McMaster-Carr Innovation & Technology Culture

On using AI and LLMs in day-to-day engineering work:

"Our developers are encouraged to find balance between using embedded AI tools to expedite the authoring of code or acceptance tests while maintaining a commitment to understanding the output and implications of any generated code."

On using AI and LLMs in day-to-day engineering work:

"With AI, it’s easy to get caught up in what’s technically possible. But the real value comes from understanding the specific problems teams are trying to solve and using that knowledge to build long-lasting, extensible systems that address those needs."

What’s your rule for fast, safe releases — and what KPI proves it works?

Make changes small, reversible and observable.

Small changes are easier to understand, test and fix. When something breaks, there’s less to reason about and a smaller blast radius. Reversible changes (i.e. using feature flags, safe rollbacks and backward‑compatible schemas) allow us to recover quickly and gracefully when problems occur. Observable changes have clear signals that tell us whether they’re working, so issues can be detected and contained early.

The KPI that proves this works is Change Failure Rate.

Small changes introduce fewer defects, observable changes surface problems quickly and reversible changes reduce the impact and duration of failures. Together, they lower the likelihood that a change results in user‑visible issues while enabling fast delivery.

Which standard or metric defines “quality” in your stack?

At the risk of double dipping, Change Failure Rate is also how I define quality. Changes exist to benefit the end user, whether that’s an external customer or an internal team. When a change disrupts their ability to use the system, it causes harm, which is the opposite of what we’re trying to achieve. To me, a high‑quality change is one that delivers value while protecting the user experience. CFR is the clearest signal of that, because it measures quality where it actually matters: in production.

Name one recent AI/automation that shipped and its impact on the team or business.

One recent AI/automation we shipped was improving search relevance on mcmaster.com using LLMs. We receive thousands of searches that our traditional search engine struggles to interpret, most commonly manufacturer part numbers or foreign‑language queries. That led to poor search results and customer abandonment.

We introduced an external LLM as an intent‑interpretation layer that maps these queries back to our product catalog before running the search. The LLM augments the system rather than replacing core search logic, which kept the design safe, explainable and easy to reason about for engineers supporting the system

We validated the change through an A/B test. Customers exposed to the LLM‑powered results were more successful at finding relevant products and placed more orders, driving a measurable lift in conversion. It also reduced failed searches, lowering friction and improving the overall customer experience.