Healthcare is the leading sector in attracting AI investments, going beyond research and development of drugs into chemical analysis, data analytics, and improving clinical trial recruitment. Despite increasing investments in AI, many healthcare, pharmaceutical, biotech, and life sciences organizations find it difficult to get their AI use cases into production.
Inefficient and fragmented processes create an environment ripe for failure. These operational breakdowns also make it difficult to recognize how establishing an AI governance framework can help companies keep up with the pace of innovation, get visibility into initiatives, while also reducing risks.
Even if your organization can produce viable AI use-cases, getting a generative AI project into production takes time, which translates into cost — and that’s not to mention the fragmented systems and processes, which are among the many challenges to AI governance adoption.
It seems that time — time being money — and governance are at odds. For any company, demonstrating ROI remains the priority for initiatives. The ever-present pressure to produce value and a competitive advantage leads to the fallacy that skipping AI governance will save time and speed progress. But establishing AI governance ensures that AI technologies are implemented safely, ethically, and effectively.
Which is why consistent, easy-to-understand documentation provides the critical information leaders need for better transparency and improved decision-making. Think about nutrition labels. Yes, the ones on the back of every box of cereal and bag of chips that you look at to quickly know if the fat, sugar, or sodium is within your tolerance before tossing it in the cart or placing it back on the shelf. The nutrition labels make it easy to decide. AI initiatives should be able to provide the same level of transparency consistently.
Similar to a nutrition label, an applied model card provides decision-makers with an AI initiative overview, intended use, bias, risks, warning, metadata, security, and maintenance data, and metrics for performance, fairness, safety, and reliability. Success means bridging the disconnect from the technical team like solution owners and developers, the executives and compliance, and the end users like patient and healthcare professionals by providing:
- Better transparency (e.g., patients and care providers get more information about how AI influences diagnoses, treatment recommendations, or resource allocations)
- Higher trust (e.g., documented bias risks, intended uses, limitations increases confidence that AI is fair, ethical, and medically sound)
- Improved safety (e.g., help ensure AI tools meet clinical standards before deployment, reducing the risk of misdiagnosis or inappropriate treatment)
- Faster innovation (e.g., standardized documentation streamlines regulatory approval and clinical validation, meaning patients access new (but safer) AI-driven innovations faster)
The Coalition for Health AI (CHAI), which aims to advance the responsible development and oversight of AI in healthcare, says that a model card (the nutritional label for AI) is a critical component to the entire AI lifecycle — not just the production phase. It can help ensure consistency, completeness, and clarity around proposed AI solution during intake, document intended usages, data usage, metadata, and risk in development, update changes made during review and validation, publish and share data to provide consistent and easy to understand model info during production, and even provide updates during monitoring and auditing phases.
Having numerous, manual processes creates risks and inefficiencies, and makes it difficult to have a standardized approach to AI governance too. This lack of standardized automation will inevitably slow down compliance efforts later in your process, ultimately, delaying moving your AI initiative into the market. On the other hand, operationalizing your AI governance by automating documentation will improve transparency and collaboration across departments which in turn will accelerate the rate and scale of innovation so you can see a return on investment sooner. Considering up to 80% of enterprises have 50 generative AI use cases in the pipeline, but only a few make it into production, it is clear that there is room to improve the process. Faster deployment is possible but it starts with better governance, streamlining intake, clarifying ownership, standardizing, and automating documentation to see more efficiency, and then scaling these practices to reduce risk and accelerate AI initiative adoption.
Those who recognize that AI governance is an innovation enabler will be able to align business priorities and AI investments — and make smarter decisions about which models to accelerate, and which to retire — ultimately leaving the enterprises better positioned to achieve ROI on their AI. The sooner healthcare organizations realize that AI governance is a catalyst for innovation, trust, and transparency, the sooner they will realize process efficiencies and speed to market, creating a competitive advantage.
Photo credit: MR.Cole_Photographer, Getty Images
Pete Foley is CEO of ModelOp, the leading AI lifecycle automation and governance software, purpose-built for enterprises. It enables organizations to bring all their AI initiatives – from GenAI and ML to regression models – to market faster, at scale, and with the confidence of end-to-end control, oversight, and value realization.
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