Robot wars

It feels like everyone is talking about ‘how they’re using it’ but we’ve found that only 13% of pharma leaders feel ‘very prepared’ to implement AI across their digital products and services.

This highlights the real gap between ambition and execution that is at the heart of our latest research – ‘The Digital Reality Check’. It reflects an all too familiar pattern. While AI dominates industry conversations, real-world adoption still lags behind.

To truly move forward, pharma teams need to shift focus from pilots to progress. That starts with identifying where AI is delivering value, where it’s underused and what’s getting in the way.

It’s undeniable, AI holds the potential to transform digital engagement in life sciences. But while investment is rising, many companies remain cautious and uncertain about how to scale its use responsibly.

AI’s ability to process and analyse large volumes of data makes it powerful for identifying behavioural patterns and generating actionable insight.

Pharma companies are making the greatest impact in data analytics and customer insight – where speed and scale are essential. But rising user expectations mean those delaying adoption risk falling behind.

It’s not only about speed to execution but how to use it effectively. AI can model customer sentiment, track usage patterns and surface valuable behavioural insight.  But it shouldn’t replace quality first-hand research.

The most meaningful insights often come from the human conversations that explore motivation, friction and context. It’s the nuance algorithms cannot reliably capture. It’s the things people don’t always say but always feel.

We can’t let our desire to push forward with AI create an imbalance between the practical and personal. Currently, 52% of pharma companies use AI-driven predictive analysis yet only 29% conduct direct user research. If that gap widens, digital products may fail to meet user needs.

There is an encouraging signal in analytics: a third of pharma leaders say AI significantly impacts personalisation and journey optimisation.

But we can’t forget: to create truly relevant experiences, we must support these efforts with high-quality content, thoughtful design and strong search capabilities.

While AI is gaining traction in analytics and personalisation, other high-impact areas remain overlooked. Design and build, SEO and content production are all considered low-impact in current AI programmes.

This is a missed opportunity in an industry that often struggles to keep digital experiences fresh, usable and relevant.

Design quality and content accessibility are key drivers of AI adoption, particularly in apps and HCP portals. However, time and budget constraints have long limited the ability to iterate.

Scaling content has always been a challenge, but with the maturing breadth of AI tools now available, pharma teams have a real opportunity to ease that burden.

Search is evolving too. As large language models influence how users discover information, pharma companies must ensure content is well structured, optimised and accurate, just as with traditional SEO.

AI has a valuable role to play here: helping teams create content that is credible, discoverable and aligned with user intent. Ignoring this capability risks disengagement, missed traffic and most importantly, a loss of trust.

Many of the barriers holding back AI adoption are the same ones that have historically slowed digital transformation.

Many leaders are focused on getting their house in order before committing to wider AI implementation. That caution is understandable: risk, complexity and capability gaps are all real barriers. But it’s worth asking: what’s the cost of waiting?

AI depends on access to sensitive data and data security remains a top concern for three-quarters of pharma leaders. Off-the-shelf AI tools often lack transparency, making it difficult to align with sector-specific standards.

These concerns are valid, but companies that lead with transparency, informed consent and strong governance will be best positioned to realise AI’s potential.

Legacy infrastructure adds another layer of complexity. Many systems were never designed for real-time processing or automation.

Replacing them is no small task, especially in risk-averse environments where a single misstep can slow or halt progress.

The friction is real but not insurmountable.

The capability gap also plays a role. A third of pharma leaders report a lack of in-house AI expertise. Without that foundation, it becomes difficult to define use cases, manage risk or scale innovation effectively.

External partners can help in the short term, but without internal understanding, AI adoption is likely to stall at pilot phase. Building AI literacy across teams will be essential to move from experimentation to meaningful impact.

Yes, generative AI is evolving fast – too fast for most organisations to fully keep up. But waiting for the perfect moment to begin is a strategy in name only.

Rather than perfection, teams should aim for momentum: identifying clear use cases, understanding feasibility and testing responsibly.

The opportunity extends well beyond analytics. Used effectively, AI can support scalable content creation, improve accessibility, personalise journeys and sharpen the relevance of every digital touchpoint.

It can enable more customer-centric products by making experiences simpler, smarter and easier to use.

Ultimately, AI should not be treated as an add-on. The most successful organisations will integrate it as a core enabler, powering a more adaptive, connected and responsive digital ecosystem.

Rob Verheul is CEO at Graphite Digital