Strategic AI Budgeting for Pharmacies in 2025

In 2025, artificial intelligence (AI) will be central to the strategic plans of pharmacies, with significant investments expected, especially in drug development, supply chain management, and patient care optimization. Over time, AI will help reduce the time and cost of bringing new drugs to market, thus lowering the cost per drug approved. One report predicts the AI market for life sciences, including pharmacy, will reach approximately $10 billion by 2032.¹ Bain & Company reports that 40% of pharmaceutical companies are already including anticipated savings from generative AI (GenAI) in their 2025 budgets.²

However, escalating costs remain a key challenge in AI's adoption. GenAI is quickly being integrated into various pharmacy and life sciences sectors. While GenAI is a powerful tool within the broader AI landscape, it remains an area where cost efficiency and return on investment (ROI) are still uncertain.

Research and development with AI. Image Credit: © Exnoi - stock.adobe.com

Gartner’s survey of life sciences executives in June 2024 found that 72% of respondents have at least one GenAI use case in production, with 30% deploying 6 or more, and 92% testing at least one use case in pilot phases. However, Gartner also reports that more than half of pharmacy organizations abandon their AI initiatives due to budgetary issues.

Given the fast-paced changes in AI technology, it’s crucial for pharmacies to budget strategically and maximize their AI investments. Here’s how to effectively plan your AI budget to drive success in the coming year.

Invest in Technologies That Streamline Pharmacy Operations and Drug Development

Pharmacy professionals are familiar with the complex and time-consuming nature of drug development, patient care, and operations. AI is accelerating these processes by streamlining decision-making and improving efficiencies. Investing in AI-driven technologies can optimize operations and reduce costs. For pharmacies, this means:

• Smart Technologies for Data-Driven Decisions: AI can help streamline workflows, automate data collection, and reduce manual tasks, allowing pharmacists to focus on higher-level clinical tasks and patient care. By using AI to capture only relevant data, pharmacies can avoid unnecessary data overload and improve cost-efficiency.
• Findability, Accessibility, Interoperability, and Reuse (FAIR) Data for Improved Drug Discovery and Patient Care: AI can enable pharmacies to better manage data across departments, ensuring transparency and improving the reproducibility of experiments and results. The use of FAIR data standards across systems ensures that data-driven decisions can be made more easily, facilitating faster research and more personalized care.
• Future-Proof Technologies: The pharmacy industry is evolving rapidly, and AI tools should be scalable to support long-term growth. Pharmacies should adopt technologies that integrate seamlessly with their existing systems and grow with the changing needs of drug development, patient services, and regulatory requirements.
• Low-Code/No-Code Solutions: Introducing low-code or no-code platforms allows pharmacy professionals to utilize AI without needing deep IT expertise. This can expand the value of AI initiatives across departments, reduce bottlenecks, and support rapid decision-making in clinical and operational settings.

Maximize ROI by Targeting Key Cost Drivers

While many pharmacies are investing in AI, it’s important to ensure those funds are being spent efficiently. Data, computing resources, and people are the primary drivers of AI-related costs. Here’s how pharmacies can maximize their AI ROI:

• Data Costs and Infrastructure: Building a solid data infrastructure is crucial. “Garbage in, garbage out” is a well-known adage in AI. Pharmacies need to ensure that their data architecture is robust, secure, and capable of handling large volumes of information. This includes investing in data storage, compute power, and security measures, all while keeping an eye on costs associated with maintaining these systems.
• Effective Use of Proprietary and Public Data: Acquiring data is expensive, but pharmacies can gain a competitive edge by combining proprietary data (from clinical trials, patient records, etc) with publicly available datasets. While public data sources can help build better AI models, proprietary data are often the key differentiator, leading to more accurate predictions in drug development and patient care strategies. Effective management of these data sources is essential for driving AI success.
• Maintenance and Overhead: Data modeling and AI systems require constant maintenance and refinement. For pharmacies, this means managing data cleansing, ensuring proper labeling, and staying updated with regulatory changes. The overhead of maintaining AI systems, whether on-premise or cloud-based, must be factored into the budget to prevent unexpected costs.

Pharmacies should aim for a "data-in-a-loop" approach where continuous input from clinical, operational, and research data enhances AI models. However, this cycle can be expensive to maintain, especially as it requires a dedicated team and resources to manage the flow of data to ensure it’s actionable.

Prioritize High-Impact AI Use Cases

In the competitive world of pharmacy, early adopters of AI are already setting themselves apart. Nearly 15% of respondents in Gartner’s survey have already deployed 11 or more AI use cases, leading the way in optimizing drug discovery and patient management. Pharmacies need to identify high-return use cases for AI investment and align them with their strategic business goals. This might include:

• Optimizing Drug Formulation: AI can help pharmacy professionals identify more efficient drug formulations, predict patient responses, and accelerate the development process.
• Enhancing Pharmacy Operations: AI can streamline inventory management, optimize drug stocking, and predict shortages before they become issues, reducing costs and ensuring better service delivery.

AI investments should be treated as investments in the future of your pharmacy, targeting areas that will provide the highest return—whether that’s improving operational efficiency, enhancing patient outcomes, or accelerating the drug development pipeline.

Tips for Setting Your AI Budget Effectively

To ensure that your pharmacy's AI budget is optimized, consider these best practices:

• Understand Pricing Metrics and Structures: Different AI vendors offer varied pricing models, such as token-based or character-based pricing. Understanding these models and their scalability is essential to budgeting effectively.
• Assess AI Proposals Thoroughly: Make sure to account for all costs when evaluating AI solutions, including fine-tuning, retraining, and other necessary adjustments.
• Negotiate for Scalability: Ensure that AI solutions can scale as your pharmacy grows. Look for transparency in pricing and potential hidden costs.
• Track Metrics and ROI: Identifying key metrics early in the AI adoption process helps measure impact and ensure you’re getting the expected return on investment.
• Focus on Cost Savings: AI isn’t just an added expense—it can replace outdated technologies and reduce operational costs. AI can handle administrative work like reporting and inventory management, freeing up time for pharmacy staff so they can focus on higher-value tasks, such as patient care.

A Scientific Approach to AI Implementation in Pharmacy

As AI continues to reshape the pharmacy landscape, it’s essential to strike a balance between technological advancements and the invaluable role of pharmacy professionals. AI has the potential to drastically reduce the time and cost of drug development, improve patient outcomes, and streamline operations—but only if implemented thoughtfully and strategically.

Pharmacies must focus on people, data, and the way data is integrated into models. The most successful AI strategies will integrate pharmacists’ expertise and insights with cutting-edge technology. By keeping the needs of pharmacy professionals at the forefront of AI planning, pharmacies can create sustainable, high-impact AI strategies that drive both innovation and efficiency.

REFERENCES

1. Deepa. AI In Life Sciences Market Size, Companies and Future Analysis. Towards Healthcare. March 2023. Accessed December 12, 2024. https://www.towardshealthcare.com/insights/ai-in-life-sciences-market

2. Berger E, Sandig R, George KC. How to Successfully Scale Generative AI in Pharma. Bain & Company. February 12, 2024. Accessed December 12, 2024. https://www.bain.com/insights/how-to-successfully-scale-generative-ai-in-pharma

3. Q24 LLMs and Generative AI: Life Science Manufacturer Perspective. Gartner. Date. Accessed December 12, 2024. https://www.gartner.com/document-reader/document/5638991?ref=sendres_email&refval=79002868