In the field of medicine, Artificial Intelligence (AI) has shifted from a futuristic notion to an increasingly integral component of how new drugs are discovered, developed, and brought to market. In the last decade, pharmaceutical companies and contract research organizations (CROs) have faced increasing challenges rising trial costs, extended timelines to drug development, increasing regulatory demands, workforce and competition issues for recruiting and retention of trials. Now a question being asked across many in the industry is “what is the forecasted spend on AI-based clinical research tools by 2030”? In this blog, let’s learn about how AI has surfaced on an enabling force, why companies are investing on AI, key uses of AI in pharma, its challenges, and implications.
Global Market Forecast for AI-Based Clinical Tools
To get a sense of the global markets AI investment into healthcare projections for the 2025-2030, it would be beneficial to use some published reports of major market firms:
- According to Grand View Research, The AI clinical trials solutions market is predicted to grow from $1.9 billion in 2023 to around $7.8 billion in 2030, which works out to a compound annual growth rate (CAGR) close to 22%. The projections imply a steady, sustainable expansion with companies gradually incorporating AI tools to facilitate the trial process.
- Coherent Solutions (as published in the South China Morning Post) states that transactions in AI-assisted clinical trials will reach over $7 billion by 2030, which closely aligns with Grand View’s story.
- Clinical Leaders Market Research analysis the future estimates slightly more, expecting the clinical research tools market to be worth around $8.5 billion by 2030.
- MarketsandMarkets takes a more cautious approach, suggesting a clinical research tools market worth $2.74 billion by 2030, which may be attributed to using a narrow description of the market and AI tools rather than the broader ecosystem of AI.
- 360iResearch (published as reported via Research and Markets) provided the most aggressive forecast, estimating the market could reach nearly $21.79 billion by 2030. This forecast for AI-based clinical research investments in the next decade leaves room for the market size to grow and exceed conservative estimates if AI technologies extend into adjacent areas of data management, remote monitoring, and predictive modelling.
While there is considerable variation among the estimates, most analysts trend towards the conclusion that global investment in AI clinical research tools will be in the range of $7–9 billion by 2030. Even at the low end of those estimates, it would represent a significant increase from today’s levels and suggest that AI is becoming a fundamental part of clinical research infrastructure in the world’s economies.
Why Companies Are Investing in AI
There are clear reasons for this level of investment:
- Rising Cost of Clinical Trials
The average cost of bringing a new drug to market is now over $2 billion, where clinical research tools market represents the greatest cost. AI possibilities to mitigate these costs by automating routine processes, improving patient recruitment, and decreasing the number of failed trials.
- Patient Recruitment and Retention
Patient recruitment of eligible patients continues to be one of the primary bottlenecks. AI algorithms can assess electronic health records, genetic data, and even social media, to efficiently and quickly identify the best candidates. This will allow trials to start faster and with less drop out.
- Data Overload
Clinical trials generate terabytes of complex unstructured data – lab reports, imaging, outcomes from wearable devices etc. AI is uniquely positioned to analyse this information in real time, ensuring cleaner datasets and feasibility of decisions.
- Safety & Pharmacovigilance
AI tools can now identify safety signals much earlier by analysing adverse event data (trials and post-market) over the multiple trials. This proactively assists companies to keep Regulators satisfied and more importantly protects patients.
- Regulatory Pressure
Entities such as the FDA and EMA are becoming more supportive of AI-enabled processes, but demand transparency and quality. Companies are spending heavily to ensure these systems will present both transparency and accuracy for regulatory agencies.
- Globalization of Clinical Trials
As trials extend into a variety of geographies, AI assists in standardizing processes across site, language, and health systems so that trial management for a global trial becomes easier.
Regional Trends in AI Adoption
Despite the general global growth picture, not all geographic regions are adopting AI in pharma clinical trials at the same rate. So, let’s see how much will pharma companies spend on AI tools for clinical research by 2030
- United States and Europe: Clinical research tools market is taking the lead, as it consists of many of the largest pharmaceutical companies, leading research institutions, and regulatory support. The investment is expected to maintain its position of dominating the global share of AI-based clinical trials.
- Asia-Pacific: This region is expected to experience the greatest AI spending in healthcare, as major countries such as India and China, have significant patient populations, have lower costs to conduct trials, and have governments supporting innovation of digital health solutions. The Indian AI-based clinical trials market is projected to increase from $22.3 million in 2021 to approximately $285.3 million by 2030, at a CAGR of more than 30%.
- Middle East and Africa: Currently, while this region has lower levels of adoption, adoption is increasing, especially for Gulf countries, which are making significant investments in healthcare infrastructure and biotechnological innovations.
Major Players and Partnerships in AI Clinical Trials
IBM Watson Health, Medidata (Dassault Systems), IQVIA, and Oracle Health Sciences are the major players in AI-enabled clinical trial platforms. Companies such as Google (DeepMind) and Microsoft have also partnered with pharma to embed AI components into research pipelines. Contract Research Organizations (CROs), such as Parexel and LabCorp are implementing AI into trial operations to inject efficiency. Pharma companies (e.g., Novartis, Pfizer, Roche) are increasingly partnering with AI startups, with a particular focus on trial design optimization, as well as integration of real-world evidence.
Implications for Pharma Professionals
For individuals pursuing careers in clinical research, pharmacovigilance, or data management, these spending estimates are not just numbers, but are career indicators. Given that companies invest billions in AI, the demand for these hybrids practitioners will continue to increase. Professionals skilled in opportunities such as machine learning applications, MedDRA coding, ICH-GCP compliance, and safety signal detection will be in high demand.
Conclusion: AI as the Backbone of Future Clinical Trials
It is forecasted that global expenditures on clinical research tools based on AI will be $7–9 billion annually by 2030, with potentially higher amounts depending on definitions of the market. This is not only a huge source of expenditure, but a fundamental transformation of how clinical trials are conceived, managed, and conducted. For pharmaceutical companies, the expense is a tactical necessity: AI is central to defining cost efficiencies, improving timelines, and achieving better outcomes for patients. For workers, it is a definite call to action to grow with the call for new skills consistent with the AI-based research. AI is not an addition to clinical trials; it will be the backbone to how therapies of the future will be developed.
FAQs- Companies AI-based clinical research tools by 2030
1. What will companies be spending on AI-based clinical research tools by 2030?
Most clinical research tools market research forecasts expect companies to spend between $7–9 billion a year by 2030, with other broader estimates looking as high as $20 billion by including money spent on adjacent technologies like remote monitoring and predictive analytics.
2. What is driving AI usage for use in clinical trials?
AI use is focused on solving major barriers to clinical trials, like cost, duration for patient recruitment, data overload, and urgency for clinical decision-making. AI will drive efficiencies and management of processes and workflows to lower costs and expedite drug delivery.
3. Where is AI uptake in clinical trials?
For now, the US and Europe are leading the market due to a strong pharmaceutical sector and supportive regulations; however, the fastest growth is projected in the Asia-Pacific region particularly India and China owing to their size and efficiencies stemming from their large patient populations.
4. What are the primary use cases for AI in clinical research?
Some of the first uses of AI in clinical research include the following:
• Predictive patient recruitment
• Natural language processing from clinical records
• Use of computer vision for imaging
• Integration of wearables
• Risk-based monitoring
• Drug repurposing and trial design
5. What are the remaining challenges to address?
Key barriers are concerns related to data privacy, acceptance from regulators, integration with legacy systems, and training of the workforce. As AI systems need to be transparent, unbiased, and secure to understand the regulated adoption.
