How to Plan Your AI Budget Now To Succeed in 2025 Stephen Tharp, SVP Customer Operations Sep 12, 2024
In 2025 AI will be at the center of business strategy with huge investments, especially in life sciences. Over time, it will reduce the cycle length and failure rate in scientific research and critical drug discovery areas, thus significantly lower the dollars per drug approved. One report shows1 that the life sciences market size for AI investment is expected to reach nearly $10 billion before 2032. Bain and Company reports2 that 40 percent of pharma companies are including anticipated savings from generative artificial intelligence in their 2024 budgets.
At the same time, growing costs is one of the primary threats to its success. GenAI specifically is rapidly being integrated into the life science value chain. While GenAI is just part of AI/ML that is being completed by life science organizations, it is certainly one of the major areas of interest, and is an area where costs and ROI are still largely unknown.
Gartner surveyed life science executives3 in June 2024 to assess sentiment and activity. Seventy-two percent of respondents have at least one GenAI use case in production and 30 percent are deploying six or more; 92 percent have at least one use case currently in pilot. However, Gartner reports that more than half of organizations abandon their efforts due to cost-related missteps.
Because the AI space is changing rapidly, it’s essential that companies are budgeting correctly and using their investments strategically. Here’s how to plan your AI budget to be successful in the year ahead.
Spend money to help scientists increase the value of data From elementary science classes to graduate work studies, the ‘Make-Test-Decide’ cycle is taught and understood logically. Today, the injection of AI and scientific intelligence platforms are speeding up this Lab-in-a-Loop concept – a beautiful interplay between the scientific wet lab where experiments are physically performed, and the dry lab, where experiments can be simulated and modeled and where informed AI engines can recommend the next experiment to test in the wet lab.
First and foremost when it comes to AI, investments should make the life of scientists easier by supporting that Lab-in-a-Loop lifecycle, which means: Scientifically-smart technologies - Scientists know what data is needed to move faster; they simply need access. Science-first tools can also help companies reduce cost by only capturing relevant data. PhDs weren’t earned for pivot tables. FAIR data & FAIR processes for reproducibility - Enable scientists to gather, understand, use, and perform data driven work as they want to without creating a heavy burden of manual metadata tagging. Research is unknown by definition. FAIR data and processes across locations, departments, modalities, and experiments provide transparency to previously invisible possibilities. Future-proof technologies - Employ tools that will grow and scale with your organization and industry, enabling flexibility not just as targets and focus change, but also offering the ability to seamlessly connect various departments and modalities. Don’t let legacy software constraints limit how people should collaborate with one another. Low-code or no-code technologies - Introduce resources that put the power in scientists hands to decrease the burden on data science and IT, and significantly expand the value of AI / ML.