. Without a solid data infrastructure, AI initiatives may struggle to deliver accurate and actionable insights. However, 65% of Irish organisations lack an AI strategy, largely due to outdated or siloed data systems.

Scattered datasets lead to poor decision-making, while weak governance creates compliance risks – especially in highly regulated industries like life sciences. A clear strategy ensures AI investments align with business objectives rather than becoming costly experiments.

Similarly, in manufacturing, there is often a large gulf between corporate AI strategies and the realities on the manufacturing floor. Many operations are still in the process of digitisation or implementing electronic batch records. But without a fully contextualised, real-time data process flow, AI cannot deliver value. In some cases, organisations must prioritise completing in-flight digital projects rather than abandoning them for new AI initiatives. In others, a step-back approach is needed to define the digital landscape before AI adoption.

To build a strong data foundation, life sciences organisations should be able to:

• Establish robust, integrated data pipelines that ensure consistency and accessibility.
• Adopt data governance frameworks such as master data management and data lineage tracking.
• Use modern integration tools that bridge legacy systems with cloud-native platforms.

AI talent & change management

AI adoption requires attention to the human element. In Ireland, 50% of employees in multinational corporations have access to AI tools, compared to just 38% in local companies. Moreover, workers at Irish organisations are nearly four times more likely than their multinational counterparts to have missed out on digital skills training in the past two years.

Effective change management is crucial when introducing AI platforms or tools. For instance, in the life sciences industry, it's assumed that for every $1 spent on technology, $5 is required for change management to successfully drive capability building, adoption, buy-in, and value capture over time. Without structured change management, organisations face execution gaps, employee resistance, and wasted resources, limiting AI's transformative potential.

To maximise adoption and value capture, life sciences organisations should be able to:

• Engage stakeholders early and often to shape AI solutions collaboratively.
• Provide role-specific training and upskilling to build digital confidence.
• Use phased rollouts and pilot projects to build trust and demonstrate value.

Communication & alignment

Transparent, ongoing communication is essential to drive buy-in and reduce resistance across all organisational levels. AI initiatives often falter when employees feel excluded or uncertain about how changes will affect their roles. Leaders must foster a culture of openness, where feedback is welcomed, and the purpose behind AI adoption is clearly articulated. This alignment ensures that AI becomes a shared journey, not a top-down directive.

To build alignment and reduce resistance, life sciences organisations should be able to:

• Develop internal communication plans that clearly explain AI’s purpose and benefits.
• Create feedback loops that allow employees to shape and improve AI initiatives.
• Equip managers with the tools to lead AI-related conversations confidently.

Use case selection & ROI measurement

Not every AI project will deliver value. A PMI Ireland survey found that 65% of businesses struggle to align AI projects with clear business objectives.

A success story of choosing an AI application is when a Swiss pharmaceutical company used AI to optimise clinical trials, reducing patient recruitment timelines by 10–15%. At the same time, a US company specialising in AI-driven drug discovery faced setbacks during its expansion into biotech, prompting a strategic reassessment. These challenges, particularly around poor target selection and integration into core R&D workflows, underscore the risks of misaligned AI use cases.

When AI use cases are chosen that do not align with organisational needs or do not have clear metrics for return on investment (ROI), companies risk underperformance or failure.

To maximise value from AI investments, life sciences organisations should be able to:

• Prioritise use cases that align with strategic goals and regulatory constraints.
• Define clear success metrics and ROI expectations for each AI initiative.
• Integrate AI into existing workflows, especially in R&D and clinical operations.

Ethical AI & trust

AI isn’t neutral – it can inherit biases that harm patients. The EU AI Act mandates strict bias prevention measures for AI in healthcare, but enforcement is still evolving. For example, if an AI-driven drug development prioritises certain genetic profiles over others, it could lead to biased outcomes, potentially excluding diverse patient populations from benefiting.

In Ireland, community pharmacies are now assessing AI’s role in dispensing, ensuring it doesn’t introduce bias or compromise patient safety. Further, companies are proactively adhering to Ireland's National AI Strategy, AI – Here for Good, which involves developing clear protocols, policies, and due diligence to protect the company, product users, and public trust.

To ensure ethical, inclusive, and trustworthy AI, life sciences organisations should be able to:

• Conduct bias audits and fairness assessments across AI models and datasets.
• Align AI development with national and EU-level ethical standards and transparency requirements.
• Build diverse, multidisciplinary teams to oversee AI design, deployment, and monitoring.

What’s next?

AI can transform Ireland’s life sciences sector, but only if the groundwork is solid. The question isn’t if AI will shape the future of life sciences, but how well-prepared organisations are to make it work.

Leaders must champion not just AI adoption, but also the cultural and operational shifts required for success. Their role is pivotal in aligning vision with execution, fostering trust, and ensuring that AI becomes a force for good across the organisation.

Reach out to a member of Grant Thornton’s team to assess your readiness and ensure you're equipped to harness AI’s full potential while navigating the complexities.

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