By Matthew Jansen, CEO, Acorn
Lately, almost every conversation I have turns to AI at some point. People want to know where it's headed, how we're using it, and what's about to change.
Those are worthwhile questions. But I've started to notice that we're often asking the wrong one.
Instead of "How do we use AI everywhere?" I think the better question is: "Where does AI actually help, and how do we use it responsibly?"
Those two questions lead to very different conversations, and very different products.
The second question is the one our team has tried to answer from day one.
We Chose Restraint Over Reach
When we started integrating AI into our platform, I made a conscious decision not to take a broad-brush approach. We weren't interested in bolting AI onto every feature just because that's what the market expected. Provider operations is too important, and healthcare depends too much on trust, for that kind of thinking.
So instead, we looked at every workflow through our customers' eyes and asked a few honest questions:
- Will this actually make our customers' work better?
- Could it lighten the administrative load for the providers and staff using our platform?
- Can it surface the right information faster, or help someone finish a task with less friction?
- Does it improve the experience without compromising compliance or the security of sensitive information?
Where the answer was yes, we moved forward. Where it wasn't, we left it alone.
That's what responsible AI actually looks like in practice. It's not about deploying more AI. It's about being disciplined enough to know where AI belongs, and where it doesn't.
Let's Talk About the Real Cost of AI
One thing I don't think gets enough attention in the AI conversation is tokenomics, the economics behind the technology.
If you're not familiar with the term, every interaction with a large language model consumes "tokens," which drive usage and cost. But when I think about tokenomics, I'm thinking about something bigger than API pricing.
I'm thinking about the true cost of deploying AI responsibly: building governance, ensuring security and compliance, maintaining the systems, and supporting adoption over time. Someone still has to monitor and validate the technology long after go-live.
Those costs don't disappear after implementation. In many ways, that's when the real work begins.
Everyone is excited about what AI can do, and honestly, they should be. It's remarkable technology. But before we ask where else we can deploy it, I believe we owe it to ourselves, and to our customers, to understand the full business case behind it.
Technology should solve a problem. It shouldn't become the strategy.
That's part of why I wanted Acorn designed differently. Our customers decide where AI adds value in their organization. They control how and when they use it. They can adopt it intentionally, at their own pace, instead of feeling pressured to turn everything on at once.
I believe that's a better path to innovation, one that aligns with each organization's operational priorities, governance requirements, and budget, not ours.
Transparency Builds Trust
Healthcare has always run on trust.
Providers trust the systems they use every day. Organizations trust the data behind their decisions. Patients trust that their information is being handled with care.
AI doesn't change that. If anything, it raises the bar.
The word I keep coming back to is transparency. I've always thought of it like looking through a pane of glass. You should be able to see clearly through to the other side.
You should understand what the technology is doing, what information it's using, how it reached a recommendation, and who's accountable for the outcome.
If you can't explain those things, it's hard to ask anyone to trust the result.
That philosophy is built into how Acorn works.
Picture a provider enrollment team onboarding dozens, maybe hundreds, of providers. AI can quickly flag missing information, catch inconsistencies, recommend next steps, and take on the routine work that would otherwise eat up hours.
The important part to remember is that AI isn’t making the decision.
The user sees exactly what the AI identified, reviews the recommendation, validates the information, and decides whether to accept it. Every action is visible. Every change can be audited. Every decision has a person behind it.
That's not just better technology. That's responsible technology.
Transparency isn't only about understanding what AI is doing. It's about giving people the confidence that they're still in control. Our customers shouldn't have to wonder how AI reached an answer, or whether a decision was made without their knowledge. They should be able to see exactly what happened, every time.
For me, transparency and trust aren't two separate ideas. One creates the other.
AI Should Make People Better at Their Jobs, Not Replace Them
There's a lot of conversation right now about AI replacing people. That's never been what excites me.
What excites me is helping people do their jobs better.
Provider operations teams spend countless hours reviewing documents, validating information, monitoring provider data, and managing workflows that are essential but incredibly time-consuming. Credentialing along can take weeks, chasing down licenses, varifying malpractice history, confirming board certifications, and tracking expirables so nothing lapses.
That's exactly where AI can make a real difference by surfacing information faster, reducing manual effort, spotting patterns, and freeing people up to focus on higher-value work instead of repetitive tasks.
But there's an important line here. Helping people work more efficiently is not the same as replacing their judgment.
Healthcare is still about people. It's about experience. It's about accountability.
That's why I've pushed our team to build Acorn so AI supports decision-making instead of replacing it. Recommendations can be reviewed. Changes can be validated. Users stay in control. There's always a human in the loop.
I think this is where our industry sometimes loses the plot. The goal was never to build smarter machines. The goal is to help people make better decisions.
Responsible AI Is a Choice, Not a Feature
Every technology company is going to talk about responsible AI as this space matures.
That's a good thing.
But responsible AI isn't a feature you ship. It isn't a line in a marketing deck. And it isn't something you bolt on after the technology is already built.
It's a choice you make from the very beginning. Choosing to deploy AI where it creates real value, not everywhere it technically can. Understanding the economics before you scale. Designing transparency into every workflow, not just the customer-facing ones. Giving customers visibility instead of asking them to trust a black box. Making sure people stay accountable for the decisions that matter most.
That's the approach we've taken at Acorn. This isn’t because it’s easier. It isn’t. But because we believe healthcare deserves better.
Where We Go From Here
I believe AI is going to transform provider operations over the next several years. It will help organizations become more productive. It will reduce the administrative burden. And it will help people find information faster and make better decisions.
That's exciting.
But I don't believe the future is about replacing people with AI. I believe the future is about giving people better tools, better information, and greater confidence to do what they already do best.
Healthcare has always been built on trust. If AI can strengthen that trust through transparency, accountability, and thoughtful design, then we've done our job.
That's the future our team is building towards here at Acorn.
