Artificial intelligence is progressing from hype and speculation to practical adoption in healthcare tech. The opportunities are endless, and the potential pitfalls are real, too, as I detailed earlier this year.
As healthcare organizations begin to examine how AI will be integrated into their workflows, it’s essential to consider the best practices that should be in place with their tech partners.
Innovation, with risk mitigation in mind
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McKinsey’s 2025 tech outlook says AI is “the accelerant” for innovation; I tend to agree, but there are boundaries needed around transparency, the degree of automation, and how the AI is regulated.
America’s “AI Action Plan,” introduced by the White House in July, centers on innovation, with less focus on risk mitigation than the European Union’s Artificial Intelligence Act.
Health technology innovators must focus on both: innovation and risk mitigation. Because, in healthcare, trust is currency. Every tool leveraging AI must be built with compliance, security, and regulatory readiness in mind.
Partners can start small but have big impact
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Technology partners should view their product offerings as a way to enable healthcare organizations to reach their own AI vision. Through interoperability and data backed by security measures and compliance knowledge, the industry can move toward frictionless operations that inform care, routing, and adherence in new ways.
AI can be an additional layer within the solutions currently used. Technology partners can be looking at how AI can support and enhance operations, workflow, or cost efficiency, versus overhauling or replacing solutions, and have a huge impact while maintaining security and compliance.
Intentional implementation: The MEG approach
AI adoption should never be an end in itself. To ensure every initiative delivers measurable value, tech leaders can take a structured approach like the MEG framework: Maintenance, Experience, and Growth.
- Maintenance covers the must-dos — the foundational investments that safeguard reliability, compliance, and security.
- Experience represents the should-dos — improvements that enhance usability, trust, and long-term differentiation.
- Growth drives the new initiatives — AI-driven innovations tied directly to clear ROI and measurable outcomes.
By sequencing investments this way, organizations can integrate AI responsibly, balancing innovation with stability and trust.
Tools to make it happen
To reduce friction, save time, and create a qualitatively superior experience, there are key tools and operations that technology partners must adopt to enable AI innovation at scale, including comprehensive API documentation, robust FHIR adoption, and defined data retention and security policies
With these foundational components in place, technology partners are poised to help providers spend less time on admin work and more time on patient care, and help payers and pharma to have clearer visibility into ROI, adherence outcomes, and real-world evidence. It also helps health tech vendors scale more quickly and safely.
AI can be used to focus on scale, savings, and informed decisions – with the right tools and partners in place. Imagine being able to view prescription price transparency, powered by AI analysis of available coupons, and deliver personalized and easy-to-understand medication information directly to patients. AI can also significantly reduce callbacks about prior auths, pharmacy routing, and follow-up questions
When I reflect on the trajectory ahead, I’m energized by the possibilities of integrating AI into technology, bringing measurable value and seamless workflows into healthcare. If technology partners can safely use AI to help power better, faster, more transparent decisions across the care continuum, we will have done our job. And if we remember to start with the patient first, we’ll solve the right problems.
Photo: Gerasimov174, Getty Images
Julian Herbert started his career in tech product development as a business analyst focused on e-commerce in the semiconductor industry. Following his curiosity, he became a management consultant at Deloitte and led M&A divestiture and integration engagements in a variety of industries, including pharma and biotech. He then transitioned back into product development and e-commerce at Amazon, launching machine learning solutions for 3rd party sellers on the platform. Julian was also tasked to lead product development for AWS Startups, building their first micro-targeted product line.
At DoseSpot, Julian leads product innovation, helping the company grow and deliver safe and reliable ePrescribing technology and software integrations across multiple healthcare markets. Julian is originally from Louisiana and graduated as valedictorian from Southern University in Baton Rouge with a B.S. in Computer Science. He also has an M.B.A. from The Ross School of Business at the University of Michigan, focusing on strategy and entrepreneurship.
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