
Blog
Every AI vendor is crowding into prior authorization, and CMS-0057 is turning it into a commodity feature. Meanwhile 80% of healthcare's administrative work sits untouched, because each of those workflows is too small for anyone to build a company around. That's your long tail, spanning care management, appeals and grievances, enrollment reconciliation, revenue cycle, and provider operations, and it's where your margin leaks and your staff burn out. This piece breaks down why no point solution will ever build for it, why the token economics (a $15 to $25 labor task runs for $1 to $2 in inference) reward whoever owns the workflow and the model calls behind it, and why owning your long tail beats renting yet more point tools.

Blog
The 2026 Stanford AI Index makes it official: 90% of leading AI models now come from industry, 88% of organizations have adopted AI, and real-world incidents are climbing faster than the controls meant to catch them. In healthcare, that gap shows up as governance debt, accruing quietly with every agent that touches prior auth, clinical decisions, or revenue cycle. Here's how actAVA CHRYSO (automated compliance, evidence, and policy under one roof) and actAVA RED (continuous, production-grade testing for hallucinations, bias, and drift) close that gap before your next audit, incident, or patient interaction.

Blog
AI agents are joining org charts. Not as side experiments, but as operational team members taking on real work across real workflows. Around 80% of U.S. workers may see AI affect at least 10% of their tasks, with 19% facing disruption to more than half of their core responsibilities. This is a structural change in how work is organized, not task-level automation. And yet most organizations are treating it as a technology procurement decision rather than a workforce management decision. actAVA's answer is Non-Human Resources: the same Build, Deploy, Govern, and Improve infrastructure that HR provides for people — applied to the AI agents now joining the team.

Blog
Two signals came out of the frontier this year, and for anyone running AI in healthcare, they say the same thing. Satya Nadella warned that "a frontier without an ecosystem is not stable," because models now learn from how you work, and that expertise can flow back out and get commoditized. In Responsible Scaling Policy v3.0, Anthropic dropped its signature pledge to pause when a model outpaces its own safety measures, shifting the burden of preventing catastrophic failures onto whoever deploys the model. Put together: you can't rent your sovereignty, and you can't rent your safety. The fix is to own the layer between your workflows and the frontier. A learning loop that compounds your institutional expertise so you can swap models without losing your "company veteran," and a compliance harness that contains the model in deterministic infrastructure code, not a vendor's promise. KORA runs on a single spine, where the same component that routes you to a better model also routes you away from a flagged one. That's the layer the frontier can't commoditize, and no policy change can take back.

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Gabriela Perez is a seasoned, multi-exit senior commercial leader focused on the intersection of healthcare, artificial intelligence, and venture building. When she isn't scaling companies from the inside, Gabriela is fueling the broader tech ecosystem. She serves as a Venture Partner at NextGen Venture Partners, backing next-generation healthcare startups, and supports AI-native healthcare solutions with the venture firm Neo. An alumna of Harvard Business School, Gabriela regularly lends her expertise as a mentor at the Harvard Innovation Labs, helping early-stage founders navigate the complex bottlenecks of scientific R&D, data infrastructure, and enterprise scaling. She is also an advisor to actAVA.ai.

Blog
Nursing turnover alone drains U.S. hospitals of billions each year. The problem isn't a staffing shortage — it's a people-operations failure. Here's what it costs, where the money goes, and how actAVA.ai's AI agents are built to stop the bleeding.

Blog
As healthcare operations shift toward agentic AI, actAVA’s new χ-BENCH benchmark reveals that today's frontier agents are not yet ready to run complex workflows independently. Developed alongside leading academic and clinical institutions, the benchmark found that the best-performing agent setup completed only 28% of complex administrative workflows on the first try, consistency never exceeded 8%, and performance dropped to 0% at critical provider-payer handoffs. Because these policy-heavy workflows carry high stakes—where failures can delay care, increase costs, and introduce compliance risks—healthcare leaders must prioritize systems that can be safely measured and governed rather than automating blindly. To address this trust gap, actAVA offers a purpose-built agent-lifecycle platform that provides the orchestration, safety guardrails, and auditability required to reliably scale AI across enterprise operations

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Dr. Caiming Xiong stands at the forefront of enterprise artificial intelligence worldwide. A world-class computer scientist and strategic executive, Dr. Xiong is widely celebrated for his unique ability to bridge the gap between abstract, foundational AI research and high-impact, commercial software products. Under his technical stewardship, Salesforce AI Research has evolved from an elite incubation lab into the primary engine powering the intelligence layer of the world’s leading CRM system. By converting bleeding-edge deep learning concepts into production-ready enterprise tools—spanning Large Language Models (LLMs), multimodal systems, and autonomous agentic workflows—Dr. Xiong is actively redefining how global businesses deploy AI to automate complex processes and elevate customer experiences.

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The Truist Summit is designed for companies that refuse to do things the way they’ve always been done. At actAVA, we saw a fundamental flaw in how healthcare organizations were approaching AI: companies were trying to manually mine overlapping, hyper-complex regulatory frameworks (such as HIPAA, NIST AI RMF, CMS HEI, and ONC HT1).

Blog
You built agents on a frontier model. You tested it, tuned your prompts around its quirks, trained your team to interpret its outputs. You committed to it. Then the model provider sends an email. Deprecation. End-of-life. Migration deadline in 90 days. The government requires a shutdown. Think it can't happen to you - think again.

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The numbers are out, and they are staggering. U.S. healthcare spending has surpassed $5.3 trillion, accounting for 18% of GDP. But here is the kicker: roughly one in five dollars never actually reaches a patient. Instead, it is swallowed whole by a $1 trillion administrative machinery of billing, credentialing, and the infamous prior authorization (PA) process. While AI agents are being pitched as the ultimate savior for healthcare’s back office, a massive gap remains between tech-vendor promises and real-world execution. Here is a summary of where healthcare administration stands, why current AI solutions are stalling, and how the industry is trying to fix its measurement problem.

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Robby Peters is a Co-Founder and Managing Partner at SemperVirens Venture Capital, an ecosystem-driven venture firm dedicated to backing the B2B companies shaping the future of health, wealth, and work. He is also an advisor to actAVA.ai. Driven by the core philosophy that the best venture capitalists must act like operators to truly earn their spot on a startup's cap table, Robby Peters has spent his career bridging early-stage founders and massive enterprise networks. Under his leadership, SemperVirens has grown into a premier investment force across workforce technology, digital health, and fintech, empowering startups with the precise go-to-market strategies and distribution channels needed to conquer complex, regulated industries.

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Your SaaS vendors are charging you to access your own data. Slowly, quietly, and with increasing confidence. How can you avoid double-paying for what is yours, and avoid adding more point-solution AI to your operations? This post is about why their defensive play won't hold, what the market already knows, and how healthcare organizations can restructure their AI spending to stop paying three times for the same intelligence.