The artificial intelligence economy has produced a significant amount of guidance. Workshops, consultants, courses, and advisors all promise to help companies understand the technology. Joel Yi, the founder of DeployAIBots, has built his company on a pointed observation about that landscape. Advice is widely available, but implementation can be harder to find, and the gap between the two is where many companies get stuck.
Joel Yi has named this gap directly. When he looked at the market, he saw many people selling ideas while fewer were focused on delivering measurable outcomes. There were people talking about artificial intelligence, running sessions, and offering recommendations, but the moment it came to deploying working systems inside a business, many efforts fell short.
That gap, in his view, is one of the central problems of the current AI moment, and it is the problem DeployAIBots was built to address.
The distinction matters because advice and implementation require different things. Explaining what artificial intelligence could do for a company is relatively easy. Actually installing a system that performs the work, integrates with how the business runs, and produces a result is far harder.
Joel Yi argues that the field has an abundance of people willing to do the first and a shortage of people able to do the second. The result is a market full of companies that understand AI in theory but have not yet put it to work in a meaningful way.
DeployAIBots positions itself on the implementation side of that divide. The Miami-based company builds agentic AI, systems designed to execute operational tasks such as scheduling, customer communication, and internal coordination rather than simply offering suggestions.
Joel Yi describes the company’s role as getting into a business and deploying systems that can handle repetitive work, with the goal of reducing costs, saving time, or increasing capacity. The emphasis on deployment is deliberate, a direct contrast to the advisory model he critiques.
Joel Yi believes the prevalence of advice over implementation helps explain some of the disappointment companies feel about artificial intelligence. A business that hires a consultant or attends a workshop may come away with a clearer understanding and a stack of recommendations, but understanding is not the same as a working system.
When the recommendations prove difficult to put into practice, the initiative can stall, and the company may conclude that AI did not live up to the hype. The problem, Joel Yi suggests, is often not the technology itself, but the absence of real implementation.
His own background gives him a builder’s impatience with talk. Joel Yi studied computer science, earned recognition for his work in artificial intelligence at Pacific Lutheran University, and built an early machine learning model in 2018 that identified rare plant species. He later became one of the first cyber officers in the United States Army cyber branch.
Across that path, the common thread is producing systems that work rather than describing systems that might. That history shapes his insistence that artificial intelligence be judged by what it deploys, not only by what it promises.
Joel Yi is careful not to dismiss the value of knowledge entirely. Understanding artificial intelligence matters, and a company cannot deploy well if it does not grasp what it is deploying.
His point is that understanding has to lead somewhere. Advice that never becomes a working system is, in practical terms, incomplete. The value is realized when the system is running and producing a result a business can measure.
This is also why Joel Yi emphasizes measurable outcomes so heavily. Implementation, unlike advice, can be tested. A deployed system either reduces costs, saves time, or increases capacity, or it does not.
Joel Yi points to his own company, which reports reclaiming more than 150 hours of work each week through its own technology, as an example of what implementation can look like when it succeeds. That is the kind of result advice alone cannot deliver.
For companies navigating the crowded AI market, Joel Yi offers a simple way to tell the difference. Ask whether a given offering ends with understanding or with a working system.
If it ends with understanding, it is advice. If it ends with a system that performs and can be measured, it is implementation. He has built DeployAIBots to live in the second category, betting that as the hype settles, businesses will increasingly value people who can help make artificial intelligence work in practice.
