AI is exposing a new digital maturity divide in pharma businesses

Businesses are finding that AI is a great leveller. Organisations with mature digital infrastructures can use AI to unlock greater insight, automate processes, and respond faster. While those with fragmented technology, inconsistent data, or disconnected processes will find that AI exposes, and sometimes magnifies, existing weaknesses, the result is a growing digital maturity divide – and it is becoming an increasing worry for organisations in the pharmaceutical industry.

The pharma organisations that are realising value are those that view AI as an extension of their digital backbone, not a shortcut around it. Only then will their digital transformation provide operational resilience and regulatory assurance, enabling data-driven decision-making and create the foundation for long-term competitiveness.

Already more than 60% of leaders plan to prioritise generative AI investment, which is expected to create between $60 billion and $110billion in annual economic value, while 89% of pharmaceutical C-suite executives strongly believe AI is critical for maintaining a competitive edge.

Yet, as AI adoption increases, it is exposing just how prepared organisations really are to operate in a digitally connected environment. While some organisations are able to adopt AI at scale, others are finding AI is exposing their weak data structures that are impeding strong decision-making.

So, what is behind this widening divide?

AI puts digital maturity under the spotlight…

The digital foundations for effective AI adoption are reliable data, connected systems, standardised processes, effective governance, and clear accountability.

Where those foundations are in place, AI can significantly extend what existing digital platforms are capable of. Integrated and trustworthy data can be transformed into predictive insights, intelligent automation, and faster, more informed decisions.

…and separates the leaders from the laggards

However, AI does not compensate for poor digital foundations. Inconsistent data, disconnected systems, unclear ownership, and weak governance can limit AI's effectiveness and introduce additional operational and regulatory risks.

This is creating a clear distinction between organisations that are building digital capability at an enterprise level and those pursuing one-off AI initiatives.

The differentiator is not who has the most AI pilots underway. It is who has created the infrastructure, governance, and operating model needed to scale AI safely and effectively.

AI is becoming more than a technology opportunity. It is increasingly a litmus test of organisational digital maturity.

This divide is not necessarily determined by company size or budget. Many organisations already possess the technologies required to support AI adoption. The difference is often whether data, processes, and governance have been connected across the enterprise. In many cases, digital maturity is less about deploying new technology and more about creating the conditions for existing technology to work together effectively.

From digital fatigue to measurable value

The value of AI in pharma emerges most clearly when applied within connected, well-governed environments, enhancing everyday operations for better decision-making and risk management at scale.

After years of transformation programs, some pharmaceutical organisations are understandably sceptical of another wave of technology investment. However, AI can help renew the case for digital transformation, but only when it is applied to clearly defined operational problems and supported by connected, well-governed data.

In pharma, compliance is fundamental. AI can support a shift from reactive quality management towards more predictive approaches. However, use in a GxP environment depends on strong data integrity, traceability, validation, and human oversight.

Emerging guidance, including the draft EU GMP Annex 22, also makes it clear that different types of AI and different levels of risk require different controls.

In R&D, AI accelerates insight generation and informed prioritisation, but is dependent on harmonised and traceable data across systems. In supply chains, it strengthens forecasting and reliability, but relies on consistent master data and processes.

The true value AI can bring to pharmaceutical companies is when it is an extension of their digital backbone, not a shortcut around it. Ultimately, AI does not remove the need for strong operational discipline – it reinforces it.

But what is holding many pharma companies back from achieving digital maturity?

Regulation

Complex regulatory, organisational, and skills challenges continue to slow digital progress. As one of the most highly regulated industries, pharmaceutical organisations must continually balance innovation, operational efficiency, and compliance with evolving regulatory requirements. A challenge for many organisations is how best to ensure digital compliance with global regulatory frameworks, such as EU GMP Annex 11 and FDA 21 CFR Part 11, without slowing implementation.

Dwindling expertise

Limited expertise in data science, AI, and cybersecurity is a constant barrier to digital transformation. However, the challenge extends beyond recruiting specialist AI talent. Many organisations also need business leaders, quality professionals, and functional experts who understand how AI can be applied responsibly within regulated environments. Without this combination of technical capability, operational knowledge, and governance expertise, organisations can struggle to move from experimentation to scaled adoption.

About 70% of pharma hiring managers struggle to find candidates who combine deep pharmaceutical knowledge with AI skills, and that’s not forgetting the challenges in retaining experienced employees in a rapidly evolving digital environment.

Addressing these challenges requires a coordinated strategy that blends technology investment with cultural transformation.

So, what are some ways pharma professionals can close the digital maturity gap?

1. Start with business outcomes, not AI tools

Many organisations begin with technology and then search for a problem to solve. Digitally mature organisations take the opposite approach, identifying high-value operational challenges before determining where AI can create measurable impact.

In quality and regulatory functions, AI can help teams manage growing volumes of documentation and information more efficiently. However, success depends on clear ownership, reliable data, and robust validation processes, rather than the technology alone.

2. Strengthen the foundations

AI is only as effective as the data and processes that underpin it. Before scaling AI initiatives, organisations should assess whether data is consistent, traceable, and connected across functions.

AI-enabled demand sensing, risk prediction, and scenario modelling can improve supply chain resilience, but these capabilities rely on trusted master data and standardised end-to-end processes across internal and partner networks.

3. Embed governance from the outset

As AI adoption increases, governance cannot be treated as an afterthought. Organisations should define ownership, validation requirements, risk controls, and decision-making responsibilities before solutions move into operation.

In GxP-regulated environments, AI can support more predictive approaches to quality and compliance, but only when supported by strong governance, data integrity, and validation practices.

4. Scale capability, not isolated pilots

The goal should not be to launch more AI initiatives, but to build the organisational capability needed to scale them effectively. Successful organisations use early projects to develop repeatable ways of working, governance models, and technical foundations.

Whether accelerating R&D through connected clinical data or enabling more personalised engagement across commercial and medical functions, long-term value comes from enterprise-wide capability, rather than standalone AI experiments.

The AI advantage is up for grabs

AI may be the catalyst, but digital maturity remains the differentiator. Pharmaceutical organisations that connect AI to trusted data, controlled processes, accountable decision-making and measurable operational outcomes will be better positioned to scale it responsibly. Those that treat it as a shortcut around unresolved digital weaknesses are likely to find that AI makes those weaknesses more visible.

The question is therefore not simply where AI can be deployed. It is whether the organisation has the digital and operational foundations to trust, govern, and scale the decisions AI helps it make.

About the author

Tarn Brown is a business transformation advisor at Columbus UK, working with organisations across life sciences to shape value led digital transformation. She facilitates strategy and change programmes that help organisations prioritise effectively, quantify value, and deliver phased roadmaps aligned to people, capability, and pace of delivery. With hands-on experience in large-scale digital transformation programmes, GxP/CSV validation, and data integrity, Brown ensures systems are implemented with compliance, resilience, and measurable outcomes. She is an active voice in the UK life sciences community, contributing thought leadership on digital maturity and innovation.