1. We asked 200 pharma leaders about AI
The State of AI in Pharma market research shows the current state of the industry. It covers AI adoption rates, investment priorities, and the challenges that slow progress. It also highlights the optimism that fuels future developments.
Key stats from 200+ pharma leaders
- About 7 in 10 organizations are already piloting or using AI across the business.
- 33% are piloting, while 36.9% are employing AI across various functions in significant ways.
- Biggest barriers:
- 46.5% lack AI expertise or talent.
- 38.6% face issues with data availability or quality
- 36.6% struggle with regulatory uncertainty
- Outlook: 62% report high or very high optimism about AI’s future impact.
We wanted to cut through the noise. We surveyed more than 200 pharma experts from Europe, North America, and Asia to get a complete view.
- How far along are they in adopting and scaling AI?
- Which areas are seeing a real impact?
- What’s still holding them back?
The result is The State of AI in Pharma. This report reveals trends in adoption, investment patterns, and readiness factors. These factors are driving the next stage of digital change in pharma and life sciences.
The pharmaceutical market is growing fast. AI is adding value and sparking innovation. Major companies are using these technologies to their advantage.
AI technologies are changing the industry at every stage. These include deep learning, natural language processing, and predictive analytics.
This report builds on data obtained directly from the leaders of the transformation. The insights reveal where pharma is today. They also show how leadership, process, and investment work together to drive AI success.
Want the full data?
Download the complete “State of AI in Pharma” report. See how your organization stacks up in adoption, maturity, and readiness.
Terminology used in the report
- AI adoption — using AI in at least one function (pilot or production).
- Piloting — limited-scope testing with a defined use case and timeline.
- Cross-functional adoption — AI is used in many teams with shared governance.
- AI maturity — the effectiveness of organizations in implementing AI. It includes three parts: processes, skills, and governance.
- Readiness — the enablers that allow scaling (data, talent, leadership support, procedures, KPIs).
Suggested related read:
How Digitalya helps pharmaceutical companies move from AI pilots to scalable impact
The data shows that AI success in pharma relies on readiness. This includes skills, governance, and integration, not just tools. Digitalya helps with this change by creating digital products. These products make AI easy to use in regulated business settings.
| HCP portals and digital experience platforms | Embed AI-assisted content discovery, next-best-action logic, and personalized journeys while keeping compliance in mind. |
| Data and system integration | Connect AI workflows with legacy systems to reduce “pilot isolation.” |
| Governance-ready implementation | Ensure traceability, role-based access, and auditable decision points for regulated teams. |
| AI-assisted workflows | Speed up tasks like reporting, segmentation, and content production. Keep human review for high-risk outputs. |
2. AI adoption is accelerating, but maturity is uneven
Our data shows that AI in the life sciences and pharmaceutical industries is moving past experimentation. Almost 70% of pharmaceutical companies are testing or using AI solutions. This shows the industry is moving from theory to real action.
Pharma companies are noticing big trends in AI use. This could transform healthcare and drug development. These firms still face hurdles like data privacy and regulatory compliance. They also need specialized talent to succeed.
About 33% of pharmaceutical companies are testing AI. Meanwhile, 36.9% are using it widely across different functions. Only 1% have intentionally blocked AI as a strategic choice.
Artificial intelligence isn’t just a side project anymore. It’s now a key part of pharma’s core infrastructure.
But while adoption is high, AI maturity remains uneven. Many companies are in mid-stage development. They are building internal knowledge and scaling use cases across departments.
For example, early AI applications in clinical trials use predictive models. These models help optimize study design and site selection.
AI-driven methods are making clinical trials more efficient. They help pharma teams cut down on manual tasks, choose sites more accurately, and speed up insights.
Only around 12% see themselves as “advanced.” This means they use AI in a smart way across different areas.
These advanced organizations test complex AI models. This helps them gain insights into both commercial and R&D functions. They use tools ranging from predictive analytics to generative AI.
Regional maturity gaps
Geography plays a major role in how quickly pharma AI evolves:
- Europe leads, with 87% of companies either piloting or scaling AI
- North America is evenly split across exploration, piloting, and adoption
- Asia-Pacific trails behind, with 23% of organizations reporting no current AI plans
This uneven process shows an important point. Growth in AI relies more on ecosystem maturity than on company size or budget. Smaller organizations are also catching up.
They invest in agile projects that show promise. When results appear, these projects can scale quickly. This shows how AI maturity is changing the pharmaceutical industry worldwide.
The message is simple: AI use in pharma is accelerating, but the competition isn’t over yet. The next phase of progress relies on turning pilots into a broad impact. This will come from improved leadership, readiness, and governance.
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3. Budgets are growing, but still cautious
Pharma companies are investing in AI — but not all are full throttle yet. AI adoption is speeding up, but many organizations still see it as a trial instead of a full-scale change.
The State of AI in Pharma report shows that 42% of pharma professionals don’t know their digital budget for AI. This lack of visibility shows that AI spending often occurs informally.
It can occur in pilot projects. It can also happen in proof-of-concept efforts. Additionally, it may take place in innovation budgets that are separate from the main strategy.
Among those who track investments:
- 20% allocate less than 5% of their digital budget to AI
- 21% invest between 5-10%
- Only 17% of companies invest more than 10%. This group is most likely to have enterprise-wide AI programs.
This uneven spending points to a cautious mindset. Many pharmaceutical companies are proving ROI before scaling. This is especially true in regulated or cost-sensitive areas like clinical trials. Here, showing value and compliance takes time.
AI in drug development and clinical trials can save money for pharmaceutical companies. This offers a solid reason for more investment.
Budget does not equal adoption, but signals commitment
Interestingly, the report found no direct link between budget size and AI adoption stage. However, descriptive data show clear trends:
- Smaller budgets (< 5%) — early exploration and planning
- Mid-range budgets (5-10%) — active piloting and experimentation
- Larger budgets (11-20%) — advanced, cross-functional adoption
In other words, the budget is not the cause of AI progress, but the result of it. Leadership grows more confident in giving out resources once pharma teams start to see measurable results from AI.
Takeaway: cautious investment reflects not skepticism but pragmatism. Pharmaceutical companies are testing how AI can make the biggest difference. They use these results to support their plans for future growth.
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4. The biggest challenges for pharma AI
As AI use grows in life sciences and pharma, one thing stands out. The main challenge isn’t the technology; it’s the people and processes.
Our research shows that the top barriers to AI adoption in pharma are deeply internal. Almost half of pharma leaders (46.5%) say the main hurdle is a lack of AI talent. Next is data availability and quality issues at 38.6%, followed by regulatory uncertainty at 36.6%.
Data security and ethics are big concerns. Organizations handle sensitive information and aim for responsible AI practices. They must also follow regulatory rules.
Regulatory oversight is vital for patient and drug safety. It ensures AI-driven drug processes are clear and effective. They also focus on patient well-being.
In other words, the challenge is whether pharma organizations are ready for it.
Where AI breaks in pharma (and what fixes it)
| Pharma challenge (from the survey) | Why does it block AI? | What “good” looks like in practice |
| Lack of AI talent or expertise | Teams can’t define use cases, evaluate vendors, or operationalize outputs | Clear use-case owners, enablement, “human-in-the-loop” workflows |
| Data availability and quality | Models don’t generalize; outputs become unreliable | Data standards, governance, and integration into core systems |
| Regulatory uncertainty | Risk teams block adoption without auditability | Traceable workflows, documented assumptions, review checkpoints |
| Legacy integration | Pilots stay siloed and never scale | APIs and integration patterns that connect AI to real processes |
Internal readiness is the real differentiator
Pharma companies face challenges in scaling AI. It’s not just about tight budgets or vendor issues. There are also gaps in organization and technology. These include:
- Change management and stakeholder buy-in (26.7%) —AI often requires new ways of working that meet resistance
- Integration with legacy systems (24.3%) — sometimes, old systems can’t handle AI workflows. This makes it hard to use advanced AI algorithms for optimization.
- Undefined processes and governance — without clear SOPs, pilots don’t turn into scalable programs
- Technical and optimization challenges — need strong AI systems to ensure transparency, fairness, and explainability. This is crucial as complexity grows.
This explains why AI readiness and AI maturity are so closely connected. Companies with leadership support, good data governance, and clear AI procedures are more likely to move past pilots. They can achieve real business results.
Early-stage companies struggle most with talent. Mature organizations face technical and optimization challenges. They must integrate AI into their current systems. They also need to find specialized vendors for complex tasks.
- Planning stage — 75% cite lack of talent as a top barrier
- Developing stage — legacy integration becomes the main issue
- Advanced stage — vendor selection and ecosystem optimization take priority
This evolution proves that AI maturity is a moving target — each step forward introduces new complexities to solve.
AI in the pharma industry isn’t held back by technology, but by readiness. To scale AI successfully, pharma leaders need to invest in skills, data governance, and internal alignment. These areas are as important as software and infrastructure.
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5. Where pharma is investing in artificial intelligence today
AI investment in pharma is becoming strategic. Our data shows that pharmaceutical companies are putting their money where results are most visible: operational efficiency and commercial performance.
Almost half of organizations (48%) invest in internal operations. A similar 47% focus on marketing and omnichannel efforts.
Many of these investments aim to use practical AI tools. They automate reporting, optimize targeting, and analyze complex databases faster than traditional systems.
AI has a dual focus. It cuts costs by automating tasks. Also, it boosts revenue through improved customer engagement.
Natural language processing (NLP) technologies help by analyzing and personalizing medical and marketing content. They do this on a large scale.
Top areas of AI investment in pharma
According to the report, the most common AI investment priorities are:
- Internal operations and automation (48%) — streamlining workflows, optimizing processes, and reducing manual effort.
- Marketing and omnichannel engagement (47%) — enhancing targeting, content personalization, and customer journey optimization.
- Customer experience and engagement (36%) — using AI for chatbots, predictive recommendations, and next-best-action insights.
- Sales force effectiveness (35%) — helping reps tailor outreach based on HCP data.
- R&D (34.5%) makes data analysis faster and improves clinical trials. It helps by making better decisions, finding patients more quickly, and improving study design. AI is speeding up clinical development. This boosts efficiency and saves money.
Areas like medical affairs, regulatory, and legal are adopting changes slowly. This shows the industry’s cautious stance in compliance-heavy roles.
AI maturity drives investment depth
The report also reveals a predictable but crucial pattern:
- Early adopters focus on customer-facing use cases like marketing and engagement
- Developing organizations expand into R&D and internal operations
- Advanced companies integrate AI into compliance and governance — using it for regulatory review, medical content approvals, and ethics monitoring.
As AI matures, investment moves from quick wins to long-term change.
Pharma AI budgets are no longer about experimentation. They focus on scaling successful use cases. They also integrate AI across departments.
This aims to deliver clear results. It improves commercial performance and boosts compliance efficiency.
AI is changing the pharmaceutical industry. It’s making research, drug manufacturing, clinical trials, and drug development faster and cheaper. This boosts efficiency and leads to better patient outcomes.
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- Boost brand loyalty with AI-driven customer insights
6. AI in pharma: optimism and what comes next
Pharma leaders have a clear message: optimism about AI is high and rising, despite the challenges.
The State of AI in Pharma shows that 62% of respondents feel optimistic about AI’s future impact. Only 9% expressed low confidence. This high positivity shows that leaders believe AI will change the industry in just a few years, even though the journey is complex.
What drives this optimism?
Our analysis found that optimism doesn’t come from technology. It stems from how ready the organization is and the support from its leaders.
Pharma leaders who have:
- clear AI procedures and governance,
- strong executive buy-in, and
- defined measurement frameworks (KPIs),
are much more confident in AI’s long-term potential. AI excels at analyzing big datasets. It predicts outcomes and boosts efficiency in drug discovery and development.
In short, optimism follows structure. Companies that see AI as a key tool — not just a test — are ready to gain its full value. This is especially true in areas like personalized medicine. Here, AI can customize treatments for each patient and enhance clinical results.
AI can boost patient outcomes. It does this by improving diagnostic accuracy, supporting decentralized clinical trials, and enabling continuous health monitoring.
The future of pharma AI
The next wave of innovation will bring AI into commercial, clinical, and compliance areas. According to the report, these are the three key trends shaping the future:
- Hyper-personalization and behavioral insights
AI will go beyond just automation. It will learn about HCP and patient behavior. Using adaptive models, it will analyze interaction data. This way, it can predict needs and personalize content in real time.
- Generative AI for regulated content
Generative AI for regulated content will help check materials against MLR standards. This speeds up approvals and ensures compliance.
- AI-powered commercial operations
AI-powered commercial operations will optimize how sales and marketing teams work together. This spans from territory planning to predictive analytics.
This is where artificial intelligence shifts from proof of concept to business-critical infrastructure. Companies investing in talent and technology today will shape “pharma innovation” in 2026 and beyond.
What this means for pharma leaders
This AI transformation is mostly about readiness, culture, and leadership. The organizations that succeed will be those that:
- treat AI as a long-term strategic priority,
- build measurable KPIs to prove value, and
- foster cross-functional collaboration between digital, commercial, and compliance teams.
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- Internal AI chatbots for pharma
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7. Why this research matters (and how to use it)
AI is becoming the backbone of the pharma industry. It shapes innovation, communication, and competition. Most organizations are looking into AI, but only a few achieve real success.
This is exactly why the State of AI in Pharma exists.
This research provides pharma leaders with a clear view of the industry’s current state. It is supported by real-world data from over 200 professionals in Europe, North America, and Asia-Pacific.
It’s not just a report but a tool to help you understand AI. It shows how AI maturity, investment, and readiness work together. This can lead to better results across all areas.
This means using the right AI tools, natural language processing, and handling patient data carefully. It also covers AI-driven clinical trials and drug discovery.
How to use this research
- Benchmark your progress — compare your company’s AI maturity against global averages
- Spot opportunities — identify functions where AI adoption delivers the highest ROI
- Build strategy with confidence — use readiness data to align leadership, process, and technology
- Prioritize partnerships — learn how collaboration with external experts accelerates the adoption of AI
About the research
This report was produced by Digitalya, in collaboration with Graphite Digital, ctcHealth, and Camino Communications. Together, we shape how AI technology, digital experience, and data intersect in the life sciences industry.
We’re helping pharma teams shift from pilots to full-scale AI transformation. Our focus is on creating solutions that are ethical, compliant, and impactful.
