When it comes to artificial intelligence in healthcare organizations, many leaders are still asking where it can deliver meaningful value. Before taking on his new role in the healthcare industry, we had the opportunity to sit down with IT Implementation Expert AJ Harris and capture his perspective as he transitioned into the sector full-time., With experience spanning both consulting and healthcare, he highlighted that one of the most practical starting points is reducing the administrative workload surrounding patient care.
The key is approaching AI as a tool that supports organizational strategy rather than implementing it for its own sake. That starts with asking why a change is needed and what success looks like. Successful adoption depends less on the technology itself and more on process design, data readiness, and change management.
Understanding Where AI Can Add Value in Healthcare
Artificial intelligence is now a regular topic in healthcare leadership discussions. Many organizations know they need to explore it, but the practical question remains: where does it actually make a difference?
In many cases, the conversation starts with urgency. Leaders are hearing that organizations that fail to adopt AI risk falling behind but translating that pressure into a practical plan can be difficult.
AJ Harris spent much of his time at Stratford helping organizations implement complex technologies within real operational environments. In conversations with healthcare leaders and clinicians, he often heard the same challenge. There is no shortage of discussion about AI, but there is less clarity about how to apply it in ways that improve day-to-day operations.
One way Harris encourages organizations to think about the technology is to separate clinical applications from operational ones.
Clinical tools such as diagnostic support systems are advancing quickly. Radiology is a common example, where AI can help identify patterns in images so specialists can review cases more efficiently. These tools continue to evolve and hold significant promise.
However, many clinicians are just as interested in another opportunity: reducing the administrative workload surrounding patient care.
“There’s a lot of administrative burden in healthcare,” Harris explains. “The more we can reduce that burden, the more time physicians can spend with patients.”
Reducing Administrative Burden with AI
Administrative work consumes a significant portion of clinicians’ time. Documentation, scheduling, chart updates, and follow-ups often happen outside of patient interactions and can extend well beyond clinic hours.
From Harris’s perspective, this is where AI can begin delivering practical improvements today.
Technologies such as automated medical scribing can capture conversations during appointments and generate structured summaries for patient records. Instead of documenting every detail manually, clinicians can review and confirm notes that have already been organized.
Other systems can identify when certain criteria are met and trigger follow-up care automatically. Scheduling tools can also help coordinate appointments and patient interactions more efficiently.
As Harris notes, many of these improvements target repetitive tasks that clinicians handle every day.
“If the system can identify that certain criteria have been met and automatically trigger a follow-up step, that saves the physician from having to do it manually.”
Individually, these tasks may seem minor. Collectively, they represent a significant share of the time physicians spend outside of patient care.
AI Is a Tool, Not a Strategy
Looking at AI through an operational lens also helps organizations approach the technology more realistically.
Harris emphasizes that AI should not be treated as a strategy on its own. It is simply another tool that can support broader organizational goals.
“AI is a very powerful tool,” he says, “but it’s still a tool. You apply it to your strategy and to the outcomes you’re trying to achieve.”
This perspective is particularly important in a market where new AI products appear almost daily. Organizations can feel pressure to adopt something quickly in order to keep pace with industry conversations.
A more practical approach, Harris advises, is to start with existing priorities and to clearly define why a change is needed. Whether an organization is trying to improve patient access, reduce administrative overhead, or improve staff efficiency, the first step is identifying where those goals intersect with processes that technology could improve.
Once that alignment is clear, the conversation shifts from How do we implement AI? to a more productive question: Where could this tool help us achieve something we already want to accomplish?
Why Change Management Matters More Than the Technology
Even when organizations identify a strong opportunity, implementation can still stall.
In Harris’s experience, the technology itself is rarely the most difficult part.
“Turning on the technology usually isn’t the hard part,” he says. “The challenge is everything around it.”
Successful implementation depends on reviewing existing processes, preparing data, and ensuring that staff understand how new tools will affect their work.
Change management plays a particularly important role. AI can introduce uncertainty for clinicians and staff, especially when its capabilities are not well understood. If people are unsure how a new system will help them, or if it appears to complicate their workflow, adoption will be slow regardless of how advanced the technology may be.
Engaging the people who will use the system early in the process can make a significant difference. When clinicians and staff understand the purpose of the change and see how it can reduce their workload, they are far more likely to support the transition.
Data Readiness Is Often the First Real Step
Another practical consideration is data.
AI systems rely heavily on structured, reliable information, and organizations often underestimate the work required to prepare it.
“AI needs data,” Harris explains. “If the data isn’t organized and clean, the technology itself won’t deliver much value.”
Healthcare organizations are often better positioned than they realize. Privacy regulations and patient protection requirements have already pushed many organizations to establish strong data governance practices.
Expanding those frameworks to support AI can help address concerns about data use while allowing organizations to take advantage of the information they already manage.
When Leadership Asks “What’s Our AI Plan?”
For many organizations, the conversation about AI begins at the leadership level. Boards and executive teams are hearing strong messages about the pace of technological change and the need to adapt quickly. Industry experts often warn that organizations that fail to adopt AI risk falling behind their competitors.
That sense of urgency is understandable. The technology is advancing rapidly, and new tools are appearing almost daily.
At the same time, Harris encourages organizations to approach the question thoughtfully rather than reactively.
The pressure to “implement AI” can sometimes lead organizations to focus on technology before fully understanding where it can create value. A more effective starting point is asking why the organization is seeking to make a change and what success will look like. This helps clarify the value proposition and determine whether the investment is worthwhile.
From there, organizations can look at where time is being lost to administrative work, where workflows create friction, and where data could support better decisions. Technology then becomes a way to support those improvements.
This principle applies well beyond healthcare. Any organization navigating rapid technological change benefits from aligning innovation with strategy, preparing its data and processes, and bringing the people who will use the technology into the conversation early.
When leaders approach AI this way, the discussion shifts from “What technology should we implement?” to “What outcomes are we trying to achieve?”
That shift often makes the difference between experimentation and meaningful improvement.
Turning AI Interest into Practical Operational Improvements
Technology adoption is most successful when it starts with clear goals and a practical implementation path.
Stratford works with organizations to evaluate where emerging technologies such as AI can support operational objectives, design implementation frameworks that align with strategy, and guide the process, data, and change management considerations required for successful adoption. If your leadership team is exploring how AI may fit within your operations, we can help you move from early discussion to a structured plan for implementation. Explore our AI services or reach out to a member of our team.
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