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Sometimes we turn down projects at ARG. Not because we don't need the work, but because taking on the wrong project hurts everyone. The client spends money on something that won't deliver. We spend time on something that won't succeed. And failed projects make organizations skeptical of AI automation for years afterward. Over time, we've developed a clear sense of which projects are likely to succeed and which aren't. Before we take on an engagement, we look for specific sign

AI automation projects fail for many reasons, but one of the most common is that nobody defined what success looks like. Teams build impressive demos, deploy agents into production, and then struggle to answer a basic question: was this worth it? Measuring ROI on AI projects is harder than measuring ROI on traditional software. The benefits are often diffuse. Time savings spread across dozens of employees. Error reductions prevent costs that would have happened but didn't. Qu

The promise of AI automation is seductive: remove humans from repetitive workflows, reduce costs, increase speed. But the most reliable deployments we've built don't remove humans entirely. They reposition them. The goal is appropriate automation, where AI handles what it's good at and humans handle what they're good at. Organizations that chase "lights-out" automation often end up with brittle systems, costly errors, and teams that don't trust the technology. Organizations t

Deploying AI agents into real-world operations is not about prompting models — it’s about engineering. When an agent processes sensitive documents, interacts with customers, touches compliance workflows, or routes operational work, it behaves like any other production system and must be treated as one. Safety, reliability, and observability aren’t optional extras; they are foundational requirements. Most of the failures we see in mid-market deployments can be traced back to o

Most mid-market companies suspect they’re behind on AI. What they don’t realize is where  they’re behind — and why it matters. When people hear “AI,” they think of futuristic technologies, massive data platforms, chatbots, or expensive enterprise initiatives designed for Fortune 500 budgets. They imagine complicated machine-learning projects, giant models, heavy infrastructure, and long implementation timelines. It feels distant from the daily reality of running operations in

Most organizations don’t realize how much time they lose to operational drag. Not because they aren’t smart, resourceful, or structured, but because the cost of manual work hides itself inside familiar routines. These workflows feel small in isolation: reviewing a form, routing an email, copying information from one system to another, assembling a packet, responding to a request. None of them look like a crisis. But repeated hundreds or thousands of times per week across mult

AI is transforming how modern operations teams work, but one of the biggest mistakes companies make early on is assuming they should “automate everything.” Automation doesn’t succeed because an organization goes broad; it succeeds because it starts precise. The real challenge isn’t building the AI—it’s knowing where to use it first . Across mid-market organizations, operational bottlenecks hide inside everyday workflows that rarely get strategic attention. Intake processes, d