In June, Vultr and S&P Global Market Intelligence unveiled an industry report, The New Battleground: Unlocking the Power of AI Maturity with Multi-Model AI. Drawing on data from this groundbreaking, industry-wide research, Vultr is releasing a series of derivative reports that provide insights into the AI operations unfolding within specific industries.
In this first installment, we explore the Healthcare & Life Sciences (H&LS) industry where AI is transforming medical research and clinical practice. From predictive analytics that foresee potential health issues to personalized medicine tailored to individual genetic profiles, AI is paving the way for a future where healthcare is more proactive, precise, and patient-centric.
Here are a few of the report’s key findings:
AI maturity: The new competitive edge
Enterprises are rushing to maximize the benefits of placing AI at the core of as many business processes as possible. This is especially true for H&LS organizations, which more often described their AI maturity as being at the highest levels compared to all other industries.
In fact, within two years, 64% of H&LS organizations anticipate reaching the most advanced level of AI maturity – the transformational stage. Only 49% of organizations in other sectors expect to achieve the same. In 2023, H&LS enterprises also outperformed other sectors relative to their 2022 performance in financial outcomes and across core business indicators, including customer support, marketing, and revenue.
Multi-model: A prescription for success
The number of models actively used within an organization says much about a company's AI maturity level, and the H&LS sector leads in this area. Not only have H&LS organizations deployed over 170 models in production on average compared to the 159 deployed for all other sectors, but the number for H&LS is expected to grow to 182 over the next year compared to just 174 for all other sectors.
AI: Boosting every business function
Similar to other sectors, H&LS expects 80% AI adoption across all business functions and AI embedded across business units and applications over the next two years.
Model training: Multiple routes, one goal
Enterprises across all industries are conducting model training through various operating paradigms. The same is true for H&LS organizations. While cloud and hybrid/multicloud together prevail (combining to capture 36%), no single paradigm dominates, whether on-premises, in a third-party cloud platform, or in hybrid and multicloud environments.
AI inference is moving to edge environments
Multiple approaches also exist for model deployment and AI inference delivery. However, due to increasing model complexity, the vast volumes of data they must process, and the rising need for ultra-low latency – not to mention strict data governance requirements – these models are being deployed closer to the end user. Indeed, 84% H&LS enterprises plan to move to the distributed edge in the near future.
The partners helping to pave the way
As enterprises look to achieve greater AI maturity, a new infrastructure order is forming that doesn’t rely solely on traditional hyperscalers, whose baggage (unpredictable costs, limited GPU availability, finite scalability, sketchy security/privacy, vendor lock-in, etc.) is becoming too hard to ignore.
Sure enough, 62% of H&LS organizations leverage a partner to build their models. Still, while 28% opt for AI specialists and 23% go with a GSI, only 11% are turning to hyperscalers.
Recommendations for AI-aspiring H&LS organizations
How can H&LS organizations take action with this information? Here are some recommendations:
Prioritize high-impact areas: To catch up, H&LS organizations that are lagging in AI adoption must identify and implement AI in areas with the most significant financial impact.
Strategic AI investments: To transform business operations, H&LS organizations must invest strategically in AI infrastructure supporting multi-model AI at scale.
Aggressive AI integration: To stay competitive, all H&LS organizations must set ambitious timelines for integrating AI across all business functions.
Optimal environment mix: H&LS organizations should determine the best combination of DIY/on-prem and cloud-based environments to meet their unique needs.
Cloud partnerships: H&LS organizations should partner with a cloud provider that supports their core cloud data engineering, AI workloads, and hybrid architectures – one that provides all the security, compliance, and core services needed to rebuild cloud infrastructure with AI at the core of every application.
Contact our sales team to learn more about how Vultr brings the vision, strategy, technology, and expertise to help the AI-aspiring become mature AI powerhouses.
And learn how pioneering biotech company Athos Therapeutics uses NVIDIA HGX H100 GPUs on Vultr to discover next-generation treatments and medicine. Access the case study here.
For a full copy of the report, click here.