What CIOs Need to Know Before Buying AI in Healthcare

Healthcare AI is changing quickly. There are many bold claims and flashy demos on the market because of advances in generative models, predictive analytics, and autonomous systems. But putting things into practice in the real world, especially in the life sciences, is anything but easy.

These days, pharma leaders aren’t buying hype; they’re buying results.

Chief Digital Officers, CIOs, and Regulatory Heads are no longer in the novelty phase. They’ve seen the product demos, heard the promises, and watched a lot of pilots stop working after the demo stage. What they need now are platforms that work in the real world, across borders, on a large scale, and with government oversight.

In life sciences, compliance, safety, and scalability aren’t just features; they’re things that have to be there.

For AI to go from being a novelty to actually changing how businesses work, platforms need to show that they can work in regulated environments, with sensitive data, and with built-in human accountability.

In the pharmaceutical industry, it’s not enough to know what AI can do; you also need to know what it can do safely, reliably, and responsibly.

This blog lists the most important things that CIOs need to think about before buying or expanding AI tools for use in healthcare and pharmaceuticals.

1. Define the Use Case with Surgical Precision

Start with the problem, not the technology.

Ask: Which part of the business do we want to make better?

What is the current cost or lack of efficiency?

Is AI the best option, or is regular automation enough?

Some examples of well-defined use cases are:

• Sorting through incoming MedInfo requests
• Making drafts of medical content automatically
• Information about the feasibility of trials
• Don’t give vague orders like “add GenAI to all HCP engagement.” Accuracy wins.

2. Check to see if AI is ready: data, people, and process

Your company needs the following before any AI model can be useful:

Clean data: Structured, free of duplicates, labelled, and compliant

People who have been trained: People who work in Med Affairs, Safety, and Regulatory need to know how to use AI.

Processes that are in sync: AI needs to work with tested workflows.

Before choosing a vendor, use a readiness scorecard. A 30-day AI readiness audit is often the first thing that Newpage clients do.

3. Look at the platform, not just the model

Look at the features of the platform around you:

Does it let people keep an eye on things?

Can you track and check prompts and outputs?

Can it use RAG (Retrieval-Augmented Generation) to base answers on approved content?

Does it protect your IP and keep your data separate?

It’s advantageous if it integrates seamlessly with your CRM, such as Salesforce Health Cloud, Veeva, etc. 

4. Compliance comes first, AI that is used in controlled settings must:

Support versioning and audit trails

Limit access based on roles and areas

Keep track of every AI-generated decision or content draft

Follow the rules set by GxP, HIPAA, and GDPR.

Ensure that your vendor can provide you with validation documents, or collaborate with partners like Newpage who can add that layer.

5. The Total Cost of Ownership (TCO)

There are many different ways to set prices for AI:

• Pay-per-query (LLMs through APIs)
• Licenses for seats (like copilots)
• Enterprise licenses with set limits on use

Don’t forget about hidden costs:

Quick engineering and fine-tuning

Tagging and checking content

Management of training and change

Pick scalable models based on how much you think you’ll need and how important it is.

6. Managing change is key to ROI

AI doesn’t just work right away. Without these things, even the best tools will fail:

Stakeholder buy-in, especially from Medical and Compliance

Training that makes it clear what AI can and can’t do

Metrics for adoption (usage, accuracy, impact)

Make an enablement plan right away. Before going live, we do AI onboarding sprints with domain teams at Newpage.

7. Build AI That’s Responsible by Design

At Newpage, every AI onboarding sprint starts with domain teams, not just data scientists. Before anything goes live, we work with stakeholders to align on what responsibility looks like in practice.

What you should be asking your AI vendors:

• How do you detect and mitigate bias?
• What safeguards are in place for model drift?
• Can your system explain its decisions in plain language?

Push for transparency:

• What data was the model trained on?
• How is your data protected?
• What governance ensures ethical use?

Responsible AI isn’t a feature. It’s the foundation.

8. Don’t Choose Hype. Choose Domain. 

AI in healthcare isn’t plug-and-play. Generic vendors often miss the mark when it comes to the complexities of regulated environments.

Here’s what to look for instead:

• Real-world life sciences or healthcare use cases
• Domain-specific models, ontologies, or vocabularies
• Validation workflows that meet pharma-grade standards
  

Or better yet, choose partners like Newpage who build AI that’s compliant, contextual, and truly useful.

The Bottom Line: AI in Healthcare Is a System, Not a Tool

AI done right isn’t just a quick win. It’s a reliable, scalable layer in your operating system.

When embedded thoughtfully, AI doesn’t just cut costs. It accelerates delivery, improves safety, and sharpens strategy.

Newpage helps CIOs and CTOs in life sciences find, test, and use AI that is right for their mission, not just the moment.