Interview with Clint Riley, Chief Operating Officer

This interview is with Clint Riley, Chief Operating Officer.

For readers meeting you for the first time, how do you describe your expertise in operational efficiency, strategic planning, and scaling organizations?

I’ve spent most of my career deeply involved in the inner workings of organizations—learning firsthand how operations, strategy, and growth intersect over extended periods. That background has given me a clear eye for where processes break down under pressure, how to build strategies that stick across teams, and what it takes to scale thoughtfully without losing what makes a company effective. Ultimately, it’s about applying those lessons to create lasting improvements rather than quick patches.

What key experiences or turning points led you to your current role optimizing operations and supply chains?

A key experience that shifted my focus toward operations optimization came during a period of rapid scaling at a previous organization when our client pipeline (the flow of applications through review and approval) began to clog under a surge in volume. Delays built up quickly, teams spent disproportionate time on rework and chasing updates, and client commitments slipped as small issues compounded. It became clear to me how much more energy gets poured into fighting fires than the relatively small amount of energy needed to prevent them from starting in the first place.

That insight demanded a complete shift in mindset: we had to build smarter, more capable processes early on, enforce them consistently, eliminate redundant layers (like checkers checking the checkers), and embed into our culture accountability for getting things right the first time. This meant adopting an operational excellence and continuous improvement mindset that focused on stopping problems at the source rather than constantly reacting.

With that context, describe the most consequential efficiency improvement you’ve led, highlighting the critical constraint and the measurable outcome.

The most consequential efficiency improvement came from fixing fragmented handoffs in our overall process. Business development teams captured only basic details, leaving downstream groups to constantly chase missing or inaccurate information, which created rework loops, extended cycle times by weeks, and drained resources on reactive fixes.

This revealed the root issue: quality control was treated as a standalone audit at the end rather than everyone’s responsibility from the start. We shifted to baking quality directly into the workflow — starting with upfront teams investing a bit more time and attention (with better tools and clear guidelines) to get complete, accurate information the first time. That added a small amount to their stage but eliminated major downstream bottlenecks.

Reframing the entire pipeline as one cohesive client experience made the value obvious: a modest upfront increase dramatically shortened total cycle time. Teams handled significantly more volume with smoother flow, more consistent commitments, and higher satisfaction — all without adding resources. The shift also embedded continuous improvement naturally, as everyone aligned on end-to-end flow rather than “my part is done,” pulling together, cutting redundancies, and letting operational excellence take root across the organization.

Stepping into new environments, what are your first-30-day diagnostics to separate signal from noise and find the leverage points?

In my first 30 days in a new environment, I focus on getting away from my desk and into the places where work happens — to see processes in action firsthand and talk directly with the people doing it every day.

I spend time with frontline team members, listening to what they say is working well, what’s getting in their way, and especially any thoughts they have for improvements. From there, I follow the process upward through middle managers to senior leaders, paying close attention to how information travels up and down the channels — where it flows smoothly and where it gets stuck or distorted. This gives me a grounded view of both the client experience and the employee experience, which turns out to be invaluable later when shaping operational strategy.

I always put people first, then process, then technology, because when folks feel genuinely heard and respected, they open up and become partners in change — making it much easier to improve processes together and introduce tools that actually help rather than hinder.

Turning to technology, share how AI changed your approach to capacity planning and workforce development, including the single metric that proved most predictive in your decisions.

The metric that proved most predictive for me is the employee productivity rate—output per hour worked compared to what was expected.

Consistently hitting around 80% was the sweet spot: below that, we risked falling behind and burning out top performers; above that, we saw diminishing returns and drops in quality. AI made tracking this accurate and automatic, which enabled fair decisions—like rewarding those who delivered the same or better output in less time—while keeping the team healthy and sustainable.

On supply chains, describe a decision where you balanced cost efficiency with resilience and sustainability, focusing on the trade-off you made.

One decision that highlighted the trade-off between cost efficiency and resilience in supply chains occurred when I was heading operations at a manufacturing facility and we received an offer for a deep discount on raw materials.

The materials were slightly out of spec for our standard needs, but the price was so attractive that the savings could fund a small piece of equipment to resize the parts to fit our production line. At first, it looked like a win for cost reduction. However, after running the numbers—factoring in the added full-time employee needed to operate the equipment, the limited cuts per hour they could achieve, and the downtime costs when the main line waited for enough resized material—we realized the “discount” was illusory. The extra labor and production delays actually made the total cost higher than buying the correctly sized raw materials at the regular price.

We chose to stick with the properly sized materials, even at a higher upfront expense. This preserved operational resilience by avoiding bottlenecks and downtime, ensured consistent flow without added complexity or risk, and maintained long-term efficiency rather than chasing short-term savings that disrupted the process. In hindsight, it reinforced that true cost efficiency comes from reliable, fit-for-purpose inputs that support steady throughput, not just the lowest sticker price.

To drive adoption, what have you found to be the most effective way to align cross-functional teams around a process change without slowing momentum?

The most effective way I’ve found to drive adoption of a process change across cross-functional teams without losing momentum is to communicate purposefully and early. I share honest context, the reasons behind the shift, and clear priorities tailored to each group’s role. This helps everyone understand how it connects to their daily work and quickly reduces uncertainty.

At the same time, I bring frontline voices into the conversation from the start. I ask targeted questions to surface real pain points and ideas, then frame the final adjustment as something we shaped together. This builds natural buy-in. It creates a safe environment for people to test and adapt with quick support and feedback. We reinforce progress through visible small wins, recognition, and refreshed metrics that show the change is already delivering value.

For strategic planning, when growth is uncertain, how do you set and communicate the trigger points that determine whether you hire, invest, or pause?

When growth feels uncertain, I set trigger points using a few straightforward leading indicators rather than waiting for perfect forecasts. These include pipeline velocity staying strong for 60–90 days, consistent revenue growth over a couple of quarters, or utilization rates approaching our upper sustainable limit without signs of burnout.

I also define clear downside triggers—such as declining leads, longer sales cycles, or external market warnings—so we can pause or shift resources early if needed.

This keeps decisions based on facts, cuts down on doubt, and lets everyone focus on the work instead of wondering what comes next.

For readers to act quickly, what is the first low-risk experiment you’ve personally run to scale output without 1:1 headcount growth that they could adapt in the next 90 days?

One of the first low-risk experiments I ran to scale output without adding headcount was a simple laminated checklist for a customer experience team in a call center handling complex client interactions. Calls were long and varied, so key details were often missed, leading to follow-ups, delays, and extra workload. I created a one-page dry-erase checklist of essential topics, distributed it to the team, and asked them to check off items as covered — keeping conversations natural while ensuring nothing was overlooked. It took less than a week to design, print, laminate, and roll out, with virtually no cost.

This noticeably reduced follow-up touches per transaction, freeing the existing team to handle steady or growing volume without hiring, and it was effective enough to later digitize. Readers can adapt this in the next 90 days for any high-volume, variable process (customer support, project intake, approvals, reporting, etc.): identify where missed steps cause rework, make a basic one-page checklist of must-cover items, pilot with a small group for 30 days (tracking follow-ups, errors, or time saved), and tweak based on results. If it doesn’t work, revert with no harm; if it does, refine and scale. The point is to start small, fail and fix fast, test low-risk ideas until you see real improvement, then codify what works — this builds momentum safely and delivers efficiency gains in almost any role or team.