Beyond AI/ML FOMO
The Answer to Every Question Seems to Be AI
In the boardrooms of nearly every healthcare and life sciences organization today, one phrase echoes louder than most: “We need to be doing something with AI.”
And that’s not necessarily wrong.
Artificial Intelligence (AI) and Machine Learning (ML) technologies hold enormous potential to improve patient outcomes, drive efficiencies, accelerate R&D, and support clinicians. And unlike in the past, clinicians are actually open (if not hungry) to adopt AI/ML into their work in healthcare and life sciences. They’re desperate for help, so they would love if AI/ML could be the key to making their work and their lives better.
But the rush to “do something” often leads to scattershot pilots, unscalable tools, and (too often) disillusionment.
The FOMO Is Real!
The pressure to not be left behind is palpable. Executives hear about AI breakthroughs weekly. Investors want innovation. Regulators are scrambling to catch up. Everyone wants to be seen as a leader.
But when they start talking about “how” this will play out, most organizations hit the same walls:
They don’t know where AI fits within their current operations.
They’re unsure how to fund, scale, and sustain AI initiatives.
They realize that their massive datasets are messy, siloed, and not ready for intelligent modeling.
In short: The vision is grand, but the infrastructure and roadmap are unclear.
Start With the Foundation: Data Readiness
Many leaders underestimate how critical data quality is to any AI strategy. You can’t build intelligent models on disjointed, ungoverned data. Before launching flashy pilots, organizations must:
Invest in data cleaning and harmonization (or at least partner with an organization that will help with this effort)
Establish strong governance and data security protocols
Prioritize interoperability across systems and platforms
Without this, even the best AI models will fall short.
Define ROI Early and Often
AI/ML is not cheap. From infrastructure to talent, investments add up fast. That’s why every initiative must be anchored in a clear business case:
What metric will this improve?
How will we measure ROI?
What workflows must change to ensure adoption?
AI needs to be more than exciting. It must be economically sustainable.
Cross-Functional Collaboration Is Essential
AI is not just an IT or innovation project. For real-world clinical or operational impact, it must be shaped by:
Clinicians, who understand workflow and patient risk
Product leaders, who can translate pain points into requirements
Data scientists, who can fine-tune models with the right context and minimal hallucinations or bias
Operational leaders, who can manage implementation and scaling
Success lies at the intersection of technology, medicine, and operations, not in any one silo.
Treat AI as a Tool, Not a Trophy
The goal of AI isn’t to look innovative. It’s to deliver value and impact. That means designing solutions that:
Fit seamlessly into existing workflows
Support human decision-making (not replace it)
Are explainable, auditable, and grounded in clinical logic
Solve real, measurable problems
Flashy demos are fun. But the AI tools that stick are the ones that disappear into the background and quietly make things better.
Moving from FOMO to Focus
AI and ML will absolutely reshape the future of healthcare but only for the organizations willing to do the hard, unglamorous work to get there, which means:
Aligning innovation goals with core business and care delivery objectives
Investing in foundational data and governance capabilities
Building cross-functional teams that reflect the complexity of healthcare
Piloting with purpose and scaling with discipline
Let’s move past the hype cycle and into meaningful, measurable, patient-centered transformation.
I know. I’m a wet blanket.
But you cannot build a beautiful house if there isn’t a solid foundation, and you certainly shouldn’t be building that house just because your next door neighbor remodeled.
