An AI credential should ship at the speed of AI.

The credentials available today are typically 6+ month institutional literacy designations with curriculum locked at enrollment. CAIC is a practitioner credential: issued in weeks, refreshed as the AI landscape shifts, through an autoresearch pipeline, with a real consulting capstone and depth on wholesale, MGA, and vendor evaluation that broader literacy programs don't cover.

Individual enrollment is open now at getcaic.org/enroll. The program is live, and all nine modules unlock the moment you enroll. The two founding tiers that seeded the credential, the 10-slot agency Founders Club and the 25-seat Inaugural Cohort, are both closed and sold out.

Individual enrollment is $997, paid in full through Stripe at getcaic.org/enroll. That is the standard rate now that the Inaugural Cohort Early Bird has sold out and the agency Founders Club has closed. If you have a referral or scholarship code, you can apply it at checkout.

Yes. You have 14 days from purchase to request a full refund, no questions asked. After 14 days all sales are final. Consuming course material during the window does not waive your refund right.

Insurance professional building an agentic operating system for the industry. Day job is in commercial insurance distribution; CAIC is the credential layer of that broader work.

Not by a state insurance department. CAIC is a professional credential like a vendor certification, not a state license or continuing-education designation. Recognition comes from the rigor of the curriculum and the practitioner depth, not from regulator endorsement.

CAIC is issued and administered by Agentic Insurance AI (Agentic Holdings LLC, New York), founded by Jay Greene. The Risk & Insurance Education Alliance is CAIC's education and distribution partner and files its continuing-education path; it does not own or administer the credential. Verification of any credential holder is at getcaic.org/verify.

Yes. Multi-seat enrollment is available; contact hello@getcaic.org.

An autoresearch pipeline ingests vendor announcements, regulatory updates, and AI capability shifts daily. Material refreshes are pushed to enrolled students as the AI landscape in insurance shifts. This is the structural difference vs. a point-in-time designation.

No. AI is automating tasks inside the agency, not the relationship at the center of it. The most exposed work is repetitive and data-heavy: intake, quoting support, servicing follow-ups, renewal prep. Judgment on coverage, handling a tough claim, and winning and keeping clients are not going away. The producers who pull ahead use AI to clear busywork and spend more time advising. Our AI for Producers playbook breaks down what changes and what does not.

The most common uses are producer prospecting and outreach, quote and application intake, customer service and servicing workflows, claims triage, and renewal preparation. The agencies seeing results start with one workflow, prove it, then expand, rather than buying a broad platform and hoping. The pattern that works is narrow and deep: one process automated end to end. CAIC's P&C Workflow Architecture module maps where AI replaces a single step versus an entire workflow across a typical agency.

The clearest returns come from high-volume, repetitive work: claims triage and intake, servicing and CSR workload, producer prospecting, and renewal prep. Buy the technology last, not first. The process redesign and data cleanup usually take longer than the automation itself, and that is where most projects stall. Start with one workflow that has clean inputs and a measurable outcome. Our claims and producer playbooks cover the specific use cases.

It can be, with governance. The main risk is errors and omissions (E&O): an AI-assisted recommendation or document that turns out wrong with no human review trail. Safe adoption means a human in the loop on anything client-facing, a record of what the AI did, and disclosure where it is required. The NAIC Model Bulletin on the use of AI by insurers and individual state Department of Insurance (DOI) guidance set the baseline. Our AI Governance playbook maps it.

Not necessarily. Most early agency workflows (drafting, summarizing carrier bulletins, internal checklists) need no nonpublic personal information at all. Workflows that genuinely need client data should run only on enterprise-tier tools with written no-training and data-retention terms, reviewed by a licensed human and logged, the same diligence the agency already applies to its AMS or rater. The risk is a deployment and contract question, not a property of AI. Our AI and Client Data playbook takes the objection apart layer by layer.

Agentic AI is software that completes multi-step tasks on its own, not just answers a single question. Instead of drafting one email when asked, an AI agent can work a renewal list end to end: pull the account, check the policy, draft the outreach, and queue the follow-up. An agentic agency is built around these workflows from intake to renewal, using response speed as a competitive advantage. CAIC's P&C Workflow Architecture and The Agentic Agency modules cover how far this goes and where the human still owns the decision.

Pick one workflow, not a platform. Choose a repetitive process with clean inputs and a clear outcome (renewal prep or first-notice claims intake are good starting points), run a small pilot, measure it, then expand. Audit your data first: most stalled projects fail because the data was not as clean or accessible as assumed, not because the AI was weak. Then decide build, buy, or borrow for that workflow. Our AI Vendor Selection playbook walks through the decision.

Match the tool to the workflow, then pressure-test the vendor. Sort options into build (make it in-house), buy (license a product), or borrow (use a general tool like ChatGPT or Claude). Ask how the vendor handles your data, whether it integrates with your agency management system (AMS), what happens to your data if you leave, and always run a 30-day pilot before signing. Our AI Vendor Selection playbook gives five vendor archetypes and twelve questions to ask.

Yes. The NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers sets expectations for governance, testing, and accountability, and a growing number of state Departments of Insurance (DOI) have adopted it or issued their own guidance. The requirements touch unfair discrimination, transparency, and oversight of third-party AI vendors. Compliance is a governance question, not a reason to avoid AI. Our AI Governance playbook maps the current landscape.

Four, in order: a working vocabulary of what today's AI can and cannot do; prompt patterns that actually move work; the judgment to see where AI replaces a step versus a whole workflow; and a method for evaluating vendors. Deep technical or coding skills are not required. For most professionals the gap is practical fluency, not engineering. That practitioner-level fluency is exactly what the Certified AI Insurance Credential (CAIC) is built to certify.

AI is already used across claims for intake, triage, document processing, and fraud signals, but it augments adjusters rather than replacing them on anything involving judgment or empathy. The realistic model is a maturity curve: start with the highest-volume, lowest-complexity claims and keep a human on decisions that carry coverage or litigation risk. Our AI in Claims Operations playbook covers the five highest-ROI use cases and a five-stage maturity model.

Yes, and most AI training ignores it. Personal and small commercial lines get the attention because the workflows are simpler and higher-volume. Wholesale, managing general agent (MGA), and carrier-side work involve different data, distribution, and risk. An MGA underwriting specialty risk has little in common with a retail agent quoting personal auto. CAIC is built with wholesale, MGA, and carrier-side depth specifically because that layer is underserved by broad AI courses.

AI is speeding up the data-gathering and risk-signal side of underwriting: pulling and structuring submission data, flagging exposures, and surfacing comparable risks faster than manual review. It does not remove the underwriter's judgment on appetite, pricing, and terms; it moves that judgment earlier and gives it better inputs. The near-term shift is augmentation, with a human accountable for the decision. CAIC covers where the seams sit between what AI does and what the underwriter owns.

Yes. The Certified AI Insurance Credential (CAIC) is a practitioner-grade certification for insurance professionals adopting AI: agency owners, producers, underwriters, wholesalers, and aspiring consultants. Unlike broad AI literacy courses, it is built specifically for insurance workflows, refreshed as the AI landscape shifts, and finishes with a real consulting capstone you can use in an engagement (the capstone project releases in November). Individual enrollment is open at getcaic.org/enroll, and the program is live: all nine modules are available the moment you enroll.

Not as a single off-the-shelf product yet. What exists is a wave of AI exclusions (ISO's CG 40 47 and carrier-proprietary equivalents) being added to standard GL, cyber, E&O, and D&O policies, plus a small specialty market writing narrow AI-specific coverage back in for buyers who can document governance. Not having an explicit exclusion does not mean you're covered; it may mean the risk was never disclosed or priced, which is the "silent cover" problem. Our Buying AI Insurance playbook covers the exclusion landscape and an 8-step buyer's checklist.