Blog
Meet Joey Kennedy
Joey Kennedy is a veteran health technology CRO and enterprise software sales leader with more than 25 years of experience. This is Joey’s fifth time leading sales at an innovative health technology company. He has held chief revenue officer (CRO) and sales leadership roles across multiple healthcare and AI technology startups. He also has an MBA from UCLA with a focus on Healthcare Marketing and a Bachelor’s degree from BYU.
By actAVA Team·5 min read
Blog
The actAVA Workforce Agent Library for Sales & Marketing has been updated with 23 new Agents.
The actAVA Sales & Marketing agent library has just more than doubled, from 22 agents to 45. The original set found, researched, and scored revenue. The 23 new agents run everything after: a Closer Agent that gives every late-stage deal a daily close plan, a deal desk that catches margin leakage before signature, contract drafting straight from CRM data, and churn-and-expansion analysis after the win. Plus a whole new marketing and PR arm: case studies that never invent a customer fact, an anti-slop brand voice auditor, media and analyst list builders, and scouts for awards and conferences. Same rules as the originals: governed on KORA, grounded in your CRM, a person owns every send.
August 5, 2026·7 min read
Blog
Healthcare Wants to Own Its Agentic Future
The build-vs-buy debate in healthcare AI is over. Deloitte found 61% of healthcare leaders are already building their own agents, and Lilly, Novo, Stanford, and Humana are proving why: an agent trained on your data is your moat; a rented one is a subscription. What operational long-tail workflows are you focused on building yourself?
August 3, 2026·4 min read
Blog
How Johns Hopkins is Owning its Agentic Future
Dr. T. Y. Alvin Liu directs the Gills AI Innovation Center at Johns Hopkins, launched by a $10 million gift from Dr. James Gills, an ophthalmologist and early NVIDIA investor. His view on where healthcare AI is heading is blunt: buy the application, and you still rent the intelligence underneath it. In this interview, he walks through the AI brain he's building to capture the center's institutional memory, why "own our AI destiny" is the answer every health system gives when you remove the resource constraint, and the gap that should worry everyone. AI-native vendors sell the top 10% of workflows, the biggest problems with the biggest markets. Nobody sells the other 90%, because they're too small, too specific, too idiosyncratic to your organization. That's the long tail actAVA is built for, and it's where ownership pays off: you come out owning a post-trained model that runs 20 to 100 times cheaper, is your IP, and is trained on your data. Owning your workflows matters. Owning the model that runs them is what makes you AI-native.
July 31, 2026·5 min read
Blog
actAVA is Heading to Ai4 2026
actAVA is heading to Ai4, America's largest AI industry conference, August 4 to 6 at The Venetian in Las Vegas. Chief Sales Officer Joey Kennedy will be there all three days, and he wants to meet the people building healthcare's agentic future in person. Ai4 runs dedicated healthcare tracks alongside 21 industry verticals and more than 1,000 speakers, making it the right room at the right time: the question in healthcare has shifted from whether to use AI to how to put it into production without adding risk. That's the exact problem actAVA was built for, and the conversation we're bringing to the floor: moving agentic AI from a promising demo to governed, auditable work across prior authorization, utilization management, and care management. Every agent is tested against χ-Bench, and compliance is automated across NIST AI RMF, HIPAA, and CMS standards, on a platform that's HIPAA-compliant and SOC 2 certified. If you're a health system leader, clinician, or head of AI, grab time with Joey in Las Vegas.
July 28, 2026·3 min read
Blog
actAVA Named an OpenAI Select Partner
actAVA has been named an OpenAI Select Partner within the OpenAI Partner Network, teaming with OpenAI to help health systems build, deploy, and scale agentic AI responsibly. The pairing is simple: OpenAI builds the frontier models, and actAVA makes them work in healthcare, wrapped in the governance, testing, and audit rigor that regulated care demands. That work spans three segments (health-tech software companies, tech-enabled service providers, and large healthcare and life science enterprises) and is already running in production, from automating actuarial research to post-discharge navigation over voice and SMS. Through a healthcare-native agent lifecycle (Plan & Build, Test & Measure, Optimize & Scale, Govern & Audit), agents are tested against χ-Bench and compliant with the NIST AI RMF, HIPAA, and CMS standards. The result: payers, providers, and health systems can move agentic AI from pilot to production across prior authorization, utilization management, and care management, without taking on more risk or unpredictable cost.
July 27, 2026·4 min read
Blog
The Model Gets the Headlines. The Harness Does the Work.
Two teams can run the same frontier model and ship two completely different agents: one trustworthy, one quietly guessing. On the public GAIA leaderboard, a single model swings about 44 points depending only on the software wrapped around it. Same weights, different harness. actAVA CTO Frank Wang argues that the number worth grading isn't the model, it's the harness: what the agent can do without a human, how many steps it gets, what it remembers, which systems it can reach, and how it's caught when it's wrong. Here's where the agent line actually sits, why the permission surface matters most in healthcare, and why we build the part you own instead of chasing the model you rent.
July 24, 2026·7 min read
Blog
Newsweek Asked If Healthcare AI Hit a Reliability Wall. The Answer Is an Architecture Choice.
Newsweek asked this week whether health care's AI ambitions have hit a reliability wall. The gap is real: 40% of healthcare organizations are spending $50-100M a year on AI, and only 18% feel ready to implement it. But the wall isn't intelligence, it's architecture. The strongest agent framework we tested resolves just 28% of complex healthcare tasks on the first attempt, and that's a systems problem no bigger model fixes. This piece makes the case that reliability is a build-versus-rent decision: keep wiring generalized models into clinical workflows and inherit their blind spots, or own the stack. That's CURA, our healthcare-native model, KORA, the factory that builds, tests, and improves the agents around it, and CHRYSO, the governance layer that keeps every agent inside your policy. Reliability comes from the architecture around the model, not the model alone.
July 23, 2026·5 min read
Blog
The State of AI Pilots in Healthcare & The 7 Ways Healthcare Agents Fail Before You Deploy Them
Three-quarters of enterprise leaders say they're adopting agentic AI. Only a small minority have it running in real production beyond chatbots, and Gartner expects over 40% of agentic projects to be canceled by 2027. Forrester calls it the chase-catch gap. The companies catching up aren't the ones with the most agents; they're the ones who understand that the infrastructure layer (MCP, A2A, frontier models) has already been commoditized, and the real value lies above it: in workflow design, domain logic, deployment speed, and measurable outcomes. Nowhere is that clearer than in healthcare, where governance isn't best practice but a HIPAA and liability requirement. Here's why the gap exists, why it's closing, and what it takes to be on the right side of it.
July 22, 2026·9 min read
Blog
Measuring AI return is the next stage of the Enterprise AI Maturity Curve
Adoption is everywhere, but returns aren't. 76% of executives are scaling autonomous AI, and 61% of CEOs are adopting agents, yet only 25% of initiatives deliver the ROI they expected. In healthcare, that gap stalls trust as much as budgets. The organizations seeing real returns aren't smarter; they're more deliberate: they define the business outcome before they build, clean their data first, put governance at the foundation, and design workflows end-to-end rather than automating individual steps. The 25%-to-75% gap isn't a technology gap. It's a strategy gap, and in healthcare it's worth closing.
July 20, 2026·6 min read
Blog
In AI, Running Many Models Beats Betting on 1
The smartest move in healthcare AI is keeping the freedom to switch the moment a better model shows up, and right now a better one shows up almost every month. Open-weight models are reaching the frontier, companies are already shifting workloads to them, and the lab that's ahead today may not be ahead in 18 months. This post makes the case for multi-model done right: route each healthcare task to the model that actually fits, run cheaper models where they're good enough, keep PHI-sensitive work private, and absorb the next breakthrough as a config change instead of a rebuild. While other enterprises cut AI cost with a hammer (switching defaults from Opus to Sonnet or turning off frontier models for everyone), per-task routing gives you a scalpel. On actAVA KORA, every model runs inside the same guardrails, audit trails, and human-in-the-loop gates, so choice never costs you control.
July 17, 2026·6 min read
Blog
Control the Tokens, Control the Future: Why Consulting Firms Should Hold the Center
The large advisory firms are right to move fast on AI. The choice worth pausing on is where the dependency lands: who sits at the center of the stack, the firm or the model provider. Most leading providers now build their own services too, which means a firm and its core supplier increasingly share territory. A control plane of your own keeps the important things on your side: the client relationship, the methodology, and the freedom to switch or blend models as pricing and regulation shift. That's one of the ideas actAVA KORA is built around. KORA owns token generation and routes tokens to whichever provider fits the task, so adoption and independence grow together. Control the tokens, control your future.
July 15, 2026·3 min read
Blog
An Update on AI Regulations for Healthcare
Two things happened in U.S. healthcare AI policy this spring that look opposed but aren't. The federal government moved to deploy AI that can diagnose and prescribe while stripping transparency and testing rules from the tools hospitals already use. At the same time, states are stepping in: Texas, Illinois, Utah, and Colorado all now require the disclosure and oversight Washington just declined to mandate, and four more laws hit on July 1 covering PA/CRNA loan caps, hospital break penalties, PBM reimbursement, and limits on AI in prior-auth denials. The takeaway for AI buyers: deregulation didn't solve the governance problem. It turned it into a 50-state compliance problem, a clinical liability problem, and a trust problem at once. The organizations that built governance into their platform, with audit trails, HITL gates, and approval lifecycles, are the ones who can move fast without creating new risk.
July 10, 2026·13 min read