Lanzko Insights
Practical notes on claims innovation and AI trends—built for claims leaders.
Successful prototypes can make or break the case for implementing change. AI now has the ability to conduct real test of real information rapidly and cost effectively.
The problem
Teams often treat implementation as all or nothing.
They either spend months planning and never ship, or push a solution into production before it is operationally safe.
In regulated environments, both paths fail. The first fails from inertia. The second fails from risk, rework, and loss of trust.
Why it persists
Prototyping is misunderstood.
Many organizations believe prototypes are unfinished, risky, or not worth stakeholder time.
At the same time, many pilots are actually production deployments without governance. They are labeled as pilots to reduce scrutiny, but they still affect decisions and outcomes.
What is missing is a structured approach to prototyping that produces real learning quickly, stays inside operational constraints, and creates a clear Phase 2 build path if validated.
The enabling approach
Prototype the workflow, not the product.
A good operational prototype is designed to answer a small number of questions, such as:
Does this reduce cycle time in the real process?
Does this improve documentation quality or consistency?
Does it reduce manual prep or rework?
Can oversight review the output efficiently?
What breaks when data quality is imperfect?
Prototyping should include real sample cases, defined user roles and review points, a narrow scope and timebox, measurable success criteria, and explicit constraints on what the tool can trigger.
Practical example
In an audit operations setting, a prototype may test centralized intake and tracking, configurable audit questions tied to a framework, structured findings output that reduces reporting time, and real time visibility into progress and blockers.
Even if AI is not used as a core function, AI-assisted development can accelerate building the portal itself.
The point is validating that the redesigned workflow reduces prep and follow up work, and that reporting becomes consistent and repeatable.
Governance and risk note
Prototypes fail when they silently change decisions.
Controls for safe prototyping include clearly labeling outputs as draft, restricting automated actions, requiring explicit human approval at decision points, logging changes and user actions, and avoiding integration into core systems until value is proven.
A prototype should reduce risk, not create a new uncontrolled pathway.
The takeaway
Prototyping is not a shortcut. It is a risk control.
In regulated operations, the best approach is timeboxed prototypes, real workflow testing, measurable criteria, and clear governance boundaries.
This creates momentum without creating exposure.