The AI hype cycle is in an unusual place right now, especially when we look at public opinion and the way the professional sector is portraying the technology’s potential.
Andy Sajous is field CTO and healthcare practice leader at digital transformation company Ahead. In all his meetings with healthcare chief information officers and other IT decision makers, none, he says, are willing to commit to a specific AI product or service for more than 12 months.
In this interview, Sajous explains why he thinks that. He describes some of the fast-moving shifts in the AI market, the challenges of building versus buying – he outlines some key actions healthcare CIOs should take as we head into a 2025 poised for even more AI transformation to see.
Q. You say that in your digital transformation work this year with CIOs and other healthcare technical decision makers, none of them will sign up for an AI product or service for more than 12 months. What do you take away from these experiences and what do you think this means for the future of AI in healthcare?
A. The reluctance to enter into AI contracts longer than twelve months reflects a deep uncertainty in the healthcare AI landscape. CIOs and other decision makers are wary of over-committing to tools in a rapidly evolving environment.
AI vendors are constantly releasing new products, but the market is flooded with startups and smaller companies whose futures are uncertain. There is great concern that a system that seems promising today could be outdated within a year, or worse, that the company behind it could be acquired or go out of business completely.
The speed at which AI technology is changing, especially after the launch of generative AI tools like ChatGPT, has created an environment where healthcare organizations are forced to think short-term when adopting new technologies.
However, this does not indicate a complete lack of belief in the potential of AI. On the contrary, healthcare organizations are well aware of AI’s potential to transform patient care, improve operational efficiency, and streamline administrative processes.
However, they also recognize that technology is still evolving, with new players constantly entering and exiting the market. CIOs are looking for flexibility, and that means they can pivot quickly if better technology emerges or if an AI tool they’ve invested in isn’t delivering the results they expected. They want to avoid being locked into long-term contracts with suppliers whose products may not keep up with the rapidly advancing state of the art.
For Considering the future of AI in healthcare, this cautious approach may delay adoption in the short term, but ultimately drive more thoughtful and strategic integration of AI into healthcare workflows. As the market matures and more stable, proven systems emerge, we may see healthcare organizations become more comfortable with longer-term commitments.
Until then, flexibility and adaptability will remain crucial. The healthcare industry will need to remain agile, continually evaluating new technologies while ensuring that patient care is not compromised by unproven or quickly outdated systems.
Q. You mention a rapid shift in AI market leadership. Who were and are currently the market leaders, and why these shifts?
A. The dynamic nature of AI means that today’s market leaders may not be tomorrow’s. The AI landscape has seen significant shifts in market leadership due to both innovation and consolidation. A few years ago, big tech companies like IBM Watson and Google’s DeepMind were pioneers in AI in healthcare, especially in areas like diagnostic imaging and predictive analytics.
With the rapid development and new AI players, the market has continued to grow. Startups and niche companies are emerging with highly specialized systems that address very specific healthcare needs, such as AI-driven clinical decision support or AI-based diagnostic tools for radiology and oncology.
Companies like NVIDIA, which provides the hardware backbone for AI development, have become indispensable, especially in areas like machine learning and computer vision. Epic, which is integrating AI into its electronic health record system, is also making significant progress by offering comprehensive, AI-enhanced systems that are more closely integrated with existing hospital workflows.
These companies are leveraging their broader platforms to introduce AI capabilities, which could make it harder for smaller, more specialized vendors to compete unless they offer a truly unique value proposition.
The shifts in market leadership are caused by several factors. First, the rapid pace of AI innovation means vendors must continually update and improve their offerings to stay competitive. Second, the consolidation of AI technologies into larger platforms, such as Epic, reduces the need for standalone AI vendors.
Finally, many healthcare organizations are still dealing with the legal and ethical issues surrounding AI, meaning that companies that can deliver not only innovative systems, but also reliable, secure, and compliant systems will ultimately lead the market. These shifts indicate that the AI landscape will remain volatile until a few clear leaders emerge.
Q. What are the challenges of building versus buying AI tools in healthcare?
A. The decision to build or buy AI tools in healthcare is not an easy one, and each path comes with its own challenges. By building AI tools in-house, healthcare organizations can tailor systems specifically to their needs. They can develop models tailored to their unique data sets and workflows, ensuring AI systems are fine-tuned to their organization’s requirements.
However, this approach requires significant resources, both in terms of financial investment and technical talent. Many healthcare organizations are facing a shortage of skilled AI professionals, and the costs of hiring and retaining such talent can be prohibitive. The ongoing maintenance and updates required to keep internally developed AI tools up to date with the latest developments in the field can further strain resources.
On the other hand, purchasing off-the-shelf AI tools offers a faster route to implementation, with less upfront development effort. These tools often come with vendor support, allowing healthcare organizations to get started quickly.
However, this approach is not without risk. The healthcare AI market is full of vendors, many of which are startups that may not be around for the long term. CIOs have expressed concerns about their commitment to suppliers whose products may not evolve at the same pace as the organization’s needs or whose business models may not be sustainable.
Additionally, off-the-shelf AI tools may not integrate seamlessly with existing healthcare IT, leading to inefficiencies and potentially hindering the technology’s effectiveness.
Another key challenge when purchasing AI tools is vendor lock-in. When a healthcare organization becomes dependent on a particular AI tool, it can be difficult to switch to another tool later if the supplier stops innovating or if a better system becomes available.
This can lead to a situation where the organization is stuck with a suboptimal device, or worse, where the vendor goes bankrupt and the healthcare system looks for alternatives. Healthcare organizations must carefully weigh the risks and benefits of building versus purchasing AI tools, considering not only the immediate costs and benefits, but also the long-term impact on their IT infrastructure and patient care.
Q. What are the key actions healthcare CIOs and other healthcare IT leaders need to take by 2025?
A. As healthcare organizations look to 2025, healthcare CIOs and IT leaders must focus on three critical areas: cloud optimization, talent development and data management. Cloud optimization is crucial because many healthcare organizations operate in a hybrid cloud environment, with both on-premise and cloud-based systems.
Optimizing cloud usage not only enables scalability and flexibility, but also helps reduce costs – an increasingly important factor given the financial pressures many healthcare organizations face. By ensuring their cloud infrastructure is both secure and efficient, healthcare systems can leverage AI and other emerging technologies without being bogged down by legacy systems or excessive infrastructure costs.
Talent development is another key area where CIOs should focus their efforts. There is a significant talent gap in the technology industry, but especially in healthcare IT, especially when it comes to AI and cloud engineering. CIOs must invest in training programs to upskill their existing workforce, while also finding creative ways to attract new talent in a highly competitive market.
This may include establishing partnerships with educational institutions, offering specialized certification programs, or working with vendors to offer joint training initiatives. Upskilling internal teams will be critical to ensuring that healthcare organizations can not only implement cutting-edge technologies, but also maintain and develop them as the industry continues to evolve.
Finally, data management is a top priority for healthcare leaders heading into 2025. As AI and data analytics become more integrated into healthcare operations, ensuring the security, privacy and ethical use of patient data will be of paramount importance. This includes implementing strong governance frameworks that can manage the massive amounts of data being generated, while also meeting regulatory requirements such as HIPAA.
Additionally, CIOs must be proactive in developing strategies to address potential risks associated with AI, such as bias in algorithms or data privacy concerns. Building a strong data governance infrastructure will be critical not only in mitigating risks but also in promoting trust in AI-driven healthcare tools.
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