Automation before everything — that is the message Hour9 is bringing to the market.
The agency, founded by Kevin Smida and based in Somerville, Massachusetts, just announced its official launch on August 22, 2026, with an approach that goes against the grain of what almost everyone else is doing out there. Instead of shoving artificial intelligence into every project that comes along, Hour9 starts at the beginning: understand the process, map out the problems, and only then decide which technology actually makes sense. It sounds obvious when you read it like that, but the truth is this kind of approach is still rare in a market that is constantly chasing the next tech hype cycle.
And the numbers from the agency’s earliest days already speak volumes. Out of 8 projects completed with 5 clients before the official launch, only 1 actually needed AI. The other 7 were solved with traditional automation or structured code — simpler, more predictable solutions that are way easier to maintain day to day. That raises a question every company should be asking before signing any contract with an AI vendor: is artificial intelligence being used in the right place — or is it just filling a gap that hasn’t been properly understood yet? 🤔
The Problem with the AI Gold Rush
Over the past few years, especially after large language models blew up in the media, it became almost mandatory for any company to say it was using artificial intelligence for something. The problem is that this pressure created a pretty unhealthy pattern: technology being chosen before the problem is even understood. It is like buying an expensive hammer before knowing whether you need to drive in a nail or a screw. The result? Expensive, overly complex projects that require constant maintenance to solve something that could have been automated with a fraction of the effort.
Kevin Smida, who holds a bioengineering degree from the University of Pennsylvania, sums up this logic well. According to him, asking where can we use AI? might seem like a solid starting point, but that question usually gets asked way too early. The real starting point is understanding the problem first and only then deciding whether AI is actually the right tool to solve it. It is a simple inversion in the order of questions, but it completely changes the outcome of a project.
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Hour9 identified exactly this gap and built its entire value proposition around it. The core idea is not to reject artificial intelligence — far from it. The idea is to respect the diagnosis before prescribing the treatment. When you start by mapping the actual process, with all its friction points, bottlenecks, and edge cases, the technology choice stops being a gamble and becomes a logical consequence of what was found. In the founder’s own words, AI needs to earn its spot. If it is not the right tool, the agency simply does not recommend it. They choose the technology to serve the workflow, not reshape the workflow to fit the technology.
And there is another point that few people talk about openly: maintaining a system with embedded AI is more expensive and more complex than maintaining a conventional automation system. Models need to be monitored, tuned, retrained, and validated constantly. When the actual business need can be solved with well-built structured automation, going with the simpler path is not a lack of ambition — it is real operational intelligence.
The Methodology Hour9 Brought to the Table
The heart of Hour9’s approach is what they call process diagnostics — a phase that comes before any conversation about tools or platforms. During this stage, the team dives deep into the client’s workflow, understanding how tasks connect, where information gets stuck, which steps depend on human decisions, and which ones could have been running on their own a long time ago. Every project starts by identifying which workflows are leaking time, generating errors, or creating friction, and then evaluates those workflows based on criteria like frequency, business impact, decision complexity, and automation feasibility.
Unlike vendors that offer pre-built solutions running inside third-party software, Hour9 designs and builds each automation specifically for the client’s business and workflow. The agency does not resell platforms, and technology recommendations are made after the process is mapped — never before. This means the client gets a recommendation based on what their workflow actually demands, not whatever the agency already had sitting on the shelf ready to sell.
When a workflow follows clear and repeatable rules with predictable inputs, Hour9 typically recommends traditional automation or structured code. AI enters the picture when the process requires interpreting ambiguous or unstructured inputs, considering context that cannot be mapped in advance, or making decisions that fixed rules cannot handle reliably. This methodology is not just a marketing differentiator. It has a direct impact on the operational efficiency of delivered projects. Simpler solutions fail less often, are easier to audit, and allow clients’ internal teams to understand and operate what was built without depending on the vendor forever.
Three Cases That Show the Methodology in Action
A few real-world examples help illustrate how this plays out in practice. A home services company came in asking for an AI agent to qualify and distribute sales leads. After mapping the decision flow, Hour9 realized the lead routing followed pretty clear-cut criteria — service area, service type, estimated value, and urgency. The recommendation was traditional automation, not AI. Simpler, cheaper, and equally effective.
A marketing agency approached Hour9 looking for automated prospecting, AI-generated proposals, and a smart sales assistant. After reviewing the operation, it became clear that the real problems were inconsistent service delivery, high client churn, and an undefined onboarding process. Hour9 turned down the project and recommended that the company standardize its operations before attempting any automation. That is the kind of honesty you rarely see in a sales pitch.
The third case involved a property management company that wanted to automate its maintenance request process. At first glance, the process seemed straightforward, but interviews with the operations team revealed there was not even a standardized procedure in place. Hour9 was initially hired to document the process as a standard operating procedure. That work revealed that about 90% of the workflow fit neatly into traditional automation and only 10% — interpreting free-text maintenance requests written by tenants — justified using AI.
What Hour9’s Numbers Reveal About the Market
The stat of 1 in 8 projects actually needing AI is not just a random data point. It is a mirror of the market — and the reflection is not exactly flattering for anyone selling AI as a universal solution. If a specialized automation and artificial intelligence agency, founded by someone who clearly has a deep understanding of the subject, concludes that only 12.5% of its initial projects genuinely required AI, that says a lot about how much hype is still inflating expectations out there.
And industry data backs up that assessment. A 2026 Goldman Sachs survey of 1,256 participants in the 10,000 Small Businesses program found that 76% of companies were already using AI, but only 14% said it was fully integrated into core operations. Interestingly, 93% of users reported a positive business impact, regardless of how deeply integrated the AI was. Meanwhile, Gartner, in April 2026, identified that poorly scoped AI initiatives misaligned with existing operations were among the leading causes of unmet return expectations. And in July 2026, the Associated Press reported a growing corporate pushback against the indiscriminate adoption of AI, as rising costs were not delivering proportional productivity gains.
As Smida himself puts it, AI can improve a strong process, but it cannot make up for a broken business model or fix a workflow that has not been clearly defined yet. The goal is not to put AI everywhere — it is to use the simplest reliable technology that solves the problem. This data also highlights a structural issue affecting many companies trying to modernize right now: the lack of a proper process diagnostics phase before any technology decision is made.
Hour9 serves service-based businesses that want to identify and fix operational inefficiencies through automation, building, monitoring, and managing solutions on an ongoing basis. The agency is betting there is a large and underserved market hungry for exactly this kind of technical honesty. Companies that have already been burned by overhyped promises, that want to understand what they actually need before buying, and that value a solution that works in the real world more than an impressive demo. If the agency’s early track record is any indication, the bet looks well-placed — and the model could end up influencing how other companies in the space build their own methodological frameworks in the years ahead. 🚀
