AI Literacy Is Not One-Size-Fits-All: What Healthcare Leaders, AI Leaders, and Implementation Teams Need to Know

AI is moving too quickly for healthcare organizations to leave AI literacy to the technology department. But that does not mean everyone needs the same level of expertise.

As healthcare organizations accelerate their adoption of artificial intelligence, one issue is becoming increasingly clear: We need to build AI literacy across the organization.

But AI literacy does not mean turning every employee into a data scientist.

A board member does not need the same technical knowledge as someone building an AI-enabled workflow. A compliance officer does not need to know how to code an AI agent, and an implementation team does not necessarily need the same strategic perspective as the executive team.

What each group does need is enough knowledge to perform its role responsibly. This distinction is critical: AI literacy in healthcare should be distributed across the organization—but tailored to responsibility.

AI Literacy Is Becoming a Leadership Competency

For years, healthcare executives have relied heavily on CIOs, CTOs, data teams, and IT departments to explain emerging technologies. AI changes that equation.

When AI can influence clinical workflows, documentation, coding, reimbursement, compliance, workforce decisions, patient communication, and business operations, leaders cannot completely delegate their understanding of it.

They don't need to understand every technical detail, but they do need to understand enough to ask the right questions.

That may be one of the most important distinctions in AI leadership: Executives don't need all the answers. They need to know which questions to ask.

Level 1: The Executive and Board

At the executive and board level, AI literacy should primarily be strategic and governance focused.

Leadership should understand enough about AI to evaluate strategic value and ROI by asking what problem the organization is solving, why AI is appropriate, what measurable outcome is expected, and how the investment supports organizational strategy.

Executives and board members also need governance and risk literacy. They should know what data the system accesses, which decisions it can influence, what it can do autonomously, where human approval is required, what happens when it is wrong, and who is accountable. From an investment and pace perspective, they must decide whether to build, buy, or partner; whether the organization is moving quickly enough or outrunning its governance capabilities; and which pilots deserve further investment or should stop.

Boards do not need to understand how to orchestrate multiple AI agents. But they should understand enough about agentic AI to recognize that a system capable of taking actions presents different governance considerations than one simply generating text.

That is responsible oversight.

Level 2: The AI Leader

Between executive strategy and technical implementation sits another increasingly important role: the AI leader.

This person—or leadership function—has to connect two worlds. They need enough technical fluency to work effectively with technical teams while understanding the business well enough to communicate with executives, compliance professionals, clinicians, operational leaders, and the board.

Their job is not simply to champion AI. It is to translate strategy into execution.

That requires the ability to identify the organizational problem, determine whether AI is appropriate for the workflow, and decide how the workflow should be redesigned. The AI leader must define what AI should do, what remains human-owned, what risks are introduced, what governance is required, and how success will be measured. Just as importantly, this person must explain those decisions to stakeholders with very different levels of technical understanding.

The AI leader becomes the bridge between possibility and practicality.

That is a very different role from simply knowing how to use AI tools.

Translating Technical AI Into Business Language

I believe this will become one of the most valuable AI leadership skills. Technical teams may discuss LLMs, tokens, context windows, retrieval, APIs, models, agents, orchestration, confidence thresholds, and vector databases. Executives, however, are asking what the technology does for the organization, what it will cost, what could go wrong, what will change, what value it will create, and who is accountable.

Healthcare AI leaders need to be able to translate between those conversations.

For example, an executive team may not need a technical explanation of orchestration. It needs to understand that the AI can coordinate several steps of a workflow but cannot independently release a client-facing recommendation because that requires human approval. That is technical architecture translated into business, governance, and accountability language.

Level 3: The Implementation Team

The implementation team's AI literacy needs to be much more operational. These are the people who help turn strategy into a functioning workflow.

Implementation teams need workflow redesign literacy so they can determine where AI enters the process, which steps can be eliminated or automated, where human judgment remains essential, and how handoffs should work. They also need data-readiness literacy to evaluate whether information is reliable, current, appropriately structured, access-controlled, and traceable to its source.

Their technical understanding should cover agent fundamentals and orchestration, including whether the workflow needs an Assistant, Analyst, Tasker, Orchestrator, or Guardian Agent; how those capabilities interact; and what happens when something unexpected occurs. Finally, they need testing and feedback literacy to determine whether the system works, test accuracy, capture errors, respond when humans disagree with AI, and improve the workflow over time.

This is where AI transformation becomes real.

Human Oversight Must Also Be Designed

One of the implementation concepts I believe deserves particular attention in healthcare is human-in-the-loop design.

It is not enough to say, "We'll have a human review it." The workflow must define where and when review occurs, who performs it, what the person is reviewing, which evidence they will see, and what authority they have. It must also establish what causes the system to stop, what triggers escalation, and what happens if the reviewer disagrees with the AI. These decisions need to be designed into the workflow. And I believe healthcare needs to go even further. This is where my concept of Human-in-Governance™ becomes important.

Human-in-the-loop determines where a person participates, while Human-in-Governance™ determines where human authority resides. Humans establish the rules, determine what AI may and may not do, set escalation thresholds, retain defined consequential decisions, monitor the system, and remain accountable.

Don't Forget the Domain Experts

There is another group that cannot be left out of this conversation. The people who actually understand the work.

In healthcare, that might include physicians, nurses, coders, compliance professionals, auditors, revenue cycle professionals, privacy professionals, quality leaders, and operations teams.

These individuals may not be AI experts, but they possess something the AI team may not have: domain expertise.

A developer can build an extraordinary AI system and still misunderstand the regulatory nuance of a Medicare billing requirement.

A data scientist can create an excellent model and still not recognize that a particular payer policy creates an exception.

A vendor can build a sophisticated coding assistant and still fail to understand how the workflow actually operates inside a particular organization.

That is why domain experts need enough AI literacy to participate in the design. The people who understand the work must help design how AI participates in the work.

Different Roles. Different Skills. Shared Responsibility.

This is where organizations sometimes get AI training wrong. They purchase one AI training program and give it to everyone. But a board member, compliance officer, operational leader, physician, and AI developer do not need the same training.

A more mature approach is role-based AI literacy.

The Board and Executive Team need strategic, risk, governance, investment, and measurement literacy. The AI Leader needs strategy, technical fluency, governance design, communication, and change leadership. The Implementation Team needs capabilities in workflow, data, agent architecture, testing, controls, and iteration.

The Domain Experts need practical AI literacy, validation skills, governance awareness, and the ability to recognize when an AI output does not make sense. The broader workforce must understand appropriate use, organizational expectations, data boundaries, limitations, and escalation requirements. These roles require different knowledge and carry different responsibilities, but they share accountability for responsible adoption.

AI Literacy Is Also a Risk Control

There is another reason healthcare organizations should invest in AI literacy: Education itself is a governance control.

Employees who do not understand AI may trust it too much or too little, enter inappropriate information, fail to validate outputs, use unapproved systems, or misinterpret AI confidence. They may fail to recognize hallucinations, ignore an incorrect recommendation because "the computer said so," or avoid useful technology entirely because they do not understand it.

Policies alone will not solve this. People need to understand why the rules exist.

AI Literacy Cannot Be a One-Time Training

Healthcare compliance professionals already understand this principle: annual training alone does not create a culture of compliance. The same will be true for AI because the technology is changing too quickly.

AI literacy needs to become an ongoing organizational capability.

That may include leadership briefings, role-specific education, use-case workshops, governance updates, scenario-based training, lessons learned from AI incidents, vendor demonstrations, AI champions, and regular discussions about emerging capabilities and risks.

The goal is not to keep everyone informed about every new AI development; that would be nearly impossible. The goal is to build an organization capable of learning continuously.

The ProCode Perspective

AI is moving at warp speed. The organizations that succeed will not be those in which a handful of technology professionals understand AI while everyone else waits for instructions. They will build AI literacy throughout the organization, but they will do it intelligently.

The board doesn't need to build an agent, the compliance officer doesn't need to become a programmer, the physician doesn't need to understand model architecture, and the developer doesn't need to become a Medicare compliance expert. They need to know enough about each other's worlds to make good decisions together.

That is the real opportunity behind distributed AI literacy.

Healthcare needs executives capable of governing AI, AI leaders capable of connecting strategy to execution, implementation teams capable of building responsibly, domain experts capable of challenging the technology, and employees who understand how to use AI appropriately.

AI transformation will not happen inside the IT department. It will happen across the organization. The organizations that build the knowledge to lead that transformation—not simply the technology to participate in it, will be far better positioned for what comes next.