New Year. New AI. Three Resolutions Worth Keeping

Let’s be honest about resolutions.

Five is ambitious.
Seven is fantasy.
By February, even four feels aggressive.

So instead of promising a total AI overhaul this year, let’s do something more realistic: make three resolutions you might actually keep.

If AI felt harder than it needed to be last year, it probably wasn’t for lack of effort.

Most organizations did the work. They sat through demos. They invested time, budget and goodwill. And yet many AI initiatives stalled quietly, not because the technology failed, but because the fundamentals weren’t in place.

AI doesn’t usually break in obvious ways. It loses focus. Ownership blurs. Decisions slow. Clinicians lose trust. Everyone gets distracted by competing priorities and pressures. Tools exist, but impact doesn’t.

That’s why this year isn’t about doing more with AI. It’s about doing fewer things better. Because the difference between AI that scales and AI that stalls often comes down to a handful of disciplined habits.

So let’s lower the bar in a smart way: three AI governance resolutions that don’t require perfection, just discipline.

Resolution #1: “We’ll stop treating AI like a side project.”

Most healthcare organizations don’t lack AI. They lack ownership.

AI lives in pilots, pockets and PowerPoint decks. Decisions are made ad hoc. Accountability is fuzzy, and when something breaks, everyone looks at someone else.

This year’s first resolution is simple: AI gets a structure.

That means:

• Knowing what AI is already in use
• Assigning clear owners
• Agreeing on how decisions get made
• And aligning AI efforts to real organizational goals

AI doesn’t need more excitement. It needs a backbone.

Resolution #2: “We’ll choose fewer tools and choose them better.”

Healthcare has no shortage of AI options. What it lacks is restraint.

This year, resolve to:

• Say no to shiny demos that don’t fit real workflows
• Ask harder questions in discovery meetings, especially around data
• Demand evidence, not promises
• And prioritize tools that don't cause friction in workflows

Choosing less, more thoughtfully, is how organizations build trust instead of fatigue.

The fastest way to kill AI momentum is to deploy too much, too fast, without a plan.

Resolution #3: “We’ll keep paying attention after go-live.”

This is the resolution most organizations skip.

AI is not “set it and forget it.” Models drift. Workflows evolve. Regulations change. What worked six months ago may not work today.

This year, commit to:

• Monitoring performance in real-world conditions
• Listening to clinicians using the tools
• Watching for bias, safety and compliance issues
• Leaning on the vendor partners to help drive improvements
• And being willing to scale, fix or retire tools when needed

This is how AI becomes a capability, not shelfware.

Why These Resolutions?

None of this is theoretical.

These patterns show up again and again in healthcare organizations that have moved past AI pilots and into real, sustained use. Radiology is a good example. When AI started showing up in everyday clinical work, teams had to deal with the unglamorous questions early: Who owns this? Where does it fit? What happens when it’s wrong? What do we do when it stops helping?

Over time, a rhythm emerged.

Whether anyone calls it a framework or not, the same behaviors tend to show up. Teams take stock of what exists. They get clearer about goals and guardrails. They choose more carefully. They integrate more deliberately. And they keep paying attention after go-live.

Assess. Define. Select. Execute. Monitor.

You don’t need to memorize that. It’s just the pattern behind the resolutions.

And this is where the New Years personal trainer analogy actually works.

Most of us know what we should be doing in January. The problem isn’t knowledge –t’s the follow-through. That’s why people hire trainers. Not because they don’t know how to work out, but because structure and accountability make the difference between good intentions and results.

The same thing is true for AI governance.

Most organizations already know the basics. The challenge isn’t understanding what should happen. It’s staying consistent once the initial energy fades.

That’s the role this eBook is meant to play.

AI Governance in Healthcare: A 5-Step Framework for Leaders is a personal trainer for your AI resolutions. Not someone yelling, just something steady to check in with. A place to pause, sanity-check decisions, pressure-test vendors and make sure AI stays aligned with real clinical and operational needs.

If last year was about trying things, this year is about noticing what actually holds up.

We put the thinking, questions and practical tools into one place so teams have something to come back to when things drift, stall, or get complicated. Because a little structure goes a long way toward keeping good intentions alive past February.