NATCON 2026: The Decisions That Will Define the Next 12 Months

I went into NATCON this year expecting a continuation of last year’s mood. Anxiety about the new funding reality, frustration with workforce shortages, defensive postures around regulatory change. Instead, the conversations I had in hallways and between sessions had a different shape. Leaders were less worried about whether they could navigate this new environment and more focused on how fast they could move within it.

From where I sat, as both a clinical psychologist and an executive, three themes emerged from this year’s conference that every behavioral health leader should be thinking about right now.

The first is that the industry is beginning to adjust to the new funding reality, with more optimism than there was last year about the path forward. The second is that AI adoption is creating a structural gap between organizations, and the gap is compounding faster than most leaders realize. The third is that trust is up and implementation barriers are down, which means the cost of waiting has flipped. Each is a leadership decision waiting to be made.

From “Will We Survive?” to “How Do We Move?”

A year ago, the dominant question in behavioral health leadership was survival. Medicaid redeterminations, payer mix volatility, workforce attrition, and federal policy uncertainty had leaders bracing for impact. This year, that posture is shifting. Operators have had time to model the new economics, renegotiate payer terms where they could, and identify the operational levers actually within their control. The optimism I heard at NATCON wasn’t naive. It was earned through a hard year of work.

What surprised me most was how practical the AI conversations had become. The CCBHC leaders I spoke with weren’t asking whether AI fit their operating model. They were asking which use case to start with. The implementation conversation has moved past deliberation, and that maturation is now visible across the industry.

At the same time, a tension is emerging. Many of the most forward-thinking organizations are already asking what’s next, including agentic workflows, predictive risk models, and integrated outcomes platforms, while a substantial portion of the industry hasn’t yet adopted what’s current. That gap is the story of this year’s NATCON.

The AI Gap Is No Longer Theoretical, and It’s Compounding

Last year, AI at NATCON was mostly aspirational. Vendors were pitching, executives were curious, and the conversation rarely got past the question of whether the technology was ready. This year, operators are talking specifically about workflows in production, including ambient documentation, the digital front door, async assessments, continuous monitoring, and risk stratification. Multiple leaders described AI not as a project but as a layer running underneath their operations.

Two camps were visible at NATCON. The first has one to three AI use cases live and is actively planning the next three. The second is still deliberating their first. The gap between them isn’t linear, it’s compounding. Each use case in production makes the next one easier through shared data infrastructure, clinician trust, change-management muscle, and vendor relationships. Leaders in the first camp are pulling further ahead every quarter, often without their competitors fully realizing it.

Loren Larsen, Videra’s CEO, framed the dynamic this way after one of the sessions: “Just like people who are still writing code by hand are getting outstripped 10:1 by people who are using AI to do it, the same thing will happen in behavioral health. By next year, the haves will have added three more AI use cases, and the have-nots will be further and further behind.”

The leadership implication is direct. Skipping the current generation of AI to wait for the next one is a losing bet. The organizations earning the right to use what’s next are the ones operationalizing what’s current right now. And the organizations on the sidelines aren’t just standing still. They’re losing ground.

Trust Is Up, Barriers Are Down, and the Excuses Are Running Out

A year ago, leadership questions about AI were defensive. Is it safe? Will my clinicians revolt? Will payers accept it? Will we get sued? This year, the questions are operational. How do we sequence rollout? What does change management look like? How do we measure ROI? How does this integrate with our EHR?

Several things changed at once to drive this shift. Clinicians who have actually used these tools are reporting time savings without quality loss, and word of mouth among clinicians is doing more for adoption than any vendor pitch ever could. Vendors have matured, with security postures more transparent, products more clinically grounded, and integrations more robust than they were even 12 months ago. Outcomes data from early adopters is now visible, and it’s persuasive. Leaders are seeing real numbers on documentation time saved, no-show reduction, and capacity gains. Regulatory clarity is improving, not deteriorating, which is a meaningful tailwind for adoption.

A year ago, “wait and see” was the conservative position. Today, it’s the risk position. Every quarter on the sidelines is a quarter of compounding disadvantage.

This is the part where my clinical seat matters most. I have spent years watching clinicians fight tools that were imposed on them rather than designed with them. The pattern is so consistent that I now treat it as a clinical risk factor in any deployment plan. If the people closest to the patient experience the technology as surveillance or as another administrative weight, adoption fails and patient care suffers downstream.

What we’re seeing across our 300+ partner facilities and more than a million patient interactions is the inverse pattern. When clinicians experience AI as something that gives them their evening back rather than something monitoring them, the adoption fight evaporates. We see it in the numbers: 94 percent completion rates on async assessments because the experience works for patients, and clinicians reclaiming the 40 to 50 percent of their day they used to spend gathering and compiling information. That time goes back into treatment, which is the only outcome that matters from where I sit.

The implementation question isn’t “will clinicians accept this?” It’s “will leadership give them tools worth accepting?”

The Reasonable Case for Waiting, and Why It Still Doesn’t Hold

There is a reasonable case for caution that I want to acknowledge directly, because I hear it from clinical leaders I respect. The model landscape is moving fast. Today’s best tool may be outpaced in 18 months. Implementation consumes scarce clinical and operational bandwidth. And every behavioral health organization carries scar tissue from technology rollouts that promised time back and delivered more clicks.

These are real concerns. They are not, however, an argument for sitting still. The infrastructure built around the current generation of tools, including data plumbing, clinician trust, change-management capacity, and vendor relationships, is exactly what makes the next generation deployable. Skip the current cycle and you don’t preserve optionality. You forfeit it. The organizations who will adopt next year’s tools well are the ones who built the muscle on this year’s.

What CEOs Should Be Deciding Now

If I had to distill the takeaway from this year’s NATCON into a single message for behavioral health CEOs and executive leadership, it would be this. The next 12 months are not a strategy cycle. They’re a positioning cycle. The decisions made now will define competitive position for the next five years.

A few things worth deciding before next NATCON:

• Treat AI adoption as a board-level strategic question, not an IT project. The organizations succeeding fastest are the ones whose CEOs made this an executive priority, not a delegated workstream.
• Don’t skip “current” to wait for “next.” The infrastructure built around today’s AI, including data plumbing, clinician workflows, and vendor relationships, is what makes tomorrow’s deployment possible.
• Sequence matters more than scope. Start where clinician and staff burden is highest and ROI is most measurable, including documentation, intake, and between-visit monitoring. These are the lowest-regret, highest-impact entry points, and they build the data and trust foundation everything else depends on.
• Bring clinicians and staff into the design conversation early. The implementations that fail are the ones imposed on the people doing the work. The ones that succeed are designed with them. From a clinical leadership seat, this is the single highest-leverage decision a CEO can make on the front end of any deployment.

One CEO I spoke with at NATCON framed the leadership challenge plainly: “CEOs need to decide how to implement AI and then make it happen. It’s still new enough that our teams won’t know what or how.” That’s the leadership reality right now. The technology has matured faster than most organizations’ internal expertise, which means the decision can’t be delegated downward and waited on. CEOs and executive leadership have to own the call.

Next year’s NATCON will look very different from this one. The leaders who decide now are the ones who will be shaping that conversation rather than catching up to it.

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For a deeper look at where AI is delivering measurable value in behavioral health right now, and what separates the organizations getting it right from the ones still waiting, Videra recently published our State of AI in Behavioral Health 2026 report, drawn from in-depth interviews with executives and clinical leaders across commercial and community behavioral health systems. Worth a read if these themes resonated.