Is AI Just IA in a Trenchcoat? Reframing AI in Digital Health

The buzz around artificial intelligence is louder than ever. Across many clinical research tools – from eConsent to site monitoring –  nearly every new solution seems to come with an “AI-powered” label. But if we take a closer look, many of these so-called AI innovations are actually intelligent automation systems, simply doing what they’re built to do: operate reliably, predictably, and without the mystery of machine learning.

Is that bad? Not at all – this isn’t about downplaying technology. It’s about being better informed in knowing what we’re working with, what we’re buying, and what we truly need. Understanding these distinctions are essential when working in a space where traceability, compliance, and validation are non-negotiable. In clinical operations, calling everything “AI” doesn’t just stretch the term – it can lead to misaligned expectations and missed opportunities for more grounded, effective solutions.

Let’s break things down further, and demonstrate why this matters.

What’s often labeled as AI is actually intelligent automation (IA). This is a category of technology that automates repeatable, rule-based tasks in order to reduce manual work and simplify workflows. At its core, IA is an evolution of Robotic Process Automation (RPA) – another category which was all the buzz in years past, but very much still plays an important role in today’s technology. While RPA was about automating repetitive tasks in a very rigid, logic-based way, IA builds upon that foundation and adds intelligence – allowing more flexible, dynamic, and “smarter” process handling. To some, it may not sound as flashy as generative AI. However, discerning technology leaders will recognize its value in being:

• Faster to implement
  
• Easier to govern and validate
  
• Better aligned with clinical environments where transparency and predictability matter
  
• Designed to integrate with existing systems instead of replacing them

The difference between AI and IA is more than a technicality. It’s the way these systems function: 

• AI is more about thinking –  it mimics human reasoning to solve problems that require learning or adaptation, like interpreting unstructured data or predicting outcomes (e.g., patient prognosis or care pathway).
  
  
• IA is more about doing – it automates structured, repeatable tasks through a blend of rules (for general guidance) + basic intelligence (to manage messiness and unexpected circumstances). This includes tasks as simple as generating summaries or routing documents, to more complex tasks such as flagging missing data, parsing complex documents (like protocols) into more digestible content, or managing automated visit scheduling.

In many healthcare settings, it’s intelligent automation that is already driving a strong portion of real, measurable improvements. It’s helping clinicians spend more time with patients and less time in front of screens. It supports regulatory compliance without requiring deep technical oversight. And, while many are still navigating a lack of clarity in early AI tools, IA platforms have already established consistency and reliability that teams trust.

Understandably, the hype around AI can create confusion. But with that comes slower decision making and adoption success. Leaders may hear “AI” and assume they’re buying something more advanced than it really is. Users may hear “AI” and assume they’re in for dealing with a technology that oversteps its role. The risk isn't just overspending – it's misalignment. This leads to over-investment of time and money in tools that don’t meet real needs or fear that automation will disrupt essential human judgment in care delivery. By being able to distinguish and identify intelligent automation, it helps reset that conversation back to adopting technology that reduces friction and enables focus.

For now, it’s expected that “AI” will be the banner term used for many new technology platforms and enhancements – whether they're truly AI, IA, or something else altogether. It’s unfortunate and imprecise, but it’s the reality of tech marketing and we can’t really blame vendors at this point. Therefore, understanding what powers these tools is more than just a technical question. It’s a strategic one. As is the case with any technology exploration, teams should be diligent and ask the usual critical questions:

• Is this solving a real problem, or is it adding complexity?
  
• Will this integrate with what we already use?
  
• Can we trust how it works or is it even making decisions at all?
  
• What outcomes should we realistically expect?
  

Ultimately, the industry might benefit from putting less stress on what a tool is called and more about what it can actually do. Our position is that it doesn’t add value to promote trends. There’s more value in helping teams find, implement, and manage tools that make sense in context. Sometimes that means helping teams see that what’s under the AI label might really be something like a well-designed IA system, and that’s often exactly what’s needed.

A proper comparison of AI and IA isn’t about which can “do more” – it’s about seeing which tool is a right fit in the right place. Progress doesn’t always mean a giant leap. Sometimes, the smartest move is a well-placed step that fits the way people actually work. Looking for clinical tech strategy, delivery, and management that’s unified – regardless of how it’s labeled? Schedule a free consultation with Unifora at the link below.