Letter from Chris, CEO of RevWisely

Every now and then, as CEO, I like to share what's actually happening behind the scenes at RevWisely—not the theory, just the work. Over the last six months, we've gone all-in on becoming AI-native. Not as an idea, but as an operating model that defines how the business actually runs day to day.

We're still a small team, and that hasn't changed. What has changed is the level of leverage we now operate with—and just as importantly, the speed of change happening around us. That external shift has forced us to rethink not just what we do, but how we do it.

Things Are Different Now

If you rewind to January, most companies were still operating in what I'd call an AI-assisted mode. Teams were experimenting with copilots, improving prompts, and layering AI into isolated use cases. It was useful, but largely incremental.

In just a few months, that landscape has shifted meaningfully. Model performance improved across reasoning and tool use. APIs became the primary interface, replacing traditional UI-driven workflows. Agent frameworks matured enough to move from experiments into production systems. At the same time, costs dropped while performance improved, and latency reached a point where real-time workflows became viable.

The net effect is that what felt advanced at the start of the year now feels like table stakes. And what felt unrealistic—end-to-end automation across real workflows—is now achievable. The pace hasn't been linear; it's been step-function. That's the part many people miss.

How We Changed Our Business

At the start of the year, we were still operating like most companies. People executed workflows, and tools supported them. That model is familiar, but it's no longer how we run.

Today, systems execute workflows and people supervise them. That shift sounds simple, but it required a complete rethink of how work gets done. We now have 37 agents running in production, organized into five teams—strategy, content, creative, QA, GTM, and analytics—and an AI GTM engineer.

That role didn't exist a year ago, and now it's central to how we operate. The constraint is no longer how much effort we can apply. It's how well the system is designed.

From Tools to Systems

One of the more practical outcomes of this shift is that we've unplugged 10 SaaS tools. This wasn't a cost-cutting exercise. It was a byproduct of redesigning workflows. Many of those tools were solving narrow problems inside processes that no longer exist in the same form.

When you change the workflow, the need for the tool often disappears. What replaces it is a more integrated system with fewer handoffs and fewer points of failure. The result is not just lower cost, but faster and more reliable execution.

What Changed Under the Hood

If you look beneath the surface, three changes have mattered most.

The first is a shift from focusing on capability to focusing on orchestration. Early on, the instinct is to build a single, highly capable agent that can handle everything. In practice, that becomes brittle and difficult to trust. What works better is a system of smaller, specialized agents, each responsible for a specific task, coordinated through an orchestrated flow of work. That structure is easier to maintain, easier to debug, and far more reliable in production.

The second shift is from writing code to building visibility. We now spend less time trying to perfect prompts and more time instrumenting the system. Every LLM call is logged and traced—what model was used, how many tokens were consumed, how long it took, what it cost, and what it produced. Without that visibility, it's difficult to trust the system. With it, you can manage performance, identify issues, and continuously improve outcomes.

The third shift is from static prompts to systems that learn. Early improvements come from refining prompts and adding context, but over time those approaches degrade. What has proven more effective is building a system that can store, update, and reference what "good" looks like. That creates a feedback loop where performance improves over time rather than drifting.

Security Is Becoming Part of the System

One area that has evolved quickly—and is still underappreciated—is security. Traditional security models were designed for people logging into systems and taking discrete actions. In an AI-native environment, that model breaks down. Agents are now reading data, making decisions, and taking actions across systems without a human in the loop at every step.

That requires a different approach. Security is no longer just about access control; it's about execution control. What is this agent allowed to do? Under what conditions? With what level of confidence? And what happens when it gets it wrong?

We've had to build guardrails directly into the system. Every workflow has defined permissions, thresholds, and escalation paths. Sensitive actions require higher confidence or human approval. Outputs are logged, traceable, and auditable by design. In many ways, observability has become a core part of security.

The other shift is that risk is now dynamic. It's not just about preventing access, but about managing behavior over time. Agents can drift. Data can change. Context can degrade. That means security isn't a one-time setup—it's an ongoing discipline tied to how the system learns and evolves.

The companies that get this right won't bolt security on at the end. They'll design it into the system from the start.

What We Got Wrong…and Fixed

There are a few things we had to learn the hard way.

First, demos are misleading. Something that works once in a controlled environment says very little about how it will perform in production. The gap between a successful demo and a reliable system is where most of the work actually happens.

Second, adding more AI doesn't necessarily improve outcomes. In fact, overcomplicating workflows tends to make them more fragile. Simplicity and structure matter more than raw capability.

Third, pilots often stall. Without a clear definition of what success looks like, teams get stuck in a loop of experimentation without impact. This is something we now see across many organizations.

Common Gaps Most Companies Face

Across the market, a consistent pattern is emerging. There is a strong interest in AI and no shortage of experimentation, but very little measurable impact at the system level. Many organizations are what I would describe as "pilot-rich and transformation-poor."

The issue isn't a lack of effort or intent. It's that most teams are layering AI into existing workflows instead of redesigning workflows from the ground up. That distinction is where the real opportunity sits.

Where We're Going

The next phase is about systems. We expect to see a continued shift toward API-first, headless operating models where workflows run without human intervention at each step. Roles like the AI GTM engineer will become more common as organizations need people who can design and manage these systems end-to-end.

At the same time, evaluation and monitoring will become essential. As systems take on more responsibility, the ability to measure performance and intervene when needed becomes critical. The companies that succeed won't be the ones with the most advanced prompts, but the ones with the most effective systems.

If You Want to Go Deeper

We've made a number of these ideas visible in how we operate:

It's still very early in the AI transformation journey, but one thing is clear. This isn't about adding AI to your business. It's about rebuilding how your business runs.

I always enjoy comparing notes with others. Schedule a Conversation

Chris
CEO