By the time an AI platform reaches my team’s tool stack, it has usually already made a strong first, second, and third impression. I've usually seen a polished demo and heard plenty of success stories along the way. On paper, it's almost always easy to see the potential for a new AI tool to address a known problem. But after years of leading technology implementations, I've learned that the hardest questions aren't answered during a walk-through or addressed by a customer testimonial. They're felt months later, when the technology becomes part of someone's workday.
As a chief nursing officer, my job isn't just to evaluate whether a technology works. It's to evaluate whether it works for our people.
Nursing leaders are protective of clinical labor. Every new technology asks something of the staff using it, whether that's learning a new workflow, changing daily routines, or trusting a new process. If a tool creates more work than it removes, adoption becomes an uphill battle from the beginning. And that doesn’t even consider the retention and iteration required to scale the results of any technology project.
That's why these are the five questions I come back to before we move forward with any AI initiative.
1. What problem are we actually trying to solve?
Every AI platform promises to solve an important problem. Before evaluating whether it can, I want our leadership team to agree on the problem we're trying to solve.
- Are we trying to improve communication between teams?
- Reduce delays?
- Give staff better visibility into how the day is unfolding?
- Improve scheduling?
- Support safer staffing decisions?
If we aren't internally aligned on the problem, it's difficult to evaluate whether a technology is the right fit. It's easy to be impressed by tool capabilities that don't address the challenge we're actually trying to solve. Technology has to fit both the organization's priorities and the people’s needs.
Learn how Houston Methodist used its block schedule to 3x one surgeon's daily cases.
Read the case study
2. How will this fit into our teams' day?
A technology can be incredibly sophisticated and still fail if it doesn't naturally fit into the way people work. I want to understand exactly how the people using it will interact with it.
- What information will they receive?
- When will they receive it?
- Does it simplify decisions or create another screen to monitor?
- Does it eliminate work or introduce another task?
Just as importantly, I want the people closest to the work involved in those conversations early.
Every department has trusted voices. Bringing those individuals into the evaluation process doesn't just produce better feedback; it helps ensure the technology is shaped around the realities of clinical work.
3. How will we use the data this technology gives us?
This may be the most important question of all. People don't decide whether they trust AI because of the algorithm. They decide based on how leaders use the information it produces.
The same data can lead to very different decisions. It can be used to assign blame or to uncover the operational barriers preventing people from succeeding.
Before implementation begins, staff deserve to understand how data will and won't be used.
- Will it help identify workflow challenges?
- Inform staffing decisions?
- Improve coordination?
Those conversations shouldn't happen after go-live. They should start before the contract is even signed and continue for the duration before implementation ever begins.
4. Will this continue creating value after implementation?
Technology shouldn't solve today's problem only to create tomorrow's.
I always want to understand what success looks like beyond implementation. Once we've achieved the initial goals, how will this technology continue helping us improve? How will it adapt as our priorities change? And how will the vendor continue partnering with us over time?
The best technology relationships don't end at go-live. They continue creating value as the organization grows and its needs evolve.
5. What will this require from our people?
Software has a purchase price, and implementation has a people cost. As the CNO, I need to understand both.
- How much time will leaders spend implementing it?
- Who will own it after go-live?
- What ongoing education, governance, and reporting will be required?
- Most importantly, what work disappears now that this technology exists?
For nursing leaders, those people factors are often the determining success factor.
If a technology simply shifts work from one place to another, we haven't really solved anything. But if it automates manual audits, eliminates unnecessary phone calls, gives staff better visibility into their day, or reduces the time leaders spend gathering operational information, that's beyond meaningful — it’s measurably impactful.
Every hour we give back to clinical teams is an hour they can spend caring for patients. And, based on my own experience, that contribution to purpose is what keeps nurse teams’ morale high and prevents burnout and attrition.
The right technology still requires the right leadership
Choosing the right AI platform is important, but technology alone doesn't determine whether an implementation succeeds.
In my experience, the organizations that see the greatest impact start asking the hard questions long before go-live. Those conversations shape everything that follows.
AI has enormous potential to improve how hospitals operate and support the people delivering care. Realizing that potential takes more than selecting the right platform. It takes thoughtful leadership, clear communication, and a commitment to building trust every step of the way.
That's why, before we evaluate features or compare vendors, I always come back to these five questions. In my experience, answering them well creates the foundation for successful adoption and gives the right technology the opportunity to deliver on its promise.
Learn how Houston Methodist used its block schedule to 3x one surgeon's daily cases.
Read the case study