AI will be the difference between success and failure for healthcare organizations within 10 years. Those who have safely adopted AI technologies and utilized them to solve key challenges will have a significant competitive advantage over those who have not. But this will not happen just based on who has the most capital to spend, and the gains won’t appear all at once. Instead, success or failure will hinge on the ability to apply AI to further existing strategies and will gradually grow over years.
Key indicators of success will be whether organizations put their strategy or their technology first, their willingness to move on AI, and their incremental — not immediate — success in adoption. We see three paths that organizations are already starting down:
Organizations in this group are acting quickly — maybe too quickly — on AI. They have an organizational “AI strategy” that actively encourages the exploration and deployment of interesting, novel use cases. They also have a decentralized approach to adoption of use cases that are relevant to their business units. By 2030, these organizations see every problem as a problem for AI to solve. The scattershot approach to investment has resulted in technological bloat, wasteful or duplicative spending, and has muddied the overall organizational strategy. These organizations have learned and successfully adopted AI, but they have not achieved key benefits to the overall organization or solved any pressing healthcare challenges.
Organizations in this group are hesitant to act on AI. Challenges like investment, implementation, and risk-aversion cause this group to, at best, wait and watch, or at worst, fully ignore the technological advances around them. By 2030, they will have fallen behind organizations that have capitalized on AI. Catching up will be particularly difficult as they lack the institutional knowledge other organizations have gained along their AI journey. These organizations may find their traditional role in the healthcare system and power in local markets eroded as other sectors and competitors move ahead.
Organizations in this group are moving now on AI in a systematic, potentially slower manner than headfirst enthusiasts. They only invest in use cases that will meaningfully impact their overall strategy instead of seeking out the most interesting AI use cases. By 2030, these organizations will have realized significant benefits from their AI investments. Depending on the organization and their strategic goals, benefits could appear as improved outcomes, reduced costs, faster drug development, more engaged patients or members, and/or more support for their workforce.
Every organization should be striving to be in that third group, but today many are falling into one of the first two groups, where they’re moving too slowly or too fast. So, what do organizations need to do to move to the third group and guarantee long-term AI success?
The first, and most impactful change, is to realize that you should not have an AI strategy. Rather, AI should enable your existing strategy. That is to say, the best AI strategy is not about AI — it’s about you and your organization. This key distinction is what keeps organizations from getting caught up in the hype or investing in technologies that are “nice to have” instead of “need to have.”
You should also consider how AI can evolve your strategy over time, specifically looking at healthcare challenges or goals previously considered unsolvable or unattainable. This mindset allows you to creatively apply new capabilities while staying focused on specific needs, such as mitigating the workforce shortage, closing care gaps, and improving health equity.
The second change organizations need to make is to assess your pace of adoption in the face of potential pitfalls and challenges. You will want to avoid moving too quickly as this can lead you to ignore AI pitfalls such as bias. At the same time, you don’t want to allow pitfalls, or hesitancy around challenges, to slow you down.
Organizations can only find a balanced pace and mitigate risks if they are willing to meet challenges head on. Instead of letting a fear of bias or errors keep you from investing, let it drive your approach to investing. Watch and learn from the successes and failures of early adopters. Take the time to build internal processes and staff expertise that will help solve these challenges for your organization. And you should consider your partnerships as well, revamping your processes for choosing a partner to make sure they have a similar approach to tackling these challenges.
Lastly, set up your organization for success with thoughtful governance and decision-making processes. Once you take that step, pick a high-impact, low-risk use case that aligns with your existing priorities. Test out your governance and decision-making processes on that use case and see where they do and don’t work. From there, re-evaluate those processes, and maybe even re-evaluate some of your larger strategies based on any new capabilities or ideas your team has surfaced. Keep repeating this process with other high-impact, low-risk use cases. This stepwise approach ensures your organization limits risk, learns from mistakes, and sees progress on a few impactful use cases.
You have to realize this is going to be hard. There are many challenges to overcome. Legislation isn’t going to solve this for you. All that means is that you both can’t jump in too quickly or not move at all. Instead, you have to work against those challenges in a systematic manner so that you are prepared to recognize the long-term benefits of investment when your organization is ready.
Even more importantly, keeping a focus on your biggest strategic goals will keep you from falling into the trap of only focusing on obvious 1-to-1 replacements of existing processes and instead will allow you to potentially find more creative, less intuitive uses of AI that meet the root causes of your challenges.
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