AI maturity in health and life science organisations

Artificial intelligence (AI) and unstructured data are beginning to transform the healthcare and life sciences sector, creating opportunities to improve patient care and streamline critical processes. However, the sector remains in the early stages of its AI maturity journey, particularly when compared to more digitally advanced industries. The sector is gradually advancing in AI maturity through a clear focus on improving patient diagnosis and treatment plans through AI-driven automation, unstructured data, and multi-modal AI, setting the foundation for transformative change.

Findings are based on research conducted by Iron Mountain alongside independent market research specialist Vanson Bourne. Data in this report is based on 186 IT and data decision-makers in the healthcare and life sciences sector, who have knowledge or involvement in their AI strategy. You can read the global report here.

Three key takeaways:

• More than one-third (37%) of decision-makers in the healthcare and life sciences industry are in the middle of their AI journey, with an additional 29% early in the AI journey, meaning many have yet to reach AI maturity within their organisations
• Improving and enhancing customer service is key for the industry, and improving patient diagnosis and treatment plans through AI-driven automation will be the cornerstone of this transformation
• As a result, 58% of organisations in the healthcare sector are leveraging unstructured data with AI, and are one of the most likely industries to see the growing importance of multi-modal AI in the next two years

Iron Mountain commissioned independent market research specialist Vanson Bourne to conduct this piece of research. The study included surveying 1,400 IT and data decision makers who have knowledge of or responsibility for AI strategy at their organisation. Respondents’ organisations had to have 250 employees or more across the following countries: US, UK, France, Germany, India and Australia.

Organisations are from several public and private sectors but there was a strong focus in banking and financial services, insurance, healthcare and life sciences, media and entertainment, the public sector (excluding healthcare) and energy. This summary is based off 186 decision makers in the healthcare and life sciences sector.

Leveraging unstructured is key to enhancing customer experience

Unstructured data, such as text, images, audio, and video, lacks a predefined format and requires advanced tools to process and analyse. However, this type of data can help construct a more concrete picture of what’s going on within an organisation, more so than structured data alone. For instance, through using AI with patient files containing unstructured data, organisations will get a quick glance at any potential patterns within the patient history, helping to quickly identify any risk factors. Unstructured data can also be pulled from patient feedback forms, and by using AI, organisations can get actionable insights into what’s working well or what areas need to be developed to heighten customer experience.

Unfortunately, this mindset of prioritising unstructured data has not yet been made as fewer from the healthcare industry than the global average say that unstructured data is going to be important to the success of their organisation over the next two years (52% vs a global average of 56%). It’s likely there’s an underestimation of unstructured data’s importance, with few fully recognising the critical role it plays in supporting resources and general patient satisfaction. This could be as a result of an employee’s lack of awareness of how unstructured data can be used, particularly within the legacy systems. Closing this gap in awareness and capability will be essential for healthcare organisations to unlock the full potential of unstructured data, enabling more effective decision-making, improving patient outcomes, and enhancing operational efficiency in an increasingly data-driven sector.

However, compliance and regulations restrictions strongly affect what organisations within the healthcare industry can and can’t do. For instance, strict data privacy regulations like HIPAA (Health Insurance Portability and Accountability Act) in the US or GDPR (General Data Protection Regulation) in Europe govern how patient data is collected, stored, and used. As customer satisfaction is the most important AI outcome for the healthcare and life sciences industry, it makes sense why adhering to these rules is critical to maintaining trust and delivering quality care. Failing to do so will not only compromise patient trust but also expose organisations to legal and financial repercussions, potentially damaging their reputation.

Employee productivity is the business outcome least likely to be considered “very important” by organisations within the industry. What’s more, this is much lower than the global average and reflects the prioritisation of patient care and compliance over operational metrics.