A Cyber Expert’s Five Takeaways from Shark Tank Star Robert Herjavec’s HIMSS25 Address

Written by Lee Kim JD CISSP CIPP/US FHIMSS. Photo courtesy of Lee Kim.

Healthcare and technology professionals must evolve their understanding of AI in order to unlock critical innovations to serve patients in the best way possible.  

AI is not a fad, it’s here to stay. And as legendary investor and entrepreneur Robert Herjavec of Shark Tank fame outlined in his recent address at the 2025 HIMSS Global Health Conference and Exhibition, we have to identify key trends, opportunities and challenges to analyze the evolving AI landscape in healthcare. 

In his session, titled “Healthcare Legacy Data Walks Into the Shark Tank, and Gets a Deal,” Herjavec provided unique and powerful insights regarding artificial intelligence and the immense amounts of data that AI must contend with. But the key is to understand that artificial intelligence is a moving target. And yet we have to evolve with it. 

To implement AI solutions into healthcare settings, we will have to learn and evolve with AI’s evolution and develop an understanding of both the technology and the data that the tech processes. We will also need data platforms that can keep up with future needs of advanced AI applications. 

Herjavec offered his perspective on investment, entrepreneurship, and emerging technologies. As a cybersecurity and privacy expert, here are my key takeaways. 

1. AI and data processing are moving targets.  
    
2. We’re about to embark upon the new industrial evolution that is artificial intelligence. To overcome AI and data challenges, organizations need to focus on integration, preparation, enablement and delivery. We need to bring data into AI.  
   1. Integration: Connect AI to data sources and effectively governing the data. 
   2. Preparation: Data development and quality frameworks must be implemented to deliver meaningful information to models and applications. 
   3. Enablement: Data engineers, data analysts and data scientists must have the appropriate tools and resources to develop and execute their tasks. 
   4. Delivery: Define the metrics to ensure that progress can be measured, and goals can be set with appropriate tools. 
       
3. Traditional data platforms — the infrastructure that supports the collection, storage, management and processing of data — face limitations. They are slow, inflexible, costly and lack scalability. There is increasing complexity and volume of data that is distributed and unstructured. Traditional data platforms may lack the ability to perform advanced data analytics and support advanced AI applications. 
    
4. We need to use a new data platform for effective data management and processing was developed to integrate diverse data sources, enable real-time processing and improve data accessibility. Unlike traditional data platforms, it should streamline data queries, reduce processing time and support secure and scalable data sharing. 
    
5. With modern data platforms, AI applications can deliver insights not previously known, due to the ability to process more data in a multi-dimensional and faster manner. Real-time data processing and AI-driven optimization can solve complex problems in healthcare, providing the path to unique and effective healthcare delivery solutions for better health outcomes of patients.

I have said for many years: Protect the data, protect the patient. But now this idea can be expanded to: Use and protect the data, help the patient.  

AI-driven healthcare relies on effective use and protection of data. We must implement data platforms that can keep up with the critical and growing need for healthcare innovation.  

Responsible use of AI-driven data analytics requires robust security and governance. 

Read more: Shark Tank's Robert Herjavec talks the big pitch