Healthcare organizations are facing an impossible situation: cut costs and invest in member care.
The financial pressure on healthcare organizations over the last three or four years has been relentless: medical losses climbing post-COVID, inflation compounding the problem, and interoperability mandates requiring new data-sharing integrations that never existed before. And with members now having a plethora of plan options, retention has become a direct function of value delivered.
What if you could do both?
Where AI Is Creating Real Value for Members Today
Government mandates in healthcare require that AI be used to approve things for members, not deny them. For prior authorizations involving oncology, dialysis, or post-surgical care, a human being needs to be part of that conversation. Due to this, the industry has to figure out how AI can actually make an impact for members while boosting efficiency.
The answer lies in redefining the operational backbone (the engine that powers member care). The opportunity there is much larger than most people outside the industry realize. The entire software engineering lifecycle—testing, data pipelines, migrations, production support—sits underneath every member-facing function. On the business side, teams managing open enrollment are onboarding new members, managing retention, and coordinating across departments simultaneously. Every hour spent on manual processes in that engine is an hour not spent on member care. Every dollar tied up in redundant infrastructure is a dollar not going back to the business. What’s happening right now is a surge of use cases where agentic AI can free up both.
When a payer can meet an open enrollment go-live deadline, even after absorbing a month of schedule loss, members can enroll in coverage on time. When a healthcare organization migrates thousands of legacy reports inside an eight-to-nine-month window instead of seventeen, the savings on licensing and infrastructure go back into the business. Those are real outcomes we’ve helped organizations achieve, and the members are the ones who benefit.
Cross-industry learning also creates real value for healthcare organizations. Automation patterns that originated in banking, another highly regulated industry, have produced sixty to seventy percent automation on repetitive tasks inside healthcare call centers, results neither side anticipated going in. The organizations that are benefiting the most from AI right now, tend to be the ones open to ideas that did not originate in their own industry.
From RPA to Agentic: Why This Moment Is Different
Automation in healthcare has evolved through three distinct phases, and the progression matters for any technology leader making investment decisions.
Robotic process automation (RPA) was the first phase. It worked, but it was narrow. A specific task within a specific system, yielding maybe ten to fifteen percent improvement. Intelligent process automation (IPA) added decision-making capabilities and pushed results further but still operated within the boundaries of individual workflows.
Agentic AI is a fundamentally different model, and it is where things get genuinely exciting. An agentic workflow can operate across multiple systems and processes simultaneously—your data pipeline, your testing environment, your production support—in ways that RPA and IPA never could. The same investment that used to yield ten to fifteen percent now delivers thirty to forty percent automation depending on task complexity. For healthcare organizations, that is the difference between absorbing cost pressure and freeing up resources to invest in member experience.
Healthcare Organizations Do Not Need to Build This Alone
Many healthcare organizations have started building AI-powered engineering capabilities on their own and have made progress. However, it typically stalls when other business priorities surface, and the initiative loses momentum; AI ends up fragmented across departments.
This makes sense. Healthcare companies are not software companies. AI engineering is not their core competency, and the sustained investment it requires competes with every other priority on the table.
Most healthcare CIOs are already sensing this. When they see a mature agentic platform that runs inside their own environment, LLM, or cloud, with PHI and PII never leaving their infrastructure, the question naturally shifts from whether to build internally to whether that is the best use of their organization’s time. The healthcare problem is what they need to solve, and the AI engineering capability can come ready-built.
But choosing the right engineering partner matters as much as choosing the right technology. The engagements that produce the strongest outcomes are the ones where the healthcare organization and their engineering partner share trust, transparency, and a genuine commitment to the client’s best interest. When something unexpected happens, what matters is both sides sharing the same mindset: we’ve got this. That means the engineering partner working at the right pace, at the right cost, and constantly looking for ways to improve a process or reduce cost before the client has to ask.
The Backbone of Care
Every operational dollar a healthcare organization frees up is a dollar that can go back to member care: better experiences, better access, and better support from the care coordinators and claims coordinators who work with members every day. Improvements in how healthcare organizations operate eventually reach the members. Faster processes, freed-up capital, and care teams with more room to do their jobs well; it all flows in the same direction. That is what all of this work comes down to, staying invested in the journey the client is on and making sure the people they serve feel the benefits.
The healthcare industry is the backbone of how millions of people access care. It has to stay healthy so they can stay healthy. If you’re a healthcare CIO or CTO, start here: audit your ops workflows. You’ll likely find thirty to forty percent of manual work that agentic AI can handle in the next twelve months. Demand that any platform runs inside your own governance and compliance framework. And stay focused on what matters most: the members who depend on your organization to deliver.
About Author
Santosh Dash leads Ascendion’s Healthcare Business Unit, where he works with some of the largest payers and providers in the country on operational cost reduction, AI-driven engineering, and member experience. He has spent over 25 years in U.S. healthcare technology. His healthcare experience covers payer, provider, and a variety of enterprise programs, where he managed a $125M+ P&L. Additional expertise spans EHR/EMR and RCM, cloud integration, automation, and AI.
