How RT Specialty is applying AI to wholesale brokerage operations without losing relationships
Transformation doesn't start with shiny demos. It starts in the engine room. For RT Specialty, chief operating officer Marissa Moscowitz is focused on brokerage and binding authority underwriting - the workflows where small bottlenecks stack into real cost and cycle time.
Her north star is simple: increase revenue or create meaningful efficiency. Everything else is noise.
Start where friction is highest: the policy lifecycle
Following up on policies sounds simple until you're working a full tower. With 20-30 layers, every step depends on the previous one getting issued and delivered. If the primary isn't locked, the excess can't move. That sequencing creates lag and manual chasing.
RT Specialty is building agentic AI to manage these follow-ups and dependencies. The aim: reduce manual effort, preserve accuracy, and keep the sequence tight - without cutting out the human relationships that win and keep business. As Moscowitz puts it, the point is to give teams more time for clients and markets, not less.
Build vs. buy: protect the "special sauce," outsource the commodity
The team constantly weighs what to build and what to source. If deep specialty expertise is the edge, they keep it in-house. If a tool is becoming commodity tech, they'll buy to move faster and focus internal talent where it matters.
This framing keeps priorities crisp: own the parts where RT's insurance knowledge creates differentiation; partner where vendors can accelerate delivery without sacrificing quality.
Adoption by design, not by mandate
Rollouts don't work if users feel like test subjects. RT Specialty brings brokerage and binding authority teams into development early, shows features as they're built, and adjusts based on feedback. No "big-bang" launches, just steady, visible progress.
Training mirrors that approach. It's less about clicks and more about context. How does the tool change what you do next? What's the downstream impact? That's the level that gets real adoption.
Leadership sets the direction, ops makes it real
Strategy comes from the top. Moscowitz's team owns execution, but the push for transformation flows from Ryan Specialty's leadership. Clear direction, constant engagement, and a bias for speed - balanced with building the right solution.
That alignment keeps momentum high and expectations grounded.
What operations leaders can apply today
- Map dependencies across the policy lifecycle. Identify where sequencing creates wait states and rework. That's your first automation target.
- Automate follow-ups with clear rules of engagement. Define who gets notified, on what trigger, with what escalation path, and what "done" means.
- Use a simple build-buy matrix. High differentiation + high insurance nuance = build. Low differentiation + stable tech = buy.
- Design for adoption from day one. Involve end users early, ship in slices, and measure real workflow impact - not logins.
- Anchor on operating metrics. Track cycle time to bind, number of touches per placement, sequencing errors, and producer-facing time recovered.
- Add guardrails as you scale AI. Include audit trails, exception handling, and bias/quality checks. For reference frameworks, see the NIST AI Risk Management Framework.
Why this works
The approach is practical: focus on the parts of the workflow that create the most drag, automate the follow-ups that drain time, and preserve the human touch where it wins deals. Add a clear build-buy stance and steady user engagement, and you get scale without burning trust.
Next steps for your team
- Pick one lifecycle stage (e.g., post-bind issuance) and document every dependency. Automate the notifications and handoffs first.
- Stand up a cross-functional review (ops, IT, brokers, compliance) to vet build-buy choices against differentiation and time-to-value.
- Pilot with a small team, measure two metrics (cycle time and touches), and expand only when both improve.
Bottom line: RT Specialty's playbook is a useful template for operations leaders - remove friction where sequencing slows you down, automate the chase work with agentic AI, and invest the saved time back into relationships.
If your team is skilling up on AI for operations and automation, explore hands-on programs here: AI courses by job role and automation-focused resources.
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